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] | the babies, and Winna started to whimper.
"That old man trying to run me off the farm?"
"It's true, Daddy. They think they gone just sweep in from New York and take it all."
Nan picked up Winna and put a chicken bone in her mouth.
"That shack you so cozy in?" Juke said to Jim. "I was born in that shack."
Jim put up his hands. "We got no intentions of taking over nothing."
Sara said, "No thank you. We got no aspirations to take over your life."
"Shut up," Juke said to her. "Jim, shut your woman up. It's you I got trouble with."
Jim said quietly, "We didn't intend to get in the middle of nothing. We hadn't even heard about any Genus Jackson when we moved in."
They were like two shoes dropping, like two creek-filled boots dropping from a dead man's feet to the ground. Genus. Jackson.
Juke smiled. He nodded. He understood now. "Thas all right. Thas all right." He slid his chair back behind him and went to his room. When he came back he was holding the twenty-two. "I should a known better than to take in outsiders. At's what got me in trouble before." He cocked the revolver, but returned it to his side. "You think I got it in me to kill? Do you? What did you conclude, after living off my cows' titties for the last six months? What you fixing to report to that Judas George Wilson?"
"We don't want any quarrel, Juke." Jim was still seated at the table, his arm around the back of Sara's chair. "We don't plan to take nothing over. We just plan to leave."
"We're gonna leave?" Sara asked him, pleading, relieved.
Jim nodded. "First thing in the morning, we'll pack up and go." To Sara he said, "It's time to go, with the baby coming."
"Hell yes, it's high time," Juke said. "But you ain't waiting till no first thing in the morning. First thing tonight!"
Juke sent Nan to help them pack up the shack. Together the three of them carried the pasteboard suitcases to the Ford, the banjo, the bolts of fabric. The dogs followed them in and out of the shack, back and forth to the car. Juke stood on the porch watching them with the gun resting on the railing, drinking his third tumbler of gin, dipping tobacco. Elma put the babies to bed. She didn't hear Sara say to Nan, "We're gonna worry about you, Miss Nan. We'd take you with us if we could." There was no one to hug but Nan, so Sara and Jim both hugged her longer than they'd expected to.
When they neared Florence, Sara asked why didn't they stop in at Mr. Wilson's, wouldn't he give them a warm place to stay, and Jim said George Wilson was the last person he wanted to see, and they were probably the last he wanted to see too, given how they'd disappointed him. They had to get out from under him, Jim said. He was glad now they were leaving under cover of night. They had just turned north on the highway when Sara said, "Stop." She put her hand on Jim's elbow. Jim pulled over to the side of the dark road. She said, "Damn damn damn." She slammed the dashboard. "We forgot the gramophone." It was on the porch of the big house, where they'd set it the night Jim learned she was pregnant.
"Damn," Jim said.
They sat in silence for a moment. Quietly Sara began to cry.
"We'll be okay, Georgia peach."
"Don't call me that, Jim."
She wasn't crying over the gramophone. She was crying for herself, because she had lost a friend, and because she too was spiteful and hateful and full of lies. Sitting in the car at the side of the road, she wasn't pregnant. Her lie had worked—they were leaving the farm, they were moving back home—but there was no satisfaction in it.
"God, I hate the South," she said.
It wasn't what she meant exactly, but saying it helped her believe it. Later, when her feelings had hardened into something more distinct; after she and Jim had made love in every shantytown between Georgia and New York; after they'd left the Model T in her uncle's driveway, as though they'd just taken it out for a spin; after she told Jim, riding a freight home, and crying real tears, that she'd lost the baby; after they'd arrived in Buffalo and moved in with Jim's mother, who prayed every night at the table for God to sow a seed in Sara's womb; and after a baby didn't come, and didn't come, she'd look back at their time at the crossroads farm and mostly hate Elma, who had two babies at the same time, more than she had milk for, more even than she wanted.
Now Jim turned around in his seat and rummaged around in the back. Underneath the stack of suitcases, he lifted a sheet of damask fabric. "Look here." Hidden under it was a crate of Cotton Gin.
Sara laughed. Jim reached for two of the jars and gave one to Sara and took one for himself. "I'd say we're square, wouldn't you?"
* * *
Parthenia Wilson did not send an eye patch but she did send a preacher. He was a small, high yellow man in top hat and tuxedo tails, like some country magician, named Teacup Clifton. He wore the hat to make himself look taller. Standing on the stump of a pine tree the men had felled, he looked, Sterling thought, like a stork on a post.
The men had sat on the tree stumps scattered around him, stumps that looked created by God for that purpose, for their wilderness church. "We worship the Lord where we find Him," said Reverend Clifton, "and we find Him everywhere. Hear His voice here in the trees! See His face there in the creek! Brothers, all we must do is open our eyes and ears! _For the Lord hears the cries of the needy, and despises not his prisoners!_ " Some of the men hollered and cried along with him, saying, "Amen," saying, "I hear you, Lord!" Sterling nodded along, but he remained quiet. He did not have a heart for God. God had burned down his house with his family in it. God had hardened his woman's heart against him. God had put him in chains. All the same, he would rather sit on this stump and listen to the man in the top hat than lay down road. It was a Sunday, the first day off in the camp since they'd begun paving the Straight. Sterling's muscles ached in a way that was almost pleasant, the way they ached only after they'd had a chance to rest. The insides of his ankles, from the shackles. His back, his arms, especially the right. Yesterday, the fry cakes and Bibles; today the preacher. They were gifts from the woman he understood to be Freddie's grandmother, George Wilson's woman. And tonight, if talk was true, there would be another gift, this one from the warden.
When the preacher turned to the Bible, and spoke of repentance, Sterling did not nod. He did not think of the crimes that had brought him here to the camp, which were not crimes. He did not think of anything. But later, after the preacher left and after the midday meal, the prisoners were sent to piss out near the turkey oaks. The white men were sent first, then the colored. The eight of them stood in a silent row, their ankles chained, draining themselves into the dirt. Then they were sent to wash their hands and faces in the nearly dry creek bed. Sterling could barely see his own face in the creek, let alone God's. But staring off into the surface, staring down the hours left in the day, Sterling did think then of Ketty on Juke's bed, the pearls hailing down on her back. And without meaning to, his mind called up its picture of his parents dropping to their knees, and as they fell their names rose up—Joseph, Lucille—and his eyes stung.
He splashed his face with the cold water. He shook his head like a dog. Still squatting, he warmed his numb hands in his armpits. Maybe it wasn't God who'd put him in chains. Maybe it wasn't even white men. Maybe it was one white man. A man who'd seen to it to lynch a Negro on his farm, and then to go on playing daddy to Sterling's child. And there he lived and breathed, just down the Straight, without a care in the world.
He wanted to save some hate for the warden but tonight the warden made it hard to hate him. It was Sunday, and to keep them happy and working another week he drove into the camp with two whores in his truck. "Y'all bad boys paving a good road. I brought y'all a reward for being so good." Sterling had been with one of them, the older one, at the county camp |
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] | of ratios of average wages of higher education and secondary school graduates ( ) May
7 Acknowledgements In various ways, numerous researchers from around the globe have provided input to this project, which the authors gratefully acknowledge. The authors would like to thank in particular our international advisors D. Bruce Johnstone, Jan Sadlak and Jamil Salmi for providing their expertise at various stages of the project; it was invaluable for designing the study and conducting the analyses on which this final report is based. Furthermore, the authors would like to acknowledge the support in data research and interpretation provided by the national experts: Martin Unger and Jakob Hartl (Austria), Alison Johnston (UK-England), Jussi Kivistö and Elias Pekkola (Finland), Tamás Kozma, József Temesi and Zsuzsanna Veroszta (Hungary), Cezary Kościelniak (Poland), Luisa Cerdeira (Portugal) and Hwanbo Park (South Korea). Finally, the authors would like to acknowledge the contribution of Lori McElroy, Pamela Marcucci and Ronja Wöstheinrich, who helped conduct background research and improve the national and comparative reports. May
8 Executive Summary Higher education systems have been and are still faced with the task of accommodating growing numbers of students without compromising the quality of education, and without creating undesirable inequalities in access and completion. It is in this context that governments have been adjusting the balance of public and private resources with the goal of achieving more inclusive, more effective, and more sustainable higher education systems. Internationally comparative data sets show that over the last two decades, there has been a general shift towards larger shares of private funding of higher education (see Chapter 2). This shift was achieved largely through raising additional or higher revenues from tuition fees (sometimes termed student contributions ). The concept of cost-sharing is used here to investigate this shift in the balance of public and private funding. Changes in the way costs are shared can take several forms, including the introduction of tuition fees where they did not previously exist or a sharp increase where they already did. They can also involve a reduction or even a freezing of student grants or student loan subsidies (reductions in student aid also constitute an increase in the private funding necessary to cover educational and living costs), but also public policies that encourage enrolment shifts from a heavily subsidised public sector to a much less subsidised, fee-dependent private sector. Looking at cost-sharing from the student perspective, the study focusses on student netcosts, i.e. total costs borne by a student after consideration of tuition fees and compensatory study aid. Even in countries without tuition fees, there is still a substantial amount of cost-sharing because no higher education system covers students' educational and living costs completely. It is surmised that an increase in private costs to students will impact on the behaviour of students. The study also looks at the institutional side of cost-sharing and investigates the changing balance of public and private revenues for higher education institutions (HEIs). From the perspective of HEIs, cost-sharing involves changes to the share of public and private funding as income sources (and thus the respective role of tuition fees, contract income, philanthropic donations, etc. as opposed to state funding). Since a change to this balance affects the relative importance of these sources, it is assumed that it will also change the behaviour of HEIs. Reflecting on the expected impacts on student and HEI behaviours of changes to the cost- sharing balance, the study is framed by four hypotheses as part of a single comprehensive policy analysis model. The hypotheses pick up on key aspects of sustainability, effectiveness and equity of tuition fee policies in higher education. Hypothesis A: As private funding increases, total revenue of HEIs increases. Hypothesis B: As the incentives to earn private funding increase, HEIs become more responsive to student demand. Hypothesis C: Increasing private funding has a negative effect on student demand. Hypothesis D: Increasing private funding affects student choice of how and what to study. To investigate how public and private funding changed and what impact various changes have had on the behaviour of HEIs and students, the authors carried out nine systematic country case studies seven countries from the European Union and two from outside Europe (Austria, Canada, England, Finland, Germany, Hungary, Poland, Portugal and May
