nils-herrmann/clean_scirepeval huggingface dataset:
Rewiring yeast acetate metabolism through MPC1 loss
of function leads to mitochondrial damage and decreases chronological
lifespan

During growth on fermentable substrates, such as glucose, pyruvate, which is the end-product of glycolysis, can be used to generate acetyl-CoA in the cytosol via acetaldehyde and acetate, or in mitochondria by direct oxidative decarboxylation. In the latter case, the mitochondrial pyruvate carrier (MPC) is responsible for pyruvate transport into mitochondrial matrix space. During chronological aging, yeast cells which lack the major structural subunit Mpc1 display a reduced lifespan accompanied by an age-dependent loss of autophagy. Here, we show that the impairment of pyruvate import into mitochondria linked to Mpc1 loss is compensated by a flux redirection of TCA cycle intermediates through the malic enzyme-dependent alternative route. In such a way, the TCA cycle operates in a "branched" fashion to generate pyruvate and is depleted of intermediates. Mutant cells cope with this depletion by increasing the activity of glyoxylate cycle and of the pathway which provides the nucleocytosolic acetyl-CoA. Moreover, cellular respiration decreases and ROS accumulate in the mitochondria which, in turn, undergo severe damage. These acquired traits in concert with the reduced autophagy restrict cell survival of the mpc1∆ mutant during chronological aging. Conversely, the activation of the carnitine shuttle by supplying acetyl-CoA to the mitochondria is sufficient to abrogate the short-lived phenotype of the mutant.
Lust, love, and life: a qualitative study of Swedish adolescents' perceptions and experiences with pornography.

There is a widespread concern in Western society about the visibility of pornography in public places and on the Internet. What are the consequences for young men and women, and how do they think about gender, sexuality, and pornography? Data was collected, through 22 individual interviews and seven focus groups, from 51 participants (36 women and 37 men aged 14-20 years) in Sweden. The results indicated a process of both normalization and ambivalence. Pornography was used as a form of social intercourse, a source of information, and a stimulus for sexual arousal. Pornography consumption was more common among the young men than among the women. For both the young men and women, the pornographic script functioned as a frame of reference in relation to bodily ideals and sexual performances. Most of the participants had acquired the necessary skills of how to deal with the exposure to pornography in a sensible and reflective manner.
Increasing the level of abstraction in FPGA-based designs

Traditional design techniques for FPGAs are based on using hardware description languages, with functional and post-place-and-route simulation as a means to check design correctness and remove detected errors. With large complexity of things to be designed it is necessary to introduce new design approaches that will increase the level of abstraction while maintaining the necessary efficiency of a computation performed in hardware that we are used to today. This paper presents one such methodology that builds upon existing research in multithreading, object composability and encapsulation, partial runtime reconfiguration, and self adaptation. The methodology is based on currently available FPGA design tools. The efficiency of the methodology is evaluated on basic vector and matrix operations.
Sex, clinical presentation, and outcome in patients with acute coronary syndromes. Global Use of Strategies to Open Occluded Coronary Arteries in Acute Coronary Syndromes IIb Investigators.

Studies have reported that women with acute myocardial infarction have in-hospital and long-term outcomes that are worse than those of men.



To assess sex-based differences in presentation and outcome, we examined data from the Global Use of Strategies to Open Occluded Coronary Arteries in Acute Coronary Syndromes IIb study, which enrolled 12,142 patients (3662 women and 8480 men) with acute coronary syndromes, including infarction with ST-segment elevation, infarction with no ST-segment elevation, and unstable angina.



Overall, the women were older than the men, and had significantly higher rates of diabetes, hypertension, and prior congestive heart failure. They had significantly lower rates of prior myocardial infarction and were less likely ever to have smoked. A smaller percentage of women than men had infarction with ST elevation (27.2 percent vs. 37.0 percent, P<0.001), and of the patients who presented with no ST elevation (those with myocardial infarction or unstable angina), fewer women than men had myocardial infarction (36.6 percent vs. 47.6 percent, P<0.001). Women had more complications than men during hospitalization and a higher mortality rate at 30 days (6.0 percent vs. 4.0 percent, P<0.001) but had similar rates of reinfarction at 30 days after presentation. However, there was a significant interaction between sex and the type of coronary syndrome at presentation (P=0.001). After stratification according to coronary syndrome and adjustment for base-line variables, there was a nonsignificant trend toward an increased risk of death or reinfarction among women as compared with men only in the group with infarction and ST elevation (odds ratio, 1.27; 95 percent confidence interval, 0.98 to 1.63; P=0.07). Among patients with unstable angina, female sex was associated with an independent protective effect (odds ratio for infarction or death, 0.65; 95 percent confidence interval, 0.49 to 0.87; P=0.003).



