nils-herrmann/clean_scirepeval huggingface dataset:
Fast Vehicle Detection with Lateral Convolutional Neural Network

In this paper, we propose a fast vehicle detector for traffic surveillance. We first explore using different feature layers from a deep residual network to perform vehicle detection. Experiment results show that the high-resolution features from earlier feature layers contain more structural information, which is good to achieve fine-grained localization but yields low recall rates. The low-resolution features in the deep layers contain semantically strong information, which is good to represent the objectness but too coarse to achieve accurate localization. Therefore, we decouple the localization and objectness prediction from a single layer. Instead, we employ a lateral network that takes the features from earlier layers as input and outputs the localization residual. Our proposed detector can achieve fast detection at a rate of 28 frames/s, and a mean average precision (mAP) of 67.25% in the DETRAC vehicle detection benchmark.
Learning-based Query Performance Modeling and Prediction

Accurate query performance prediction (QPP) is central to effective resource management, query optimization and query scheduling. Analytical cost models, used in current generation of query optimizers, have been successful in comparing the costs of alternative query plans, but they are poor predictors of execution latency. As a more promising approach to QPP, this paper studies the practicality and utility of sophisticated learning-based models, which have recently been applied to a variety of predictive tasks with great success, in both static (i.e., fixed) and dynamic query workloads. We propose and evaluate predictive modeling techniques that learn query execution behavior at different granularities, ranging from coarse-grained plan-level models to fine-grained operator-level models. We demonstrate that these two extremes offer a tradeoff between high accuracy for static workload queries and generality to unforeseen queries in dynamic workloads, respectively, and introduce a hybrid approach that combines their respective strengths by selectively composing them in the process of QPP. We discuss how we can use a training workload to (i) pre-build and materialize such models offline, so that they are readily available for future predictions, and (ii) build new models online as new predictions are needed. All prediction models are built using only static features (available prior to query execution) and the performance values obtained from the offline execution of the training workload. We fully implemented all these techniques and extensions on top of Postgre SQL and evaluated them experimentally by quantifying their effectiveness over analytical workloads, represented by well-established TPC-H data and queries. The results provide quantitative evidence that learning-based modeling for QPP is both feasible and effective for both static and dynamic workload scenarios.
Observations on periorbital and midface aging.

Many of the anatomical changes of facial aging are still poorly understood. This study looked at the aging process in individuals linearly over time, focusing on aspects of periorbital aging and the upper midface.



The author compared photographs of patients' friends and relatives taken 10 to 50 years before with closely matched recent follow-up pictures. The best-matching old and recent pictures were equally sized and superimposed in the computer. The images were then assembled into GIF animations, which automate the fading of one image into the other and back again indefinitely.



The following findings were new to the author: (1) the border of the pigmented lid skin and thicker cheek skin (the lid-cheek junction) is remarkably stable in position over time, becoming more visible by contrast, not by vertical descent as is commonly assumed. (2) Orbicularis wrinkles on the cheek and moles and other markers on the upper midface were also stable over decades. (3) With aging, there can be a distinct change in the shape of the upper eyelid. The young upper lid frequently has a medially biased peak. The upper lid peak becomes more central in the older lid. This article addresses these three issues. No evidence was seen here for descent of the globe in the orbit.



There seems to be very little ptosis (inferior descent) of the lid-cheek junction or of the upper midface. These findings suggest that vertical descent of skin, and by association, subcutaneous tissue, is not necessarily a major component of aging in those areas. In addition, the arc of the upper lid changes shape in a characteristic way in some patients. Other known changes of the periorbital area are visualized.
Domain Generalization for Object Recognition with Multi-task Autoencoders

The problem of domain generalization is to take knowledge acquired from a number of related domains, where training data is available, and to then successfully apply it to previously unseen domains. We propose a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance for cross-domain object recognition. The algorithm extends the standard denoising autoencoder framework by substituting artificially induced corruption with naturally occurring inter-domain variability in the appearance of objects. Instead of reconstructing images from noisy versions, MTAE learns to transform the original image into analogs in multiple related domains. It thereby learns features that are robust to variations across domains. The learnt features are then used as inputs to a classifier. We evaluated the performance of the algorithm on benchmark image recognition datasets, where the task is to learn features from multiple datasets and to then predict the image label from unseen datasets. We found that (denoising) MTAE outperforms alternative autoencoder-based models as well as the current state-of-the-art algorithms for domain generalization.
Mitigating Sybils in Federated Learning Poisoning