9 South Korea). This selection of countries provides a variety of settings in which to investigate the effects. Different countries were chosen according to the following criteria: geographic coverage, economic strength, population size, enrolment numbers, share of students in private higher education, degree of public funding of institutions, changes to private funding, tuition fee policy, and student financial aid provision. It was surmised that the countries with the biggest and most rapid shifts in tuition fee policy would be most interesting for the study, since such shifts could be expected to have greater impact on student and HEI behaviour than a generally stable tuition fee policy. For this reason the cases were split into discontinuity countries (Austria, England, Germany and Portugal) and continuity countries (Canada, Finland, Hungary, Poland and South Korea) for the analysis. The table below presents a summary overview of the country patterns (a detailed overview of the developments can be found in Table 2.2 of the main report). Overview of tuition fee policy change in the period of investigation Share of fee-paying students Increased England 1998 England 2006 Germany 2006/07 Austria 2001 Stayed the same Finland Portugal 2003 (England 2012)* South Korea Canada Decreased Poland Austria 2009 Germany Hungary Average amount paid per student Decreased Stayed the same Increased Note: When no year is given it means that any change was gradual. (England 2012)*: The 2012 tuition fee reform in England is outside the period of investigation of this study, but will be included where data are available and relevant for the purpose of this study. For each case study country, a detailed national report assembles all the elements of the respective cost-sharing system to fully portray the balance between public and private sources of revenues (see National Reports in the main appendix). Main Findings From the analysis, we can make the following observations with respect to how changes in fees affect students and institutions: Higher education data systems remain incomplete The state of the data needed to reasonably assess the state of access to higher education is in many countries fairly poor. In some countries notably Hungary and Poland even obtaining simple information about institutional income sources is nearly impossible. Furthermore, even tracking the level of tuition fees which are actually paid remains difficult in some jurisdictions. This situation presents a constraint for policy-based research on a national level, but makes studies aiming to provide a robust evidence base on a cross-national level even more challenging. The conclusions drawn from this study must be viewed in the light of this fact. May
10 The introduction of tuition fees usually makes the system better-off overall, by increasing the total amount of resources available The study concludes that as private funding for institutions increased, public revenues did not tend to decrease, bringing about an overall increase in institutional income (Hypothesis A, see Chapter 3). The hypothesis was tested using per-student income from public and private sources as a basic indicator. It was found that for most countries and periods of time, the hypothesis can be verified. The few periods of decreasing public perstudent income corresponded to phases of either economic crisis (Canada in the 1990s) or massive enrolment growth (Poland in the 1990s to early 2000s, Austria in the late 2000s). In these specific cases, overall student funding (public and private funding combined) also decreased, i.e. private funding was not used to compensate for this. Most interesting from a policy evaluation perspective are cases in which governments had the goal of bringing about shifts in the cost-sharing balance. Such attempts were made in four of the case study countries: Austria, England, Germany and Portugal. It was shown that from a purely financial point of view, the tuition fee reforms in Germany and Portugal were comparatively modest in scope. Their aim was to provide the system with more funds, without fundamentally altering the predominance of the public sector in higher education funding. The analysis showed that this objective was achieved in Portugal and to some degree in Germany. In Austria, the initial goal of introducing tuition fees appears to have been to bring about a net shift in the cost-sharing balance rather than to increase the funds available to the system. If this is the correct interpretation of the underlying policy goals, then the Austrian reform can be considered a (short-lived) success: the introduction of tuition fees changed the cost-sharing balance, but did not lead to an increase in overall funds for institutions. In England, in contrast to the other three discontinuity countries studied here, the tuition fee reforms successively transformed the system into a model in which private contributions serve as a mainstay of institutional funding of higher education. Particularly the most recent reform from 2012 (which cannot yet be evaluated sufficiently because it is so recent) shows a clear shift to private funding of higher education with liquidity and affordability problems being alleviated through a comprehensive public loan scheme. The resources gained through new fee-derived income are not always invested in ways that would be expected to perceptibly improve the student experience We note that across a number of case studies, the tendency over time was for studentsper-staff ratios to rise, even when institutional income per-student was rising. This is a question of HEI behaviour investigated using Hypothesis B (see Chapter 4). The most extreme example of this was in Canada, where student-teacher ratios rose detrimentally by 20% even as per-student income rose by 40%. Thus, while it may be true that fees make institutions better off, they do not necessarily make for a better student experience, even when per-student income is rising. There are three main reasons why this is so. Firstly, in some cases, new funds are dedicated to expansion rather than |
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15
] | jury finds the defendant guilty of first degree murder, it necessarily reflects a determination of the defendant's intent. Thus, in Sedeno, the diminished capacity defense would not have permitted the jury to find involuntary manslaughter in the face of the finding of express malice, i.e., express intent, reflected in the first degree murder conviction. In Polley, the questions whether the killing was intentional, accidental, or the product of the negligent use of a firearm, were necessarily resolved against the defendant by the jury's first degree murder verdict. Finally, in Parnell, the jury's finding of felony murder foreclosed the issue of diminished capacity.
We believe the present case is governed by the reasoning in Sedeno, Polley, and Parnell. Here, the issue of accidental shooting raised by appellants goes to the intent with which DeJesus shot and killed Zeitsoff. We find *22 the jury's verdict resolved the factual issues raised by appellants against them for two reasons.
First, the jury here, unlike the juries in Glenn, Webber, and Ceja, convicted appellants of first degree murder. Thus, the jury found express malice, i.e., the intent to kill and found the murder was premeditated, deliberate and/or willful. Thus, having reached this factual conclusion, the jury could not have found defendants guilty of involuntary manslaughter. (People v. Sedeno, supra, 10 Cal.3d at p. 721.) Thus, as in Polley, the first degree murder conviction necessarily determined the issue raised by the defense of accident.
Second, the jury here, as in Parnell, found true the special circumstances alleged by the prosecution, i.e., that the murder was done after lying in wait, and that the murder was committed for financial gain. This, too, demonstrates the jury's factual determination the killing was intentional and deliberate.[14] Obviously, the jury could not have reached this verdict if it gave credence to the claim of an accidental shooting. At most, it would have found second degree murder without the special circumstances. Thus, here, the "omitted instruction was necessarily resolved adversely to [appellants] under other, properly given instructions."[15]
Therefore, we hold that to the extent the failure to give the involuntary manslaughter instruction was error, the error was harmless.
C. The Accomplice Instruction.
(6a) Appellants contend the trial court erred in not giving their requested accomplice instruction in connection with Lohr's testimony. We agree. However, in light of the substantial corroboration of Lohr's testimony and the circumstances which would cause the jury to view his evidence with distrust, we find the error harmless.
(7) Section 1111 provides the testimony of an accomplice, i.e., a person who may be charged with the same crime as the defendant, must be *23 corroborated.[16] In order for the witness to be chargeable with the same crime as the defendant: "... it would be necessary for the witness to be considered a principal under the provisions of section 31, which includes `[a]ll persons concerned in the commission of a crime ... whether they directly commit the act constituting the offense, or aid and abet in its commission, or not being present, have advised and encouraged its commission....'" (People v. Hoover (1974) 12 Cal.3d 875, 879 [117 Cal. Rptr. 672, 528 P.2d 760].)
To be an accomplice, a witness must have "`"guilty knowledge and intent with regard to the commission of the crime...."'" (People v. Daniels (1991) 52 Cal.3d 815, 866-867 [277 Cal. Rptr. 122, 802 P.2d 906], citations omitted.) The definition of an accomplice "encompasses all principals to the crime including aiders and abettors and coconspirators." (People v. Stankewitz (1990) 51 Cal.3d 72, 90 [270 Cal. Rptr. 817, 793 P.2d 23].) To be an accomplice, one must act "`with knowledge of the criminal purpose of the perpetrator and with an intent or purpose either of committing, or of encouraging, or facilitating commission of, the offense.'" (Id. at pp. 90-91.)
The defendant has the burden of proving the witness's status as an accomplice by a preponderance of the evidence. (People v. Fauber (1992) 2 Cal.4th 792, 834 [9 Cal. Rptr.2d 24, 831 P.2d 249]; People v. Sully (1991) 53 Cal.3d 1195, 1228 [283 Cal. Rptr. 144, 812 P.2d 163].) Where the facts are in dispute, the issue is one for the jury. (People v. Stankewitz, supra, 51 Cal.3d at pp. 90-91; People v. Sully, supra, 53 Cal.3d at pp. 1227-1228; People v. Tewksbury (1976) 15 Cal.3d 953, 960 [127 Cal. Rptr. 135, 544 P.2d 1335]; People v. Mayberry (1975) 15 Cal.3d 143, 159 [125 Cal. Rptr. 745, 542 P.2d 1337].)
(6b) Here, appellants requested an accomplice instruction which the trial court refused on the basis appellants had not met their burden to prove Lohr was an accomplice by a preponderance of the evidence. While we do not believe Lohr was an accomplice as a matter of law, we do agree with appellants the issue was not free from doubt and should have been determined by the jury.