Women and men with acute coronary syndromes had different clinical profiles, presentation, and outcomes. These differences could not be entirely accounted for by differences in base-line characteristics and may reflect pathophysiologic and anatomical differences between men and women.
Perforinopathy: A Spectrum of Human Immune Disease Caused by Defective Perforin Delivery or Function

Congenital perforin deficiency is considered a rare cause of human immunopathology and immune dysregulation, and classically presents as a fatal illness early in infancy. However, we propose that a group of related disorders in which killer lymphocytes deliver only partially active perforin or a reduced quantum of wild-type perforin to the immune synapse should be considered part of an extended syndrome with overlapping but more variable clinical features. Apart from the many rare mutations scattered over the coding sequences, up to 10% of Caucasians carry the severely hypomorphic PRF1 allele C272 > T (leading to A91V mutation) and the overall prevalence of the homozygous state for A91V is around 1 in 600 individuals. We therefore postulate that the partial loss of perforin function and its clinical consequences may be more common then currently suspected. An acute clinical presentation is infrequent in A91V heterozygous individuals, but we postulate that the partial loss of perforin function may potentially be manifested in childhood or early adulthood as "idiopathic" inflammatory disease, or through increased cancer susceptibility - either hematological malignancy or multiple, independent primary cancers. We suggest the new term "perforinopathy" to signify the common functional endpoints of all the known consequences of perforin deficiency and failure to deliver fully functional perforin.
Multi-Scale Structure-Aware Network for Human Pose Estimation

We develop a robust multi-scale structure-aware neural network for human pose estimation. This method improves the recent deep conv-deconv hourglass models with four key improvements: (1) multiscale supervision to strengthen contextual feature learning in matching body keypoints by combining feature heatmaps across scales, (2) multiscale regression network at the end to globally optimize the structural matching of the multi-scale features, (3) structure-aware loss used in the intermediate supervision and at the regression to improve the matching of keypoints and respective neighbors to infer a higher-order matching configurations, and (4) a keypoint masking training scheme that can effectively fine-tune our network to robustly localize occluded keypoints via adjacent matches. Our method can effectively improve state-of-theart pose estimation methods that suffer from difficulties in scale varieties, occlusions, and complex multi-person scenarios. This multi-scale supervision tightly integrates with the regression network to effectively (i) localize keypoints using the ensemble of multi-scale features, and (ii) infer global pose configuration by maximizing structural consistencies across multiple keypoints and scales. The keypoint masking training enhances these advantages to focus learning on hard occlusion samples. Our method achieves the leading position in the MPII challenge leaderboard among the state-of-the-art methods.
Reinforcement learning of motor skills in high dimensions: A path integral approach

Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far due to the computational difficulties that reinforcement learning encounters in high dimensional continuous state-action spaces. In this paper, we derive a novel approach to RL for parameterized control policies based on the framework of stochastic optimal control with path integrals. While solidly grounded in optimal control theory and estimation theory, the update equations for learning are surprisingly simple and have no danger of numerical instabilities as neither matrix inversions nor gradient learning rates are required. Empirical evaluations demonstrate significant performance improvements over gradient-based policy learning and scalability to high-dimensional control problems. Finally, a learning experiment on a robot dog illustrates the functionality of our algorithm in a real-world scenario. We believe that our new algorithm, Policy Improvement with Path Integrals (PI2), offers currently one of the most efficient, numerically robust, and easy to implement algorithms for RL in robotics.
Interactive partner control in close interactions for real-time applications

This article presents a new framework for synthesizing motion of a virtual character in response to the actions performed by a user-controlled character in real time. In particular, the proposed method can handle scenes in which the characters are closely interacting with each other such as those in partner dancing and fighting. In such interactions, coordinating the virtual characters with the human player automatically is extremely difficult because the system has to predict the intention of the player character. In addition, the style variations from different users affect the accuracy in recognizing the movements of the player character when determining the responses of the virtual character. To solve these problems, our framework makes use of the spatial relationship-based representation of the body parts called interaction mesh, which has been proven effective for motion adaptation. The method is computationally efficient, enabling real-time character control for interactive applications. We demonstrate its effectiveness and versatility in synthesizing a wide variety of motions with close interactions.
Corrigendum: Algorithm 806: SPRNG: a scalable library for pseudorandom number generation