Machine learning (ML) over distributed multiparty data is required for a variety of domains. Existing approaches, such as federated learning, collect the outputs computed by a group of devices at a central aggregator and run iterative algorithms to train a globally shared model. Unfortunately, such approaches are susceptible to a variety of attacks, including model poisoning, which is made substantially worse in the presence of sybils. In this paper we first evaluate the vulnerability of federated learning to sybil-based poisoning attacks. We then describe FoolsGold, a novel defense to this problem that identifies poisoning sybils based on the diversity of client updates in the distributed learning process. Unlike prior work, our system does not bound the expected number of attackers, requires no auxiliary information outside of the learning process, and makes fewer assumptions about clients
Severe short stature due to 3-M syndrome with a novel OBSL1 gene mutation.

3-M syndrome is an underdiagnosed autosomal recessive disorder characterized by severe pre- and postnatal growth retardation with minimal dysmorphic features and distinguishing radiological findings. We report a patient who was first admitted at 7.5 years of age. He was born to consanguineous parents with a birth weight of 2250 g. Physical examination revealed a severe short stature (height, 95 cm; SD score -5.64) and minimal dysmorphic features. Biochemistry, endocrine work-up, and karyotype were normal. Reevaluation at 16.5 years of age revealed a height of 128.5 cm (SD score -5.27), prominent forehead, anteverted nasal openings, fleshy nasal tip, full lips, malar hypoplasia, hyperlordosis, prominent heels, testicular volumes 8-10 mL, and pubic hair consistent with Tanner stage II. Growth hormone trial for a year resulted in inadequate height gain (3 cm). The diagnosis of 3-M syndrome was made upon typical findings (thin long bones with diaphyseal narrowing and tall lumbar vertebrae) in a recent skeletal survey. Genetic analysis disclosed a homozygote frame shift mutation in exon 2: c.457_458delinsT resulting in p.Gly153fs.
Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming

The kernel function plays a central role in kernel methods. In this paper, we consider the automated learning of the kernel matrix over a convex combination of pre-specified kernel matrices in Regularized Kernel Discriminant Analysis (RKDA), which performs lineardiscriminant analysis in the feature space via the kernel trick. Previous studies have shown that this kernel learning problem can be formulated as a semidefinite program (SDP), which is however computationally expensive, even with the recent advances in interior point methods. Based on the equivalence relationship between RKDA and least square problems in the binary-class case, we propose a Quadratically Constrained Quadratic Programming (QCQP) formulation for the kernel learning problem, which can be solved more efficiently than SDP. While most existing work on kernel learning deal with binary-class problems only, we show that our QCQP formulation can be extended naturally to the multi-class case. Experimental results on both binary-class and multi-class benchmarkdata sets show the efficacy of the proposed QCQP formulations.
Information Contagion: an Empirical Study of the Spread of News on Digg and Twitter Social Networks

Social networks have emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. Social media has made social networks ubiquitous, and also given researchers access to massive quantities of data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We address this gap by analyzing data from two popular social news sites. Specifically, we extract social networks of active users on Digg and Twitter, and track how interest in news stories spreads among them. We show that social networks play a crucial role in the spread of information on these sites, and that network structure affects dynamics of information flow.
Careful what you share in six seconds: Detecting cyberbullying instances in Vine

As online social networks have grown in popularity, teenage users have become increasingly exposed to the threats of cyberbullying. The primary goal of this research paper is to investigate cyberbullying behaviors in Vine, a mobile based video-sharing online social network, and design novel approaches to automatically detect instances of cyberbullying over Vine media sessions. We first collect a set of Vine video sessions and use CrowdFlower, a crowd-sourced website, to label the media sessions for cyberbullying and cyberaggression. We then perform a detailed analysis of cyberbullying behavior in Vine. Based on the labeled data, we design a classifier to detect instances of cyberbullying and evaluate the performance of that classifier.
Wiring specificity in the direction-selectivity circuit of the retina