The principal question is whether Lohr had the requisite guilty knowledge and intent relating to the crime. Lohr indicated that, during the days preceding the murder, he had not taken Duncan seriously and had tried to talk him *24 out of killing Zeitsoff. On the day of the murder, he told Duncan he would not be in the room, and, in fact, went into the bathroom when Zeitsoff arrived. To explain his failure to leave the premises completely and his involvement in moving the body after the murder, he indicated he was concerned that if he left the house or did not help in moving the body, he would be kicked out of Duncan's house and/or Duncan would no longer supply him with drugs.
On the other hand, Lohr testified he was aware of the plan. He knew DeJesus had been procured by Duncan to perpetrate the murder of Zeitsoff. When asked about his intended involvement in the plot, Lohr's response was, at best, enigmatic:
"Q. You had already told Ian, had you not, that you did not want to be involved in a in a homicide; in a killing?
"A. I told him I would not kill [Zeitsoff].
"Q. Did you tell [Duncan] that although you would not pull the trigger, you would be willing to help out?
"A. Yes.
".... .... .... .... .... .... ....
"Q. Do you have a recollection of telling [Duncan] in what manner you would be willing to help out?
"A. In helping get rid of the body."
With respect to his reason for wanting to be in the bathroom during the actual commission of the murder, Lohr testified he wanted to be there "[i]n case something went wrong." This suggests his reason was to protect himself, not to avoid participation in murder itself. At trial, he testified he was "not 100%" sure the murder would occur that night, but on prior occasions, he stated he knew it would occur. In any event, he never attempted to warn Zeitsoff of the plot to kill him.
After the murder, Lohr helped remove Zeitsoff's possessions from the BMW. He assisted in putting plastic covering over the body and in moving the body to the trunk of the BMW. He also assisted in wiping his and appellants' fingerprints off the car. He accepted $100 from Duncan, though it was not clear if this was for his help in removing the body or to "keep him quiet."
*25 We conclude that, based upon all of the facts, the jury could have decided Lohr's testimony indicated he acted "`with knowledge of the criminal purpose of the perpetrator and with an intent or purpose either of committing, or of encouraging, or facilitating commission of, the offense.'" (People v. Stankewitz, supra, 51 Cal.3d at p. 90.) Accordingly, the issues of whether Lohr was an accomplice and the effect on his testimony if he was should have been submitted to the jury. (People v. Sully, supra, 53 Cal.3d at pp. 1227-1228; People v. Mayberry, supra, 15 Cal.3d at p. 159.)
However, the error was harmless here for two reasons. First, there was substantial evidence to corroborate Lohr's testimony in the record. (8) "It has been recognized that the failure to instruct on accomplice testimony pursuant to section 1111 is harmless where there is sufficient corroborating evidence in the record...." (People v. Miranda (1987) 44 Cal.3d 57, 100 [241 Cal. Rptr. 594, 744 P.2d 1127], citations omitted.) The corroborating evidence may be entirely circumstantial. (Ibid.; People v. Thurman (1972) 28 Cal. App.3d 725, 730 [104 Cal. Rptr. 804].) The corroborating evidence may be "`slight and entitled to little consideration when standing alone.'" (People v. |
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] | ,
input logic we_core_i,
output logic we_mem_o,
input logic [3:0] be_core_i,
output logic [3:0] be_mem_o,
input logic [31:0] stall_en_i,
input logic [31:0] stall_mode_i,
input logic [31:0] max_stall_i,
input logic [31:0] gnt_stall_i,
input logic [31:0] valid_stall_i
);
import cv32e40p_pkg::*;
import perturbation_pkg::*;
`ifndef VERILATOR
logic req_per_q, grant_per_q, rvalid_per_q;
typedef struct {
logic [ADDR_WIDTH-1:0] addr;
logic we;
logic [ 3:0] be;
logic [DATA_WIDTH-1:0] wdata;
logic [DATA_WIDTH-1:0] rdata;
} stall_mem_t;
class rand_gnt_cycles;
rand int n;
endclass : rand_gnt_cycles
class rand_data_cycles;
rand int n;
endclass : rand_data_cycles
mailbox #(stall_mem_t) core_reqs = new ();
mailbox #(stall_mem_t) core_resps = new ();
mailbox #(logic) core_resps_granted = new ();
mailbox #(stall_mem_t) memory_transfers = new ();
always_latch
begin
if (req_core_i)
req_per_q <= 1'b1;
else
req_per_q <= 1'b0;
end
always_latch
begin
if (rvalid_mem_i)
rvalid_per_q <= 1'b1;
else
rvalid_per_q <= 1'b0;
end
always_latch
begin
if (grant_mem_i)
grant_per_q <= 1'b1;
else
grant_per_q <= 1'b0;
end
//Grant Process
initial
begin
stall_mem_t mem_acc;
automatic rand_gnt_cycles wait_cycles = new ();
int temp;
int stalls, max_val;
#10;//wait at the very beginning
while(1) begin
@(posedge clk_i);
grant_core_o = 1'b0;
#1;
if (!req_per_q) begin
wait(req_per_q == 1'b1);
end
if ((stall_en_i[0]) && (stall_mode_i == STANDARD)) begin //FIXED NUMBER OF STALLS MODE
stalls = gnt_stall_i;
end else if ((stall_en_i[0]) && (stall_mode_i == RANDOM)) begin
max_val = max_stall_i;
temp = wait_cycles.randomize() with{
n >= 0;
n<= max_val;
};
stalls = wait_cycles.n;
end else begin
stalls = 0;
end
while(stalls != 0) begin
@(negedge clk_i);
stalls--;
end
@(negedge clk_i);
if(req_per_q == 1'b1) begin
grant_core_o = 1'b1;
mem_acc.addr = addr_core_i;
mem_acc.be = be_core_i;
mem_acc.we = we_core_i;
mem_acc.wdata = wdata_core_i;
core_reqs.put(mem_acc);
core_resps_granted.put(1'b1);
end
end
end
initial
begin
stall_mem_t mem_acc;
automatic rand_data_cycles wait_cycles = new ();
logic granted;
int temp, stalls, max_val;
while(1) begin
@(posedge clk_i);
rvalid_core_o = 1'b0;
rdata_core_o = 'x;
#1;
core_resps_granted.get(granted);
core_resps.get(mem_acc);
if ((stall_en_i[0]) && (stall_mode_i == STANDARD)) begin //FIXED NUMBER OF STALLS MODE
stalls = valid_stall_i;
end else if ((stall_en_i[0]) && (stall_mode_i == RANDOM)) begin
max_val = max_stall_i;
temp = wait_cycles.randomize() with {
n>= 0;
n<= max_val;
};
stalls = wait_cycles.n;
end else begin
stalls = 0;
end
while(stalls != 0) begin
@(negedge clk_i);
stalls--;
end
rdata_core_o = mem_acc.rdata;
rvalid_core_o = 1'b1;
end
end
initial
begin
stall_mem_t mem_acc;
we_mem_o = 1'b0;
req_mem_o = 1'b0;
addr_mem_o = '0;
be_mem_o = 4'b0;
wdata_mem_o = 'x;
while(1) begin
@(posedge clk_i);
#1;
req_mem_o = 1'b0;
addr_mem_o = '0;
wdata_mem_o = 'x;
core_reqs.get(mem_acc);
req_mem_o = 1'b1;
addr_mem_o = mem_acc.addr;
we_mem_o = mem_acc.we;
be_mem_o = mem_acc.be;
wdata_mem_o = mem_acc.wdata;
wait(grant_per_q);
memory_transfers.put(mem_acc);
end
end
initial
begin
stall_mem_t mem_acc;
while(1) begin
memory_transfers.get(mem_acc);
wait(rvalid_per_q == 1'b1);
@(negedge clk_i);
mem_acc.rdata = rdata_mem_i;
core_resps.put(mem_acc);
end
end
`endif
endmodule
A headrest positioning system for automatically adjusting the position of a headrest of a vehicle seat. The headrest can be a power headrest that is part of a power seat system, where the position of the headrest can be preset by a seat occupant with the other positions of the seat in a memory function....http://www.google.com/patents/US20060250016?utm_source=gb-gplus-sharePatent US20060250016 - Automatically adjusting head restraint system
A headrest positioning system for automatically adjusting the position of a headrest of a vehicle seat. The headrest can be a power headrest that is part of a power seat system, where the position of the headrest can be preset by a seat occupant with the other positions of the seat in a memory function. The system includes a seat occupant sensing system that senses the position of the seat occupant's head, eyes or other facial features to determine a desired position of the headrest. The system compares the actual position of the headrest to the desired position, and automatically adjusts the position of the headrest to the desired position. In one embodiment, an imaging system determines the position of the seat occupant's head. In another embodiment, the system determines the position of seat occupant's eyes based on the position of the driver side mirror.
Images(7)
Claims(22)
1. A headrest positioning system for automatically positioning a headrest of a vehicle seat, said system comprising:
a headrest sensor for detecting the position of the headrest, said headrest sensor generating a headrest position signal indicative of the position of the headrest;
a seat occupant detecting sub- |
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] |
THGenerateFloatTypes.h
THGenerateComplexTypes.h
THGenerateIntTypes.h
THGenerateQUInt8Type.h
THGenerateQInt8Type.h
THGenerateQInt32Type.h
THGenerateQTypes.h
THLapack.h
THStorage.h
THStorageFunctions.h
THTensor.h
THTensorApply.h
THTensorDimApply.h
THVector.h
THHalf.h
THTensor.hpp
THStorageFunctions.hpp
THGenerator.hpp
DESTINATION "${ATEN_INSTALL_INCLUDE_SUBDIR}/TH")
install(FILES
vector/AVX.h
../ATen/native/cpu/avx_mathfun.h
DESTINATION "${ATEN_INSTALL_INCLUDE_SUBDIR}/TH/vector")
install(FILES
generic/THBlas.cpp
generic/THBlas.h
generic/THLapack.cpp
generic/THLapack.h
generic/THStorage.cpp
generic/THStorage.h
generic/THStorageCopy.cpp
generic/THStorageCopy.h
generic/THTensor.cpp
generic/THTensor.h
generic/THTensor.hpp
generic/THTensorLapack.cpp
generic/THTensorLapack.h
generic/THTensorMath.cpp
generic/THTensorMath.h
generic/THTensorRandom.cpp
generic/THTensorRandom.h
generic/THVectorDispatch.cpp
generic/THVector.h
# See Note [TH abstraction violation]
generic/THTensorFastGetSet.hpp
DESTINATION "${ATEN_INSTALL_INCLUDE_SUBDIR}/TH/generic")
2001 Bolivian census
The Tenth Census of Bolivia was conducted on 5 September 2001. The population was 8,274,325.
