nils-herrmann/clean_scirepeval huggingface dataset:
Robust Recovery of Subspace Structures by Low-Rank Representation

In this paper, we address the subspace clustering problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to cluster the samples into their respective subspaces and remove possible outliers as well. To this end, we propose a novel objective function named Low-Rank Representation (LRR), which seeks the lowest rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary. It is shown that the convex program associated with LRR solves the subspace clustering problem in the following sense: When the data is clean, we prove that LRR exactly recovers the true subspace structures; when the data are contaminated by outliers, we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for data corrupted by arbitrary sparse errors, LRR can also approximately recover the row space with theoretical guarantees. Since the subspace membership is provably determined by the row space, these further imply that LRR can perform robust subspace clustering and error correction in an efficient and effective way.
A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies

This study examines the use of deep convolutional neural network in the classification of rice plants according to health status based on images of its leaves. A three-class classifier was implemented representing normal, unhealthy, and snailinfested plants via transfer learning from an AlexNet deep network. The network achieved an accuracy of 91.23%, using stochastic gradient descent with mini batch size of thirty (30) and initial learning rate of 0.0001. Six hundred (600) images of rice plants representing the classes were used in the training. The training and testing dataset-images were captured from rice fields around the district and validated by technicians in the field of agriculture. Deep neural network; convolutional neural network; rice; transfer learning; AlexNet
A Survey on Financial Applications of Metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.
A Review of Traditional and Novel Treatments for Seizures in Autism Spectrum Disorder: Findings from a Systematic Review and Expert Panel

Despite the fact that seizures are commonly associated with autism spectrum disorder (ASD), the effectiveness of treatments for seizures has not been well studied in individuals with ASD. This manuscript reviews both traditional and novel treatments for seizures associated with ASD. Studies were selected by systematically searching major electronic databases and by a panel of experts that treat ASD individuals. Only a few anti-epileptic drugs (AEDs) have undergone carefully controlled trials in ASD, but these trials examined outcomes other than seizures. Several lines of evidence point to valproate, lamotrigine, and levetiracetam as the most effective and tolerable AEDs for individuals with ASD. Limited evidence supports the use of traditional non-AED treatments, such as the ketogenic and modified Atkins diet, multiple subpial transections, immunomodulation, and neurofeedback treatments. Although specific treatments may be more appropriate for specific genetic and metabolic syndromes associated with ASD and seizures, there are few studies which have documented the effectiveness of treatments for seizures for specific syndromes. Limited evidence supports l-carnitine, multivitamins, and N-acetyl-l-cysteine in mitochondrial disease and dysfunction, folinic acid in cerebral folate abnormalities and early treatment with vigabatrin in tuberous sclerosis complex. Finally, there is limited evidence for a number of novel treatments, particularly magnesium with pyridoxine, omega-3 fatty acids, the gluten-free casein-free diet, and low-frequency repetitive transcranial magnetic simulation. Zinc and l-carnosine are potential novel treatments supported by basic research but not clinical studies. This review demonstrates the wide variety of treatments used to treat seizures in individuals with ASD as well as the striking lack of clinical trials performed to support the use of these treatments. Additional studies concerning these treatments for controlling seizures in individuals with ASD are warranted.
Learning with Average Top-k Loss

In this work, we introduce the average top-k (ATk) loss as a new aggregate loss for supervised learning, which is the average over the k largest individual losses over a training dataset. We show that the ATk loss is a natural generalization of the two widely used aggregate losses, namely the average loss and the maximum loss, but can combine their advantages and mitigate their drawbacks to better adapt to different data distributions. Furthermore, it remains a convex function over all individual losses, which can lead to convex optimization problems that can be solved effectively with conventional gradient-based methods. We provide an intuitive interpretation of the ATk loss based on its equivalent effect on the continuous individual loss functions, suggesting that it can reduce the penalty on correctly classified data. We further give a learning theory analysis of MATk learning on the classification calibration of the ATk loss and the error bounds of ATk-SVM. We demonstrate the applicability of minimum average top-k learning for binary classification and regression using synthetic and real datasets.
Multi-task Self-Supervised Visual Learning

We investigate methods for combining multiple selfsupervised tasks—i.e., supervised tasks where data can be collected without manual labeling—in order to train a single visual representation. First, we provide an apples-toapples comparison of four different self-supervised tasks using the very deep ResNet-101 architecture. We then combine tasks to jointly train a network. We also explore lasso regularization to encourage the network to factorize the information in its representation, and methods for “harmonizing” network inputs in order to learn a more unified representation. We evaluate all methods on ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our results show that deeper networks work better, and that combining tasks—even via a na¨ýve multihead architecture—always improves performance. Our best joint network nearly matches the PASCAL performance of a model pre-trained on ImageNet classification, and matches the ImageNet network on NYU depth prediction.
Cognitive deficits in depression: possible implications for functional neuropathology.