In this article we present background, rationale, and a description of the Scalable Parallel Random Number Generators (SPRNG) library. We begin by presenting some methods for parallel pseudorandom number generation. We will focus on methods based on parameterization, meaning that we will not consider splitting methods such as the leap-frog or blocking methods. We describe, in detail, parameterized versions of the following pseudorandom number generators: (1) linear congruential generators, (ii) shift-register generators, and (iii) lagged-Fibonacci generators. We briefly describe the methods, detail some advantages and disadvantages of each method, and recount results from number theory that impact our understanding of their quality of parallel applications. SPRNG was designed around the uniform implementation of different families of parameterized random number generators. We then present a short description of SPRNG. The description contained within this document is meant only to outline the rationale behind and the capabilities of SPRNG. Much more information, including examples and detailed documentation aimed at helping users with putting and using SPRNG on scalable systems is available at http://sprng.cs.fsu.edu. In this description of SPRNG we discuss the random-number generator library as well as the suite of tests of randomness that is an integral part of SPRNG. Random-number tools for parallel Monte Carlo applications must be subjected to classical as well as new types of empirical tests of randomness to eliminate generators that show defects when used in scalable environments.
Omega-3 fatty acids, lipids, and apoE lipidation in Alzheimer's disease: a rationale for multi-nutrient dementia prevention.

In the last decade, it has become obvious that Alzheimer's disease (AD) is closely linked to changes in lipids or lipid metabolism. One of the main pathological hallmarks of AD is amyloid-β (Aβ) deposition. Aβ is derived from sequential proteolytic processing of the amyloid precursor protein (APP). Interestingly, both, the APP and all APP secretases are transmembrane proteins that cleave APP close to and in the lipid bilayer. Moreover, apoE4 has been identified as the most prevalent genetic risk factor for AD. ApoE is the main lipoprotein in the brain, which has an abundant role in the transport of lipids and brain lipid metabolism. Several lipidomic approaches revealed changes in the lipid levels of cerebrospinal fluid or in post mortem AD brains. Here, we review the impact of apoE and lipids in AD, focusing on the major brain lipid classes, sphingomyelin, plasmalogens, gangliosides, sulfatides, DHA, and EPA, as well as on lipid signaling molecules, like ceramide and sphingosine-1-phosphate. As nutritional approaches showed limited beneficial effects in clinical studies, the opportunities of combining different supplements in multi-nutritional approaches are discussed and summarized.
Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Predicti

We propose strategies for selecting a good neural network architecture for modeling any spe-ciic data set. Our approach involves eeciently searching the space of possible architectures and selecting a \best" architecture based on estimates of generalization performance. Since an exhaustive search over the space of architectures is computationally infeasible, we propose heuristic strategies which dramatically reduce the search complexity. These employ directed search algorithms, including selecting the number of nodes via sequential network construction (SNC), sensitivity based pruning (SBP) of inputs, and optimal brain damage (OBD) pruning for weights. A selection criterion, the estimated generalization performance or prediction risk, is used to guide the heuristic search and to choose the nal network. Both predicted squared error (PSE) and nonlinear cross{validation (NCV) are used for estimating the prediction risk from the available data. We apply these heuristic search and prediction risk estimation techniques to the problem of predicting corporate bond ratings. This problem is very attractive as a case study, since it is characterized by a limited set of data and by the lack of a complete a priori model which could be used to impose a structure to the network architecture.
Robust, low-cost, non-intrusive sensing and recognition of seated postures

In this paper, we present a methodology for recognizing seatedpostures using data from pressure sensors installed on a chair.Information about seated postures could be used to help avoidadverse effects of sitting for long periods of time or to predictseated activities for a human-computer interface. Our system designdisplays accurate near-real-time classification performance on datafrom subjects on which the posture recognition system was nottrained by using a set of carefully designed, subject-invariantsignal features. By using a near-optimal sensor placement strategy,we keep the number of required sensors low thereby reducing costand computational complexity. We evaluated the performance of ourtechnology using a series of empirical methods including (1)cross-validation (classification accuracy of 87% for ten posturesusing data from 31 sensors), and (2) a physical deployment of oursystem (78% classification accuracy using data from 19sensors).
Mimir: an automatic reporting and reasoning system for deep learning based analysis in the medical domain

Automatic detection of diseases is a growing field of interest, and machine learning in form of deep learning neural networks are frequently explored as a potential tool for the medical video analysis. To both improve the "black box"-understanding and assist in the administrative duties of writing an examination report, we release an automated multimedia reporting software dissecting the neural network to learn the intermediate analysis steps, i.e., we are adding a new level of understanding and explainability by looking into the deep learning algorithms decision processes. The presented open-source software can be used for easy retrieval and reuse of data for automatic report generation, comparisons, teaching and research. As an example, we use live colonoscopy as a use case which is the gold standard examination of the large bowel, commonly performed for clinical and screening purposes. The added information has potentially a large value, and reuse of the data for the automatic reporting may potentially save the doctors large amounts of time.
Bypassing KLS: Gaussian Cooling and an O^*(n3) Volume Algorithm