References
Category:2001 in Bolivia
Category:Censuses in Bolivia
BoliviaMa’an News Agency
As Palestinian leaders prepare to seek UN recognition of statehood in September, there is increasing talk in the US, Israel and elsewhere of disbanding the UN agency for Palestinian refugees, the UN Relief and Works Agency, and handing responsibility for Palestinian refugees to the UN High Commission for Refugees (UNHCR).
Some argue UNHCR would resettle the refugees, robbing them of their right to return to their homes.
But are these ideas based on a sound understanding of international law and refugee practice? Are they based on a real grasp of the mandates of UNRWA and UNHCR?
To set the record straight, Ma‘an turned to UNRWA spokesman Chris Gunness.
It is argued that if UNRWA was disbanded and responsibility for Palestinian refugees handed over to UNHCR they would be resettled out of Israel and give up the right of return. Is this the case and if not, what would UNHCR’s role be?
Gunness: This is not the case. Palestine refugees are entitled to a just and lasting solution to their plight. This solution would optimally be achieved by the parties and political actors in the context of a negotiated conclusion of the Israeli-Palestinian conflict, and must be in accordance with UN resolutions and international law.
It should also be voluntary which means consulting the refugees. There is no merit to claims that the Palestine refugee issue can be addressed by transferring responsibility for Palestine refugees from UNRWA to another agency.
Please note two important caveats. First, UNRWA and UNHCR work to distinct mandates, operational and legal definitions, areas of operation, operational realities and constitutive instruments. My responses are consistent with these distinctions and should not be read to assume or imply direct correspondence between the two agencies or their refugee definitions. Rather, I should be understood to draw on the shared premises and common concepts that underpin the precepts applied by UNRWA and UNHCR in their efforts to assist and protect refugees in their respective areas of responsibility.
Second, UNRWA is not in a position to speak for UNHCR and does not purport to speak for UNHCR. However, responses to your questions require reference to documents that are posted on UNHCR’s website and are available to the public. My responses are based on UNRWA’s understanding of the plain meaning of these documents as well as the Agency‘s own appreciation of its mission and its knowledge of the system of international law and practice that govern the protection of refugees globally.
Established principles and practice – as well as realities on the ground - clearly refute the argument that the right of return of Palestine refugees would disappear or be abandoned if UNHCR were responsible for these refugees.
Over decades of international practice, refugee situations have been resolved in three principal ways: local integration, resettlement in third countries and voluntary repatriation. Of these, the voluntary return of refugees to their country of origin has come to be recognised by refugees, states and international agencies as the optimal solution to the plight of refugees.
It is equally recognised that for refugees everywhere, a precondition for solutions to refugee situations is the resolution by political actors of the underlying causes of dispute and conflict.
This point is made in the 2008 UNHCR document, Protracted Refugee Situations: A discussion paper prepared for the High Commissioner’s dialogue on Protection Challenges. Paragraph 7 of the document observes that "Protracted refugee situations are usually created and sustained by the failure to resolve … differences in a peaceful manner and in a way that respects human rights."
Paragraph 9 of this document goes on to note that: "[…] the functions of refugee protection and humanitarian action, vital as they are, can make only a very modest contribution to the prevention and resolution of conflicts that oblige people to live in exile for long periods of time. If those objectives are to be attained, political will and political action are required on the part of states, regional organisations and relevant components of the UN system, including the Security Council and General Assembly."
The preface to the UNHCR Handbook on Voluntary Repatriation states that "voluntary repatriation is usually viewed as the most desirable long-term solution by the refugees themselves as well as by the international community. UNHCR‘s humanitarian action in pursuit of lasting solutions to the refugee problems is therefore oriented, first and foremost, in favour of enabling a refugee to exercise the right to return home in safety and with dignity."
Chapter 1, paragraph 6 of the UNHCR Handbook states that "the right of refugees to return to their country of origin is fully recognised in international law. The Universal Declaration of Human Rights (1948) established in article 13 (2) that "Everyone has the right to leave any country, including his own, and return to his country." While the Universal Declaration of Human Rights, as a Resolution of the General Assembly, is not a treaty requiring signature or consent, it sets the code of conduct and serves as a point of reference for all universal and regional human rights instruments subsequently adopted."
The UNCHR Handbook in its subsequent chapters sets out the obligations of states and of UNHCR in protecting the right of return in the context of voluntary repatriation as well as monitoring, protecting and advocating for the rights of refugees who have returned home.
Conclusion 4 of the report to UNHCR’s Executive Committee of 2005, on Local Integration states "Voluntary repatriation, local integration and resettlement are the traditional durable solutions and […] all remain viable and important responses to refugee situations;" "voluntary repatriation, in safety and dignity, where and when feasible, remains the most preferred solution in the majority of refugee situations;" a combination of solutions, taking into account the specific circumstances of each refugee situation, can help achieving long lasting solutions."
It is often said that UNRWA perpetuates the Palestinian refugee problem by granting refugee status through the generations and that handing the refugees over to UNHCR would not allow this. Is this the case?
This is not the case. As I have already noted, Palestine refugees are entitled to a just and lasting solution to their plight. In the absence of -- and pending the realisation of -- such a solution, it stands to reason that their status as refugees will remain.
Questions raised about the passing of refugee status through generations stem from a lack of understanding of the international protection regime. These questions serve only to distract from the need to address the real reasons for the protracted Palestinian refugee situation, namely the absence of negotiated solution to the underlying political issues.
UNHCR‘s Handbook on Procedures and Criteria for determining Refugee Status provides in paragraph 184: "If the head of a family meets the criteria of the definition, [for refugee status] his dependants are normally granted refugee status according to the principle of family unity."
In effect, refugee families everywhere retain their status as refugees until they fall within the terms of a cessation clause or are able to avail themselves of one of three durable solutions already mentioned -- voluntary repatriation, local integration or resettlement in a third country.
Also, Chapter 5 of the UNHCR publication, Procedural Standards for Refugee Status Determination under UNHCR’s Mandate is very clear that in accordance with the refugee’s right to family unity, refugee status is transferred through the generations. According to Chapter 5.1.2 "the categories of persons who should be considered to be eligible for derivative status |
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4799,
437
] | 2))$ is a deformation retract of $\Omega^{(1,0)}(n+1,n+1)$, there exists the homotopy equivalent fiber map$$\mathfrak{i}_{SO}:SO_{n+2}(\gamma_{G_{n+1}}^{n+1}\oplus\varepsilon_{G_{n+1}}^{1},TP\oplus\varepsilon_{P}^{2})\longrightarrow\boldsymbol{\Omega}_{n+1}^{(1,0)}=\Omega^{(1,0)}(\gamma_{G_{n+1}}^{n+1},TP\oplus\varepsilon
_{P}^{1})$$ over $G_{n+1}\times P$ associated to $i_{n+1}^{SO}$. The image is denoted by $\mathbf{SO}_{n+2}$. Furthermore, $\mathfrak{i}_{SO}$ induces the bundle map $$\mathfrak{B}:\widehat{\gamma}_{\boldsymbol{\Omega}_{n+1}^{(1,0)}}^{\ell
}|_{\mathbf{SO}_{n+2}}\longrightarrow\widehat{\gamma}_{\boldsymbol{\Omega
}_{n+1}^{(1,0)}}^{\ell}$$ covering the inclusion $\mathbf{SO}_{n+2}\rightarrow\boldsymbol{\Omega}_{n+1}^{(1,0)}$.
\(i) The fiber map $\mathfrak{i}_{SO}$ is a homotopy equivalence.
\(ii) The Thom map$$T(\mathfrak{B)}:T\left( \widehat{\gamma}_{\boldsymbol{\Omega}_{n+1}^{(1,0)}}^{\ell}|_{\mathbf{SO}_{n+2}}\right) \longrightarrow T\left( \widehat
{\gamma}_{\boldsymbol{\Omega}_{n+1}^{(1,0)}}^{\ell}\right)$$ is a homotopy equivalence.