While depression is known to involve a disturbance of mood, movement and cognition, its associated cognitive deficits are frequently viewed as simple epiphenomena of the disorder.



To review the status of cognitive deficits in depression and their putative neurobiological underpinnings.



Selective computerised review of the literature examining cognitive deficits in depression and their brain correlates.



Recent studies report both mnemonic deficits and the presence of executive impairment--possibly selective for set-shifting tasks--in depression. Many studies suggest that these occur independent of age, depression severity and subtype, task 'difficulty', motivation and response bias: some persist upon clinical 'recovery'.



Mnemonic and executive deficits do no appear to be epiphenomena of depressive disorder. A focus on the interactions between motivation, affect and cognitive function may allow greater understanding of the interplay between key aspects of the dorsal and ventral aspects of the prefrontal cortex in depression.
Autophagy and mitophagy in the context of doxorubicin-induced cardiotoxicity

Doxorubicin (Dox) is a cytotoxic drug widely incorporated in various chemotherapy protocols. Severe side effects such as cardiotoxicity, however, limit Dox application. Mechanisms by which Dox promotes cardiac damage and cardiomyocyte cell death have been investigated extensively, but a definitive picture has yet to emerge. Autophagy, regarded generally as a protective mechanism that maintains cell viability by recycling unwanted and damaged cellular constituents, is nevertheless subject to dysregulation having detrimental effects for the cell. Autophagic cell death has been described, and has been proposed to contribute to Dox-cardiotoxicity. Additionally, mitophagy, autophagic removal of damaged mitochondria, is affected by Dox in a manner contributing to toxicity. Here we will review Dox-induced cardiotoxicity and cell death in the broad context of the autophagy and mitophagy processes.
Motion reconstruction using sparse accelerometer data

The development of methods and tools for the generation of visually appealing motion sequences using prerecorded motion capture data has become an important research area in computer animation. In particular, data-driven approaches have been used for reconstructing high-dimensional motion sequences from low-dimensional control signals. In this article, we contribute to this strand of research by introducing a novel framework for generating full-body animations controlled by only four 3D accelerometers that are attached to the extremities of a human actor. Our approach relies on a knowledge base that consists of a large number of motion clips obtained from marker-based motion capturing. Based on the sparse accelerometer input a cross-domain retrieval procedure is applied to build up a lazy neighborhood graph in an online fashion. This graph structure points to suitable motion fragments in the knowledge base, which are then used in the reconstruction step. Supported by a kd-tree index structure, our procedure scales to even large datasets consisting of millions of frames. Our combined approach allows for reconstructing visually plausible continuous motion streams, even in the presence of moderate tempo variations which may not be directly reflected by the given knowledge base.
Seizure characteristics in Pallister-Killian syndrome.

Pallister-Killian syndrome (PKS) is a congenital disorder attributed to supernumerary isochromosome 12p mosaicism. Craniofacial dysmorphism, learning impairment and seizures are considered cardinal features. However, little is known regarding the seizure and epilepsy patterns in PKS. To better define the prevalence and spectrum of seizures in PKS, we studied 51 patients (39 male, 12 female; median age 4 years and 9 months; age range 7 months to 31 years) with confirmed 12p tetrasomy. Using a parent-based structured questionnaire, we collected data regarding seizure onset, frequency, timing, semiology, and medication therapy. Patients were recruited through our practice, at PKS Kids family events, and via the PKS Kids website. Epilepsy occurred in 27 (53%) with 23 (85%) of those with seizures having seizure onset prior to 3.5 years of age. Mean age at seizure onset was 2 years and 4 months. The most common seizure types were myoclonic (15/27, 56%), generalized convulsions (13/27, 48%), and clustered tonic spasms (similar to infantile spasms; 8/27, 30%). Thirteen of 27 patients with seizures (48%) had more than one seizure type with 26 out of 27 (96%) ever having taken antiepileptic medications. Nineteen of 27 (70%) continued to have seizures and 17/27 (63%) remained on antiepileptic medication. The most commonly used medications were: levetiracetam (10/27, 37%), valproic acid (10/27, 37%), and topiramate (9/27, 33%) with levetiracetam felt to be "most helpful" by parents (6/27, 22%). Further exploration of seizure timing, in-depth analysis of EEG recordings, and collection of MRI data to rule out confounding factors is warranted.
A 6-bit 0.81-mW 700-MS/s SAR ADC With Sparkle-Code Correction, Resolution Enhancement, and Background Window Width Calibration