We present an O*(n3) randomized algorithm for estimating the volume of a well-rounded convex body given by a membership oracle, improving on the previous best complexity of O*(n4). The new algorithmic ingredient is an accelerated cooling schedule where the rate of cooling increases with the temperature. Previously, the known approach for potentially achieving such complexity relied on a positive resolution of the KLS hyperplane conjecture, a central open problem in convex geometry.
Design and characterization of corporate feed rectangular microstrip patch array antenna

The modern mobile communication systems requires high gain, large bandwidth and minimal size antenna's that are capable of providing better performance over a wide range of frequency spectrum. This requirement leads to the design of Microstrip patch antenna. This paper proposes the design of 4-Element microstrip patch antenna array which uses the corporate feed technique for excitation. Low dielectric constant substrates are generally preferred for maximum radiation. Thus it prefers Taconic as a dielectric substrate. Desired patch antenna design is initially simulated by using high frequency simulation software SONNET and FEKO and patch antenna is designed as per requirements. Antenna dimensions such as Length (L), Width (W) and substrate Dielectric Constant (εr) and parameters like Return Loss, Gain and Impedance are calculated using high frequency simulation software. The antenna has been designed for the range 9-11 GHz. Hence this antenna is highly suitable for X-band applications.
Taming the 800 Pound Gorilla: The Rise and Decline of NTP DDoS Attacks

Distributed Denial of Service (DDoS) attacks based on Network Time Protocol (NTP) amplification, which became prominent in December 2013, have received significant global attention. We chronicle how this attack rapidly rose from obscurity to become the dominant large DDoS vector. Via the lens of five distinct datasets, we characterize the advent and evolution of these attacks. Through a dataset that measures a large fraction of global Internet traffic, we show a three order of magnitude rise in NTP. Using a large darknet, we observe a similar rise in global scanning activity, both malicious and research. We then dissect an active probing dataset, which reveals that the pool of amplifiers totaled 2.2M unique IPs and includes a small number of "mega amplifiers," servers that replied to a single tiny probe packet with gigabytes of data. This dataset also allows us, for the first time, to analyze global DDoS attack victims (including ports attacked) and incidents, where we show 437K unique IPs targeted with at least 3 trillion packets, totaling more than a petabyte. Finally, ISP datasets shed light on the local impact of these attacks. In aggregate, we show the magnitude of this major Internet threat, the community's response, and the effect of that response.
Perioperative risk factors in patients with liver disease undergoing non-hepatic surgery.

The patients with liver disease present for various surgical interventions. Surgery may lead to complications in a significant proportion of these patients. These complications may result in considerable morbidity and mortality. Preoperative assessment can predict survival to some extent in patients with liver disease undergoing surgical procedures. A review of literature suggests nature and the type of surgery in these patients determines the peri-operative morbidity and mortality. Optimization of premorbid factors may help to reduce perioperative mortality and morbidity. The purpose of this review is to discuss the effect of liver disease on perioperative outcome; to understand various risk scoring systems and their prognostic significance; to delineate different preoperative variables implicated in postoperative complications and morbidity; to establish the effect of nature and type of surgery on postoperative outcome in patients with liver disease and to discuss optimal anaesthesia strategy in patients with liver disease.
Catalyst: triggering collective action with thresholds

The web is a catalyst for drawing people together around shared goals, but many groups never reach critical mass. It can thus be risky to commit time or effort to a goal: participants show up only to discover that nobody else did, and organizers devote significant effort to causes that never get off the ground. Crowdfunding has lessened some of this risk by only calling in donations when an effort reaches a collective monetary goal. However, it leaves unsolved the harder problem of mobilizing effort, time and participation. We generalize the concept into activation thresholds, commitments that are conditioned on others' participation. With activation thresholds, supporters only need to show up for an event if enough other people commit as well. Catalyst is a platform that introduces activation thresholds for on-demand events. For more complex coordination needs, Catalyst also provides thresholds based on time or role (e.g., a bake sale requiring commitments for bakers, decorators, and sellers). In a multi-month field deployment, Catalyst helped users organize events including food bank volunteering, on-demand study groups, and mass participation events like a human chess game. Our results suggest that activation thresholds can indeed catalyze a large class of new collective efforts.