Let $SO(TP\oplus\varepsilon_{P}^{1})$ denote the total space of the principal bundle over $P$ associated to $TP\oplus\varepsilon_{P}^{1}$, whose fiber is $SO(n+1)$. Let $(SO(\ell)\times E_{n+1})\backslash SO(n+\ell+1)$ be the Stiefel manifold. Consider the natural actions of $SO(n+1)$ on $SO(TP\oplus
\varepsilon_{P}^{1})$ from the right-hand side and on $SO(n+2)$ through $SO(n+1)\times E_{1}$ from the left-hand side, and the natural actions of $SO(n+1)$ on $SO(n+2)$ through $SO(n+1)\times E_{1}$ from the right-hand side and on $(SO(\ell)\times E_{n+1})\backslash SO(n+\ell+1)$ from the left-hand side respectively. Then we can express as$$\begin{aligned}
\mathbf{SO}_{n+2} & =SO(TP\oplus\varepsilon_{P}^{1})\underset{SO(n+1)}{\times}SO(n+2)\\
& \text{ \ \ \ \ \ \ \ \ }\underset{SO(n+1)}{\times}((SO(\ell)\times
E_{n+1})\backslash SO(n+\ell+1)).\end{aligned}$$ Identify the quotient space $SO(TP\oplus\varepsilon_{P}^{1})/SO(n+1)$ with $P$. Then we have the projection $pr_{P}^{SO}:\mathbf{SO}_{n+2}\rightarrow
P$ by forgetting the component $SO(n+2)\underset{SO(n+1)}{\times}((SO(\ell)\times E_{n+1})\backslash SO(n+\ell+1))$, denoted by $\mathfrak{f}$, which is the canonical fiber of $pr_{P}^{SO}$. Let $pr_{S^{n+1}}^{\mathfrak{{f}}}:\mathfrak{f}\rightarrow SO(n+2)/SO(n+1)=S^{n+1}$ be the projection forgetting the component $(SO(\ell)\times E_{n+1})\backslash
SO(n+\ell+1)$. Since the last space is $(\ell-1)$-connected,$$(pr_{S^{n+1}}^{\mathfrak{f}})_{\ast}:\pi_{i}(\mathfrak{f})\rightarrow\pi
_{i}(S^{n+1})$$ is an isomorphism for $i<\ell$. Hence, we have the following lemma.
The homomorphism $(pr_{P}^{SO})_{\ast}:\pi_{i}(\mathbf{SO}_{n+2})\rightarrow\pi_{i}(P)$ is an isomorphism for $i<n+1$ and is an epimorphism for $i=n+1$.
Let us define a bundle map$$\mathbf{b}_{\mathcal{T}}:(\pi_{G_{n+1}}^{k})^{\ast}(\gamma_{G_{n+1}}^{n+1})|_{\mathbf{SO}_{n+2}}\longrightarrow TP\oplus\varepsilon_{P}^{1}$$ as follows. We denote an element of $\mathbf{SO}_{n+2}$ and $(\pi_{G_{n+1}}^{k})^{\ast}(\gamma_{G_{n+1}}^{n+1})|_{\mathbf{SO}_{n+2}}$ by $[a,y,h]$ and by $[a,y,h,\mathbf{v]}$ respectively, where $a\in G_{n+1}$, $y\in P$, $\mathbf{v\in}a$ and $h:a\rightarrow T_{y}P\oplus\mathbb{R}$ is an isomorphism preserving orientations and norms. Then we set $\mathbf{b}_{\mathcal{T}}([a,y,h,\mathbf{v])}=h(\mathbf{v)}$. Let us consider the trivialization $(t_{P}\oplus id_{P}\times\mathbb{R})\circ(id_{TP}\oplus\mathbf{k}_{P}^{\backsim}):TP\oplus\varepsilon_{P}^{1}\oplus\nu
_{P}^{\ell}\rightarrow\varepsilon_{P}^{n+\ell+1}$. By [@FoldSurg Proposition 3.3], there exists a bundle map$$\mathbf{b}_{\nu}:\widehat{\gamma}_{\boldsymbol{\Omega}_{n+1}^{(1,0)}}^{\ell
}|_{\mathbf{SO}_{n+2}}\rightarrow\nu_{P}^{\ell}$$ such that $(t_{P}\oplus id_{P}\times\mathbb{R})\circ(id_{TP}\oplus
\mathbf{k}_{P}^{\backsim})\circ(\mathbf{b}_{\mathcal{T}}\oplus\mathbf{b}_{\nu
})\circ t_{\boldsymbol{\Omega}_{n+1}^{(1,0)}}|_{\mathbf{SO}_{n+2}}$ is homotopic to $pr_{P}^{SO}\times id_{\mathbb{R}^{n+\ell+1}}$. Then we have the following lemma.
The homomorphism induced from the Thom map of $\mathbf{b}_{\nu}$$$T(\mathbf{b}_{\nu})_{\ast}:\pi_{i+\ell}\left( T(\widehat{\gamma
}_{\boldsymbol{\Omega}_{n+1}^{(1,0)}}^{\ell}|_{\mathbf{SO}_{n+2}})\right)
\longrightarrow\pi_{i+\ell}\left( T(\nu_{P}^{\ell})\right)$$ is an isomorphism for $i<n+1$ and is an epimorphism for $i=n+1$.
By Lemma 6.2, (6.2), [@SpaAT Section 5, 9 Theorem] and the Thom Isomorphism Theorem,$$T(\mathbf{b}_{\nu})_{\ast}:H_{i+\ell}\left( T(\widehat{\gamma}_{\boldsymbol{\Omega}_{n+1}^{(1,0)}}^{\ell}|_{\mathbf{SO}_{n+2}})\right)
\longrightarrow H_{i+\ell}\left( T(\nu_{P}^{\ell})\right)$$ is an isomorphism for $i<n+1$ and is an epimorphism for $i=n+1$. Since $T((\pi_{G_{n+1}}^{k})^{\ast}(\widehat{\gamma}_{G_{n+1}}^{\ell})|_{\mathbf{SO}_{n+2}})$ and $T(\nu_{P}^{\ell})$ are simply connected, it follows from the Hurewicz Isomorphism Theorem that$$T(\mathbf{b}_{\nu})_{\ast}:\pi_{i+\ell}\left( T(\widehat{\gamma
}_{\boldsymbol{\Omega}_{n+1}^{(1,0)}}^{\ell}|_{\mathbf{SO}_{n+2}})\right)
\longrightarrow\pi_{i+\ell}\left( T(\nu_{P}^{\ell})\right)$$ is an isomorphism for $i<n+1$ and is an epimorphism for $i=n+1$.
According to [@SpaDual], let $\{S^{n+\ell},T(\nu_{P}^{\ell})\}$ denote the set of S-homotopy classes of S-maps $S^{i}\wedge S^{n+\ell}\rightarrow
S^{i}\wedge T(\nu_{P}^{\ell})$ $({i}\geq0)$. An element of $\{S^{n+\ell},T(\nu_{P}^{\ell})\}$ represented by a map $\alpha:S^{n+\ell}\rightarrow
S^{i}\wedge T(\nu_{P}^{\ell})$ is written as $\{\alpha\}$. Since $\ell\gg n$, $\{S^{n+\ell},T(\nu_{P}^{\ell})\}$ is isomorphic to $\pi_{n+\ell}\left(
T(\nu_{P}^{\ell |
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] | amme niiden lausekkeiden poistamista, joilla välittäjille myönnetään sopimusperusteinen suoja, on yksinkertaisesti se, että kilpailulainsäädäntö ei ole asianmukainen väline sopimusosapuolten välisen mahdollisen epätasapainon käsittelemiseen. Nämä kysymykset, kuten keskusteltiin, kun laadimme asetusta (EY) N:o 1/2003, kuuluvat kauppaoikeuden alaan.
Automarkkinoiden kaltaisilla kilpailukykyisillä markkinoilla kilpailulainsäädännöllä ei pitäisi puuttua eri sopimusosapuolten väliseen voimatasapainoon. Niin tekeminen olisi tungettelevaa. Meidän on toimittava oikeasuhteisesti, kun puutumme markkinoiden toimintaan.
Kuulemisprosessissa saimme selville myös muita, ehkä vähemmän myönteisiä asioita: toisin kuin autojen hinnat korjaustyön keskimääräinen hinta on itse asiassa noussut viime vuosien aikana. Korjaus ja huolto ovat kuluttajille hyvin tärkeitä: ei vain turvallisuuden ja luotettavuuden takia, vaan myös siksi, että korjauslaskut ovat 40 prosenttia auton omistamisen kokonaiskustannuksista. Valitettavasti riippumattomien korjaamojen valmiuksia kilpailla valtuutettujen korjaamojen kanssa haittaavat edelleen monet rajoitukset, mukaan luettuna varaosien ja teknisten tietojen rajallinen saatavuus. Uudistuksellamme pyritään siksi antamaan riippumattomille korjaamoille parempi pääsy varaosiin ja teknisiin tietoihin ja estämään se, että ne suljetaan markkinoilta muilla, uudemmilla käytännöillä. Se johtaa laadukkaampiin korjauspalveluihin ja alempiin hintoihin.
Lopuksi, uskon lujasti, että uudet puitteet ovat kuluttajille entistä suotuisammat. Ensisijainen tavoitteemme on lisätä kilpailua jälkimarkkinoilla - korjaus ja huolto - joilla sitä on vähiten? Vaikka ajoneuvojen valmistajien kaupallinen asema saattaa olla vahva välittäjiin verrattuna, ne taistelevat raivokkaasti toistensa kanssa, ja siksi nyt ei tarvita poikkeusta, tällaisia sopimuksia koskevan kilpailun säilyttämiseksi, alakohtaisia sopimuksia koskevasta ryhmäpoikkeusasetuksesta, jonka komissio antoi äskettäin ja joka myös tulee voimaan tämän kuukauden lopussa. Todellakin, komissio ja erityisesti minun yksikköni, kilpailun pääosasto, valvovat alaa hyvin huolellisesti, eikä pitäisi olla epäilystäkään komission päättäväisyydestä valvoa kilpailusääntöjä ja ryhtyä tarvittaviin toimiin, jos huomataan joitakin vakavia rikkomuksia tai puutteita.
Othmar Karas
Arvoisa puhemies, arvoisa komission jäsen, hyvät kollegat, olemme nyt kuulleet paljon. Tällä suullisella kysymyksellä ja päätöslauselmalla halusimme antaa autojen välittäjille ja pienille ja keskisuurille yrityksille äänen, koska komissio ei ole kiinnittänyt viime vuosina tarpeeksi huomiota tähän ääneen. Kuulemisen aikana ennen kaikkea autojen välittäjät ilmaisivat huolta ja epävarmuutta epätasa-arvoisesta kohtelusta valmistajiin verrattuna. Oikeudellinen epävarmuus otettiin esiin kuten myös kilpailun väheneminen pienten autonvälittäjien kohtaamien ongelmien takia. Emme kuitenkaan ole saaneet vastausta.