This paper presents a 6-bit high-speed successive approximation register analog-to-digital converter (ADC) with sparkle-code correction. By quantizing the comparator decision time (CDT), the sparkle codes are identified and corrected, reducing the error rate from 10−4 to below 10−9. Furthermore, CDT quantization enables 1-bit increase in the ADC resolution by setting the detection boundary to be ±0.25 LSB. Thus, only five comparison cycles are needed to reach 6 bits, leading to an increased ADC speed. A novel dither-based background calibration technique is devised to accurately control the CDT detection window size and ensure process, temperature, and voltage robustness. A prototype ADC in 40-nm CMOS achieves 35.3-dB signal-to-noise/distortion ratio (SNDR) and consumes 0.81 mW while sampling at 700 MS/s.
The History of Visual Magic in Computers

Figure 1.1 shows the basic construction of a 3D graphics computer. That is also the general organization of this book, with each block more or less representing a chapter (there is no chapter on memory, but memory is discussed in multiple chapters). The book traces the earliest understanding of 3D and then the foundational mathematics to explain and construct 3D. From there we follow the history of the computer, beginning with mechanical computers, and ending up with tablets. Next, we present the amazing computer graphics (CG) algorithms and tricks, and it’s difficult to tell the story because there were a couple of periods where eruptions of new ideas and techniques seem to occur all at once. With the fundamentals of how to draw lines and create realistic images better understood, the applications that exploited those foundations. The applications of course can’t do the work by themselves and so the following chapter is on the 3D controllers that drive the display. The chapter that logically follows that is on the development of the displays, and a chapter follows that on stereovision.
American College of Rheumatology Guideline for the Treatment of Rheumatoid Arthritis

JASVINDER A. SINGH, KENNETH G. SAAG, S. LOUIS BRIDGES JR., ELIE A. AKL, RAVEENDHARA R. BANNURU, MATTHEW C. SULLIVAN, ELIZAVETA VAYSBROT, CHRISTINE MCNAUGHTON, MIKALA OSANI, ROBERT H. SHMERLING, JEFFREY R. CURTIS, DANIEL E. FURST, DEBORAH PARKS, ARTHUR KAVANAUGH, JAMES O’DELL, CHARLES KING, AMYE LEONG, ERIC L. MATTESON, JOHN T. SCHOUSBOE, BARBARA DREVLOW, SETH GINSBERG, JAMES GROBER, E. WILLIAM ST.CLAIR, ELIZABETH TINDALL, AMY S. MILLER, AND TIMOTHY MCALINDON
Fast and precise Fourier transforms

Many applications of fast Fourier transforms (FFT’s), such as computer tomography, geophysical signal processing, high-resolution imaging radars, and prediction filters, require high-precision output. An error analysis reveals that the usual method of fixed-point computation of FFT’s of vectors of length2 leads to an average loss of/2 bits of precision. This phenomenon, often referred to as computational noise, causes major problems for arithmetic units with limited precision which are often used for real-time applications. Several researchers have noted that calculation of FFT’s with algebraic integers avoids computational noise entirely, see, e.g., . We will combine a new algorithm for approximating complex numbers by cyclotomic integers with Chinese remaindering strategies to give an efficient algorithm to compute -bit precision FFT’s of length . More precisely, we will approximate complex numbers by cyclotomic integers in [ 2 2 ] whose coefficients, when expressed as polynomials in 2 2 , are bounded in absolute value by some integer . For fixed our algorithm runs in time (log( )), and produces an approximation with worst case error of (1 2 ). We will prove that this algorithm has optimal worst case error by proving a corresponding lower bound on the worst case error of any approximation algorithm for this task. The main tool for designing the algorithms is the use of the cyclotomic units, a subgroup of finite index in the unit group of the cyclotomic field. First implementations of our algorithms indicate that they are fast enough to be used for the design of low-cost high-speed/highprecision FFT chips.
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance

Information theoretic measures form a fundamental class of measures for comparing clusterings, and have recently received increasing interest. Neverthel ss, a number of questions concerning their properties and inter-relationships remain unresolv ed. In this paper, we perform an organized study of information theoretic measures for clustering com parison, including several existing popular measures in the literature, as well as some newly propos ed nes. We discuss and prove their important properties, such as the metric property and the no rmalization property. We then highlight to the clustering community the importance of correct ing information theoretic measures for chance, especially when the data size is small compared to th e number of clusters present therein. Of the available information theoretic based measures, we a dvocate the normalized information distance (NID) as a general measure of choice, for it possess e concurrently several important properties, such as being both a metric and a normalized meas ure, admitting an exact analytical adjusted-for-chance form, and using the nominal [0,1] range better than other normalized variants.
Virtual Valcamonica: Collaborative Exploration of Prehistoric Petroglyphs and Their Surrounding Environment in Multi-User Virtual Reality

In this article, we present a novel, multi-user, virtual reality environment for the interactive, collaborative 3D analysis of large 3D scans and the technical advancements that were necessary to build it: a multi-view rendering system for large 3D point clouds, a suitable display infrastructure, and a suite of collaborative 3D interaction techniques. The cultural heritage site of Valcamonica in Italy with its large collection of prehistoric rock-art served as an exemplary use case for evaluation. The results show that our output-sensitive level-of-detail rendering system is capable of visualizing a 3D dataset with an aggregate size of more than 14 billion points at interactive frame rates. The system design in this exemplar application results from close exchange with a small group of potential users: archaeologists with expertise in rockart. The system allows them to explore the prehistoric art and its spatial context with highly realistic appearance. A set of dedicated interaction techniques was developed to facilitate collaborative visual analysis. A multi-display workspace supports the immediate comparison of geographically distributed artifacts. An expert review of the final demonstrator confirmed the potential for added value in rock-art research and the usability of our collaborative interaction techniques.
Malacology: A Programmable Storage System

Storage systems need to support high-performance for special-purpose data processing applications that run on an evolving storage device technology landscape. This puts tremendous pressure on storage systems to support rapid change both in terms of their interfaces and their performance. But adapting storage systems can be difficult because unprincipled changes might jeopardize years of code-hardening and performance optimization efforts that were necessary for users to entrust their data to the storage system. We introduce the programmable storage approach, which exposes internal services and abstractions of the storage stack as building blocks for higher-level services. We also build a prototype to explore how existing abstractions of common storage system services can be leveraged to adapt to the needs of new data processing systems and the increasing variety of storage devices. We illustrate the advantages and challenges of this approach by composing existing internal abstractions into two new higher-level services: a file system metadata load balancer and a high-performance distributed shared-log. The evaluation demonstrates that our services inherit desirable qualities of the back-end storage system, including the ability to balance load, efficiently propagate service metadata, recover from failure, and navigate trade-offs between latency and throughput using leases.
Analysis and Implementation of a Single-Stage Flyback PV Microinverter With Soft Switching

This paper presents a novel zero-voltage switching (ZVS) approach to a grid-connected single-stage flyback inverter. The soft-switching of the primary switch is achieved by allowing negative current from the grid side through bidirectional switches placed on the secondary side of the transformer. Basically, the negative current discharges the metal-oxide-semiconductor field-effect transistor's output capacitor, thereby allowing turn on of the primary switch under zero voltage. To optimize the amount of reactive current required to achieve ZVS, a variable-frequency control scheme is implemented over the line cycle. In addition, the bidirectional switches on the secondary side of the transformer have ZVS during the turn- on times. Therefore, the switching losses of the bidirectional switches are negligible. A 250-W prototype has been implemented to validate the proposed scheme. Experimental results confirm the feasibility and superior performance of the converter compared with the conventional flyback inverter.
SCRAP: Architecture for signature-based protection from Code Reuse Attacks

Code Reuse Attacks (CRAs) recently emerged as a new class of security exploits. CRAs construct malicious programs out of small fragments (gadgets) of existing code, thus eliminating the need for code injection. Existing defenses against CRAs often incur large performance overheads or require extensive binary rewriting and other changes to the system software. In this paper, we examine a signature-based detection of CRAs, where the attack is detected by observing the behavior of programs and detecting the gadget execution patterns. We first demonstrate that naive signature-based defenses can be defeated by introducing special “delay gadgets” as part of the attack. We then show how a software-configurable signature-based approach can be designed to defend against such stealth CRAs, including the attacks that manage to use longer-length gadgets. The proposed defense (called SCRAP) can be implemented entirely in hardware using simple logic at the commit stage of the pipeline. SCRAP is realized with minimal performance cost, no changes to the software layers and no implications on binary compatibility. Finally, we show that SCRAP generates no false alarms on a wide range of applications.