Pyydän teitä - minuuttia vaille 12 - käyttämään 21 päivää ennen toukokuun 26. päivää sisällyttääksenne päätöslauselman, jonka parlamentti antaa huomenna, asetukseenne, jotta voitte tulla autonvälittäjiä puolitiehen vastaan muuttamatta suuntaa, jonka komissio haluaa valita. Olkaa hyvä ja suhtautukaa parlamenttiin ja autonvälittäjien huoliin vakavasti ja sisällyttäkää heidän huolensa ja pienten ja keskisuurten yritysten huolet asetukseen.
Olle Ludvigsson
S&D-ryhmän puolesta. - (SV) Arvoisa puhemies, haluaisin korostaa neljää kysymystä tässä keskustelussa. Ensinnäkin, on olemassa valitettava pyrkimys asettaa autoteollisuuden pienet ja suuret yritykset toisiaan vastaan. Niillä on tietyssä määrin erilaiset edut, mutta meidän on ensisijaisesti keskityttävä luomaan sääntelyjärjestelmä, jonka avulla ne voivat tehdä tehokkaasti yhteistyötä.
Toiseksi, on hyvin myönteinen merkki, että uusien autojen markkinoiden kilpailu on parantunut viime vuoden aikana. Tämä on hyvä esimerkki siitä, että mitkään markkinat eivät ole mahdottomia ja että pitkällä aikavälillä on mahdollista saada paljon aikaan kilpailua vahvistavilla toimenpiteillä. Toivottavasti jälkimarkkinoilla kehitys on tulevaisuudessa yhtä myönteistä.
Kolmanneksi, on tärkeää, että komissio seuraa hyvin aktiivisesti kehitystä uusien autojen markkinoiden kilpailun osalta. Sitä pitäisi valvoa jatkuvasti. Kaikkien asianosaisten osapuolten pitäisi saada mahdollisimman pian lopulliset tiedot säännöistä, joita sovelletaan vuoden 2013 kesäkuusta alkaen.
Neljänneksi, meidän pitäisi tehostaa keskustelujamme siitä, miten aiomme vaihtaa vihreisiin, ympäristön kannalta suotuisampiin autoihin. Se on ehdottomasti olennaisen tärkeä prosessi. Toisaalta, kilpailusääntöjen on oltava joustavia niiden tukien osalta, joita tarvitaan, jotta sähköajoneuvot ja muut ympäristön kannalta suotuisat vaihtoehdot voivat vakiintua markkinoilla, ja toisaalta säännöillä on varmistettava, että ympäristön kannalta suotuisat autot eivät joudu epäedulliseen asemaan vähittäisalalla tai jälkimarkkinoilla.
Cristian Silviu Buşoi
ALDE-ryhmän puolesta. - (EN) Arvoisa puhemies, auton hankintaa ja omistamista pidetään yhtenä kotitalouksien suurimpana menoeränä. Kilpailupolitiikan perimmäinen tavoite on varmistaa kuluttajien valinnanvapaus ja tuotteiden saatavuus alemmilla ja koht |
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] | isoleucine, is tightly controlled by a complex interplay of transcriptional regulation and feedback inhibition mechanisms \[[@pcbi.1005371.ref018], [@pcbi.1005371.ref019]\]. In the other direction, the identification of specific regulatory programs that are utilized to avoid the accumulation of toxic intermediates is a promising avenue to discover novel endogenous antibiotics that allow to more efficiently kill pathogens through self-poisoning.
In this work, we use dynamic optimization to study how the toxicity of intermediates influences regulatory programs that control metabolic pathways. For such pathways, our optimization approach predicts a more focused regulation of enzymes that occur upstream of toxic intermediates. Moreover, there is a strong tendency of transcriptional regulation to target enzymes that are efficient in terms of catalytic activity as well as substrate affinity and possess less toxic upstream intermediates. Especially a focus on transcriptional regulation of efficient enzymes is surprising since it is often assumed that rate-limiting steps, which are catalyzed by inefficient enzymes, are key points of pathway control \[[@pcbi.1005371.ref020], [@pcbi.1005371.ref021]\]. We validate our prediction through a detailed examination of the interplay between enzyme efficiency as well as transcriptional control in *Escherichia coli* and a study of the relationship between regulatory effort as well as intermediate toxicity in the metabolic networks of more than 5000 prokaryotes. Moreover, we illustrate the usefulness of our approach through a detailed investigation of the influence of intermediate toxicity on the production of endogenous toxic metabolites in lipopolysaccharide metabolism.
Materials and methods {#sec002}
=====================
Modeling approach {#sec003}
-----------------
To model the toxicity of intermediates, we extended the optimization problem formulated by Wessely et al. \[[@pcbi.1005371.ref010]\] with the explicit consideration of toxicity of individual intermediates through concentration thresholds for each metabolite *β*~*i*~ ([Fig 1A](#pcbi.1005371.g001){ref-type="fig"}). The underlying ordinary differential equation system (ODE) describes a linear pathway converting a substrate *S* via five enzymatic reactions into a product *P* (see [S1 Text](#pcbi.1005371.s001){ref-type="supplementary-material"}). The selection of a five step linear pathway was made since it provides a good trade off between long and short linear pathways and is also the mean length of the defined linear pathways based on the listed pathways in MetaCyc \[[@pcbi.1005371.ref024]\]. Nevertheless, we obtain comparable results also for shorter and longer pathway lengths (see [S2 Text](#pcbi.1005371.s002){ref-type="supplementary-material"}).
![(A) Model of a linear pathway used for optimization. The substrate **S** is converted to a product **P** via five reactions catalyzed by enzymes with the concentration *e*~*j*~(*t*) with kinetic parameters *k*~*cat*~ and *K*~*m*~ and four intermediates *X*~*i*~ with concentration thresholds *β* that indicate their toxicity. The product **P** is diluted by *v*~*g*~ (dilution rate). (B) Objective function of the optimization problem. The objective function minimizes the regulatory effort of *e*(*t*) (green areas) and the initial concentration of enzymes *e*(0).](pcbi.1005371.g001){#pcbi.1005371.g001}
While the substrate of the pathway is assumed to be constant in concentration, the product undergoes a drain e.g. by growth or other environmental changes *v*~*g*~(*t*), which vary over time: $$\begin{aligned}
{v_{g}\left( t \right)} & {= \begin{cases}
{g_{1},} & {t < 10} \\
{g_{2},} & {10 \leq t < 20} \\
{g_{3},} & {20 \leq t \leq 30} \\
\end{cases}} \\
\end{aligned}$$ The dilution is balanced by the regulation of the concentration of enzymes that catalyze the conversion of metabolites modeled as irreversible Michaelis-Menten kinetics with the parameters *k*~*cat*~ and *K*~*m*~. As discussed previously \[[@pcbi.1005371.ref010]\], considering an outflow of the product through growth is conceptually identical to a change of product demand, but can be formulated more conveniently in the optimization approach. Results were found to be robust against variations in the preset changes of dilution rate *v*~*g*~(*t*) (see [S2 Text](#pcbi.1005371.s002){ref-type="supplementary-material"}). Therefore the same changes in dilution rate *v*~*g*~(*t*) like in Wessely et al. \[[@pcbi.1005371.ref010]\] were used for all optimization runs.
The toxicity of intermediates is simulated as constraints on the concentration of the metabolites: $$0 \leq x_{i}\left( t \right) \leq \beta_{i}$$
A biological interpretation of the toxicity threshold used in the optimization is, for instance, the half inhibitory concentration (IC50) of a metabolite, that is, the concentration at which half of a bacterial population is inhibited in its growth.
In the objective function we minimize two factors: the regulatory effort and protein costs. The regulatory effort is modeled as the deviation from initial enzyme concentrations and protein costs by the initial concentration of enzymes. Both objectives are weighted with the factor *σ*. Hence, the following objective function: $$\begin{aligned}
{F\left( e \right)} & {= \min\limits_{e_{1{(t)},...,e_{5}{(t)}}}\sum\limits_{j = 1}^{5}\left( {\int\limits_{0}^{T_{max}}\left( \sigma \cdot \underset{\text{abundance}}{\underset{︸}{e_{j}\left( 0 \right)}} + \underset{\text{regulation}}{\underset{︸}{\left( e_{j}\left( t \right)-e_{j}\left( 0 \right) \right)^{2}}} \right)dt} \right)} \\
\end{aligned}$$ is used to determine the optimal regulatory program. The factor *σ* models the costs for the whole pathway and not of single enzymes. In previous studies \[[@pcbi.1005371.ref014], [@pcbi.1005371.ref016]\] we discovered that varying synthesis costs within the pathway can influence the regulatory strategy, but is neglected here to focus on the impact of toxic intermediates.
The model consists of several parameters with arbitrary units ([Table 1](#pcbi.1005371.t001){ref-type="table"}). The time-dependent concentrations of the five enzymes are used as control variables during the optimization. The simulation time span is set to 30 arbitrary time units and has no influence on optimization results (see also \[[@pcbi.1005371.ref015]\]). Parameters connected to toxicity (*β*) and kinetics (*k*~*cat*~,*K*~*m*~) are tested for their influence on optimal regulatory programs for two values of *σ* corresponding to high and low enzyme costs, respectively. To test the influence of toxicity and kinetic parameters, we performed 1000 runs in which their values were chosen randomly from the intervals indicated in [Table 1](#pcbi.1005371.t001){ref-type="table"}.
10.1371/journal.pcbi.1005371.t001
###### Parameter and variable overview.
![](pcbi.1005371.t001){#pcbi.1005371.t001g}
Factor Value Description
---------------- --------------------------------- ------------------------------------------
*e*~*j*~ \[0,∞\] enzyme concentration (control variable)
*σ* $\frac{1}{3}$ or $\frac{1}{30}$ weight of enzyme cost
*T*~*max*~ 30 timespan of optimization
*k*~*cat*,*j*~ \[0,2\]\* turnover number
*K*~*m*,*j*~ \[0,2\]\* inverse substrate affinity
*β*~*i*~ \[0,4\]\* threshold of intermediate concentrations
A star \* indicates that the parameters are sampled from the indicated range.
Dynamic optimization and analysis of simulation results {#sec004}
-------------------------------------------------------
The above described optimization problem has time dependent and continuous decision (*e*(*t*)) and state variables (metabolite concentrations), and hence has to be solved using dynamic optimization. Similar to previous works \[[@pcbi.1005371.ref010], [@pcbi.1005371.ref014], [@pcbi.1005371.ref016]\], we used a quasi-sequential approach with the extensions of \[[@pcbi.1005371.ref022]\] to handle approximation errors and moving finite elements. Since this method is gradient-based, the optimization was repeated one hundred times for each random parameter set using random initializations to avoid local optima. For each random parameter set, we only considered the solution with the best objective function value.
To investigate the influence of kinetic and toxicity parameters on optimal regulatory programs, 1000 randomly distributed parameter samples were obtained as Sobol-sequences \[[@pcbi.1005371.ref023]\], which are values quasi-randomly distributed in the parameter space. In contrast to randomly chosen parameters sets, values obtained from a Sobol-sequence are maximizing the distance to each other in the parameter space. This leads to an optimal coverage of the parameter space and therefore less parameter tests are needed.
We are specifically interested in the relationship between regulatory effort, measured as deviation of enzyme time-courses from initial enzyme concentrations, and toxicity as well as kinetic parameters. To investigate this relationship, we partitioned the randomized parameters sets depending on the amount of regulatory effort at the individual pathway positions. Subsequently, we compared the distribution of toxicity parameters *β* and enzyme efficiency $k_{\textit |
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] | have some bad news about your car." "Which one?" "I think it's best you see for yourself." "I'll let them know we're on our way." "What do you know about this guy?" "He's another c.I." "Peter leans on him when neal's not around." "Really?" "We should talk to him." "No, let's let it play out." "Let's see where he leads us." "So, Thursday has a view, huh?" "To say Thursday has a view... is like saying the hubble telespe... has a magnifying glass." "It ithe most spectacular cityscape... our fair city has to offer." "Really?" "I have to go now." "Ay." "Are you gonna watch "moon landing..." "factr fiction?"..." "No." "Oh." "Then it leaves with me." "It was nice to see you, Mrs. suit." "Nice to see you, too, moz." "Thanks for the vino." "It was neal's." "Looks like our c.I. 'S left the building." "You want us to follow him or stay here?" "We should follow him." "We do both." "My guys stay and monitor the house." "We'll follow the miniature Caffrey." "I guess it's "put your c.I. To work" day." "This way." "Pretend you know me." "Give me a reason." "I've given your sweetheart safe passage, and in about two minutes, we're gonna clear his name." "They got to be talking about Burke and Franklin." "Excuse me, guys, I got to take this." "This is neal." "Hey." "Are you with deckard?" "Yeah, yeah." "Are you there, sir?" "I am." "Who that?" "Tell him it's bancroft." "It's bancroft." "Diana should be with stan volker." "Yeah." "Uh-huh." "That's correct." "Give me the phone." "Look, I think deckard wants to speak with you." "Caffrey, you still there?" "Did you lose him?" "It's deckard, sir." "Deckard?" "Put Caffrey back on." "Yes, sir." "I'm back." "Okay." "Have you ever run a prisoner's dilemma?" "I've been in one." "Well, now's your chance to run it on a U.S. marshal." "You clear on what to do?" "Crystal clear." "Thank you, sir." "What's going on?" "Just follow my lead." "$tan 1 was stolen earlier today... and apparently taken on quite a joy ride." "They thought they could bring it here for parts." "What are they doing in there?" "I think you know what they're doing in there." "That's it." "Who did this?" "We have them in custody, Mr. volker." "Don't worry." "Well, they're gonna pay." "Just need your signature..." "If you could read over this statement for me." "I think you'll find everything's in order." "Volker's cutting a deal with the FBI." "He's selling you out." "Yep, there it is..." "a signed confession." "See, once you showed up at volker's office, he knew it was only a matter of ti before you turned on him." "He made a deal." "Come on, deckard." "Don't make it harder than it is." "There is one more thing." "What is this all about?" "Deckard says you paid off an FBI Agent... to intimidate and kill witnesses in federal trials." "You're looking at 25 to life..." "Unless you start talking." "Deckard came up with the whole thing." "What do you want to know about him?" "Give it up." "Stay back!" "Hands up, deckard!" "It's over." "Put the gun down on the ground!" "You realize the grief you caused me with bancroft... when you fled?" "Being on this side of a manhunt... isn't all peaches and dandelions, is it?" "Beats being led away in cuffs." "Good working with you, deckard." "How'd it go?" "They've cleared Franklin of all charges." "Bancroft's reinstating him into the white collar division." "That's great work, everyone." "Let's get some rest." "Neal..." "Good work." "Thank you." "I'll be headed home, too." "Won't be going to the Powell today, 'cause, you know, it's outside my radius." "Please?" "Thanks for playing last night, moz." "Oh, the follow-the-leader stuff?" "That was child's play." "The reason I'm calling now... is because I've obtained some information since then." "What'd you find?" "I know who Kate called from the plane." "It was the FBI Agent who set up the deal between you and Kate." "Garrett Fowler." "Now I need to find him." "Caffrey!" "I'll get right back to you." "Sir." "Come here." "You planning on seeing this?" "Well, yeah, I was, but, you know, it's out of my range and..." "Uh-huh." "I've wanted to see this thing since it opened." "Let's go." "You know, from the moment I met you," "I could tell you're a man of refined tastes." "Oh, I know, hon." "I'll be home. 15 minutes, tops." "Oh, I can't wait, either." "Just a few hours sleep, and I'll be yours all day..." "And night."My First (and perhaps only) Blogging Award!
My friend Susie has nominated me for the Very Inspiring Blogger Award. She is so sweet. Her blog is SusieShy45, and it always a joy to read: so please check it out. This is my second blogging host site, and I love WordPress: I have made so many friends and found so many blogs to read some days I can’t keep up.
I started a blog because I want to write. Good reason, right? What I have found a vivid community of bloggers that I never expected. They are supportive, funny and very necessary to me. I thank everyone who reads my blog, and hope that I will continue to interest, entertain or inspire you (whatever it takes to keep you reading 😉 )
There are some rules I need to follow to accept this award, the first was taken care of by thanking Susie (which I would have done anyway). The second is that I need to list the rules and display the award (as if I wouldn’t!), the third is share seven facts about myself (forthcoming) and the fourth is that I nominate 15 fellow bloggers for the award (also forthcoming).
Seven Facts about me:
1) In my 20s I was terribly depressed that I would never write anything to measure up to War & Peace
2) I have an odd combination of OCD and ADD, which makes me both compulsive and a scatterbrain ( I am not diagnosed, just have certain tendencies that way that have been generally noticed by most people I come in contact with)
3) I love anything that has four legs and a tail, and, fortunately, they love me back
4) I am currently unemployed and working to get a certification in Medical Admin Assistant to find a grown-up career
5) I really enjoy helping people
6) I was a farmer
7) I no longer care if I write War & Peace, I happy to write on my two blogs and see what comes
I chose my bloggers for different reasons. Some are inspiring for the quality and quantity they write. It can be difficult to post several times a week and still be interesting and relevant. These bloggers do that. Some I picked for what they write. Opening a vein and spilling your life’s blood for the world to see is hard, and it is inspiring how unflinchingly these bloggers do that.Q:
Loading images in array Java
Which is the best way to store images in an array? I'm using ImageIO and I now wish to store them in an array.
A:
PixelGrabber is what you want.
Image img = null;
try {
img = ImageIO.read(new File("img.png"));
} catch (IOException e) {
e.printStackTrace();
}
int w = img.getWidth(null);
int h = img.getHeight(null);
int[] pixels = new int[w * h];
PixelGrabber pg = new PixelGrabber(img, 0, 0, w, h, pixels, 0, w);
try {
pg.grabPixels();
} catch (InterruptedException e) {
System.err.println("interrupted waiting for pixels!");
return;
}
if ((pg.getStatus() & ImageObserver.ABORT) != 0) {
System.err.println("image fetch aborted or errored");
return;
}
for (int i = 0; i < pixels.length; i++)
System.out.println(pixels[i]);
A:
If you read the whole images in memory, you can |
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] | -09-00012}
-----------------------------------------------
### 5.2.1. Deep Learning Used for Antibody Lead Optimization {#sec5dot2dot1-antibodies-09-00012}
A successful application of deep learning within the domain of interaction prediction comes from a sequence-based approach proposed to optimize an existing antibody candidate by Mason et al. \[[@B35-antibodies-09-00012]\]. Rather than using a public domain dataset, the authors generate a relatively small number of variants (5 × 10^4^) of an existing therapeutic antibody by introducing mutations to the H3 regions of the CDR and screening the variants for binding against a target antigen. The H3 sequences, labeled as binding or non-binding, were used as input to long-term-short-term recurrent neural networks and convolutional neural networks which were trained to predict the binding label of the sequences. Trained networks were then used to filter a computationally generated set of 7.2 × 10^7^ candidate sequences to 3.1 × 10^6^ predicted binders. Experimental testing showed that 30 out of 30 randomly selected predicted binding sequences bound specifically to the target antigen, with one of the thirty exhibiting a three-fold increase in affinity.
The significance of this method is highlighted by the comparative analysis with a structure-based approach. The authors demonstrate that the number of new binding sequence suggestions generated by the structural modeling software is orders of magnitude smaller than the actual expected binding sequence space and that the free energy estimation of the modelled structures could not be used as a reliable classifier of binding activity.
While structure-based methods have the potential to represent richer features for a given input, sequence-based methods benefit from more data availability due to developed experimental methods such as next-generation sequencing.
### 5.2.2. Ens-Grad {#sec5dot2dot2-antibodies-09-00012}
Another sequence optimization algorithm, Ens-Grad, uses an ensemble of neural networks and gradient ascent to optimize an H3 seed sequence \[[@B36-antibodies-09-00012]\]. Briefly, Liu et al. report training an ensemble of six neural networks (five convolutional) using experimental phage display data generated from panning experiments. Panning experiments approximate enrichment in binding affinity by subjecting a set of H3 sequences bound to phages to a binding competition where non-binders are washed away and binders are kept for subsequent rounds \[[@B37-antibodies-09-00012]\]. Several different models with varying architectures were trained using either a means squared error loss for regression of enrichment, or a binary cross entropy for classification of H3 CDRs that were successively enriched in rounds of panning.
After fitting the ensemble of neural networks, the authors use gradient ascent to optimize input seed sequences. Contrary to gradient descent, which is generally used to modify neural network weights so as to minimize a loss function such as classification error, gradient ascent is used in this case to modify the input sequence so as to maximize the output. The authors suggest that the use of an ensemble of several neural networks allows for optimization to take controlled paths by optimizing with respect to different network outputs.
Using this optimization protocol, the authors were able to generate sequences with greater enrichment than both seed sequences and sequences within the training dataset. This significant result suggests that the neural network models were able to extrapolate beyond input training data, possibly by learning high level representations of what determines enriched binding.
Additionally, the authors demonstrate superior performance using a gradient ascent method compared to more common generative models such as variational auto-encoders and genetic algorithms. However, it is unclear whether or not the difference between these methods is attributable to the style of optimization, or to the difference in the architecture of the network (e.g., number of layers or layer sizes).
Similar to the method developed by Mason et al. \[[@B35-antibodies-09-00012]\], this method completely circumvents the need for structural data which is significantly more difficult to acquire. However, it is highly unlikely that these methods generalize well across target antigens. In each method the network is fit to data points derived from a single target antigen and therefore applying this method to a different target would require extensive wet-lab testing to generate the training data and refit the model.
### 5.2.3. DeepInterface {#sec5dot2dot3-antibodies-09-00012}
DeepInterface is a structure-based method which aims to classify protein complexes in their docked conformational state as either true or false binders \[[@B38-antibodies-09-00012]\]. The input to the network is a voxel grid constructed from a fixed-size box placed around the interface. To handle rotation ambiguity, the authors align the vector between the structures center of mass to one of the three coordinate axes. The network itself is composed of four convolutional layers followed by batch normalization and rectified linear units. To transform the voxel space into a one-dimensional vector and subsequently into a prediction of binding, global average pooling is applied to the voxel space followed by two fully connected layers.
Of note here is the generation of negative data used to train the network. Negative examples in this case refer to any structures which are not true binders. Using negative examples is a vital step in classification, as the network must be exposed to some form of negative input for successful training. To generate these negative examples, the authors use a fast-Fourier transform (FFT)-based docking algorithm, ZDOCK, to select incorrect docking solutions from the set of sampled conformations \[[@B13-antibodies-09-00012]\].
Representation of the protein interface as a voxel grid is an intuitive yet problematic strategy. Firstly, the input size of the network restricts the voxel space to a single size. The authors overcome this by limiting the size of the interfaces passed into the network to those small enough to fit into the bounded grid space. Secondly, a rotational ambiguity problem arises due to the absence of a common axis across all interfaces. Similar voxel methods used for 3D objects can usually take advantage of an implied gravity vector to eliminate ambiguity across an axis. Handling ambiguity between the remaining axes can be done using a randomly rotated version of the input or by implementing rotational pooling into the network architecture. However, these methods are impractical for more than two dimensions as the number of possible rotations needed grows exponentially. Despite these limitations, DeepInterface achieves 75% classification accuracy on benchmark datasets, demonstrating viability in this classification task
Due to the differences previously mentioned between antibody-antigen interfaces and general PPIs, it is not clear that the model here presented would be capable of avoiding false positive classification. This problem may give rise to out-of-distribution errors, which arise when the underlying training dataset is not representative of its real-world use case. However, except for the bounding voxel space size, the model architecture and input structure presented within DeepInterface is somewhat agnostic to the type of interface evaluated. It should be noted, however, that the model's reliance on spatial arrangement of the interface area should not hinder its applicability towards antibody--antigen interfaces, which possess non-discernable differences in shape complementarity \[[@B6-antibodies-09-00012]\].
### 5.2.4. MaSIF-Search {#sec5dot2dot4-antibodies-09-00012}
The MaSIF approach comes out of the growing field of geometric deep learning. Starting from a mesh representation of a protein surface, a patch is created by selecting a point on the mesh and all neighboring surface points within a defined geodesic distance \[[@B39-antibodies-09-00012]\]. Each of the surface points is annotated with geometric and chemical features which describe degrees of curvature, concavity, electrostatic potential, hydrophobicity and hydrogen bond potential. The patch is down-sampled into a grid of 80 bins (5 radial × 16 angular). Each bin contains the statistical mean of the feature attributes of the points which are assigned to the corresponding bin. The 80 bins, indexed by polar and angular coordinates, are passed as input into a set of geodesic convolutional filters to generate a one-dimensional descriptor of the protein surface. Rotational max-pooling is used to overcome angular ambiguity. The one-dimensional descriptor is then refined by a fully connected layer. The remaining architecture is regarded as application-specific, which expresses the ability to use the 1D descriptors as input into an application specific model.
To train an application specific model for interaction prediction, a modified version of the triplet loss function is used, which minimizes the Euclidean distance between the 1D descriptors of an anchor (a binding protein patch) and a positive (a complimentary patch to the anchor), and maximizes the distance between the anchor and a negative (a randomly chosen, non-complementary surface patch to the anchor). The authors deem two surface patches from two separate proteins to be positive pairs if the patch centers are within a small distance from one another at the protein--protein interface.
To measure the model's performance, the authors classify interacting vs. non-interacting pairs and report an area under the curve of the receiver operating characteristic (ROC AUC) of 0.99 when using geometric and chemical features. The authors further evaluate the model's performance on different subsets of the data by creating subsets of low, high and very high interface complementarity. It is interesting to note that, as expected, the classification performance of the model drops to 0.81 ROC AUC using both geometric and chemical feature sets on the low shape complementary subset and further to 0.73 and 0.75 when using only the geometric and chemical features on this subset, respectively.
MaSIF search is trained on a mix of antibody--antigen and protein--protein interfaces with no distinction between the two. As mentioned previously, antibody--antigen interactions exhibit a similar shape complementarity to that of other protein--protein interfaces \[[@B6-antibodies-09-00012]\]. |
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] | country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TEX').property('city', 'Telluride').property('state', 'CO').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YXT').property('city', 'Terrace').property('state', 'BC').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TXK').property('city', 'Texarkana').property('state', 'AR').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TVF').property('city', 'Thief River Falls').property('state', 'MN').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YTH').property('city', 'Thompson').property('state', 'MB').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YQT').property('city', 'Thunder Bay').property('state', 'ON').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YTS').property('city', 'Timmins').property('state', 'ON').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'OOK').property('city', 'Toksook Bay').property('state', 'AK').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TOL').property('city', 'Toledo').property('state', 'OH').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TOP').property('city', 'Eureka').property('state', 'KS').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YYZ').property('city', 'Toronto').property('state', 'ON').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YTZ').property('city', 'Toronto').property('state', 'ON').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YTO').property('city', 'Toronto').property('state', 'ON').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TVC').property('city', 'Traverse City').property('state', 'MI').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TTN').property('city', 'Trenton').property('state', 'NJ').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TRI').property('city', 'Tri-City Airport').property('state', 'TN').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TUS').property('city', 'Tucson').property('state', 'AZ').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TUL').property('city', 'Tulsa').property('state', 'OK').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TUP').property('city', 'Tupelo').property('state', 'MS').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TWF').property('city', 'Twin Falls').property('state', 'ID').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'TYR').property('city', 'Tyler').property('state', 'TX').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'UNK').property('city', 'Unalakleet').property('state', 'AK').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'EGE').property('city', 'Vail').property('state', 'CO').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YVO').property('city', 'Val-d'Or').property('state', 'PQ').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'VDZ').property('city', 'Valdez').property('state', 'AK').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'VLD').property('city', 'Valdosta').property('state', 'GA').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YVR').property('city', 'Vancouver').property('state', 'BC').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YYJ').property('city', 'Victoria').property('state', 'BC').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'VCT').property('city', 'Victoria').property('state', 'TX').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'VIS').property('city', 'Visalia').property('state', 'CA').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'YWK').property('city', 'Wabush, Labrador').property('state', 'NF').property('country', 'Canada').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'ACT').property('city', 'Waco').property('state', 'TX').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'ALW').property('city', 'Walla Walla').property('state', 'WA').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'IAD').property('city', 'Washington DC').property('state', 'DC').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'DCA').property('city', 'Washington DC').property('state', 'DC').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'WAS').property('city', 'Washington DC').property('state', 'DC').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'ALO').property('city', 'Waterloo').property('state', 'IA').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'ART').property('city', 'Watertown').property('state', 'NY').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'ATY').property('city', 'Watertown').property('state', 'SD').property('country', 'USA').property('dst', 'ZZZ')
:> g.addV('airport').property('iata', 'CWA').property('city', 'Wausau').property('state', 'WI').property('country', 'USA').property('dst', 'ZZZ')
:> |
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