The proper connectivity between neurons is essential for the implementation of the algorithms used in neural computations, such as the detection of directed motion by the retina. The analysis of neuronal connectivity is possible with electron microscopy, but technological limitations have impeded the acquisition of high-resolution data on a large enough scale. Here we show, using serial block-face electron microscopy and two-photon calcium imaging, that the dendrites of mouse starburst amacrine cells make highly specific synapses with direction-selective ganglion cells depending on the ganglion cell’s preferred direction. Our findings indicate that a structural (wiring) asymmetry contributes to the computation of direction selectivity. The nature of this asymmetry supports some models of direction selectivity and rules out others. It also puts constraints on the developmental mechanisms behind the formation of synaptic connections. Our study demonstrates how otherwise intractable neurobiological questions can be addressed by combining functional imaging with the analysis of neuronal connectivity using large-scale electron microscopy.
Introduction to Algorithms

problems To understand the class of polynomial-time solvable proble ms, we must first have a formal notion of what a “problem” is. We define anbstract problemQ to be a binary relation on a set I of probleminstancesand a setS of problemsolutions. For example, an instance for SHORTEST-PATH is a triple consi sting of a graph and two vertices. A solution is a sequence of vertices in the g raph, with perhaps the empty sequence denoting that no path exists. The problem SHORTEST-PATH itself is the relation that associates each instance of a gra ph and two vertices with a shortest path in the graph that connects the two vertices. S ince shortest paths are not necessarily unique, a given problem instance may have mo r than one solution. This formulation of an abstract problem is more general than is required for our purposes. As we saw above, the theory of NP-completeness res tricts attention to decision problems : those having a yes/no solution. In this case, we can view an abstract decision problem as a function that maps the instan ce setI to the solution set {0, 1}. For example, a decision problem related to SHORTEST-PATH i s the problem PATH that we saw earlier. If i = 〈G,u, v,k〉 is an instance of the decision problem PATH, then PATH(i ) = 1 (yes) if a shortest path fromu to v has at mostk edges, and PATH (i ) = 0 (no) otherwise. Many abstract problems are not decision problems, but rather optimization problems , in which some value must be minimized or maximized. As we saw above, however, it is usual ly a simple matter to recast an optimization problem as a decision problem that is no harder. 1See Hopcroft and Ullman [156] or Lewis and Papadimitriou [20 4] for a thorough treatment of the Turing-machine model. 34.1 Polynomial time 973
Evaluating topic coherence measures

Topic models extract representative word sets—called topics—from word counts in documents without requiring any semantic annotations. Topics are not guaranteed to be well interpretable, therefore, coherence measures have been proposed to distinguish between good and bad topics. Studies of topic coherence so far are limited to measures that score pairs of individual words. For the first time, we include coherence measures from scientific philosophy that score pairs of more complex word subsets and apply them to topic scoring.
Model-based hierarchical reinforcement learning and human action control

Recent work has reawakened interest in goal-directed or 'model-based' choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour.
BloomFlash: Bloom Filter on Flash-Based Storage

The bloom filter is a probabilistic data structure that provides a compact representation of a set of elements. To keep false positive probabilities low, the size of the bloom filter must be dimensioned a priori to be linear in the maximum number of keys inserted, with the linearity constant ranging typically from one to few bytes. A bloom filter is most commonly used as an in memory data structure, hence its size is limited by the availability of RAM space on the machine. As datasets have grown over time to Internet scale, so have the RAM space requirements of bloom filters. If sufficient RAM space is not available, we advocate that flash memory may serve as a suitable medium for storing bloom filters, since it is about one-tenth the cost of RAM per GB while still providing access times orders of magnitude faster than hard disk. We present BLOOMFLASH, a bloom filter designed for flash memory based storage, that provides a new dimension of trade off with bloom filter access times to reduce RAM space usage (and hence system cost). The simple design of a single flat bloom filter on flash suffers from many performance bottlenecks, including in-place bit updates that are inefficient on flash and multiple reads and random writes spread out across many flash pages for a single lookup or insert operation. To mitigate these performance bottlenecks, BLOOMFLASH leverages two key design innovations: (i) buffering bit updates in RAM and applying them in bulk to flash that helps to reduce random writes to flash, and (ii) a hierarchical bloom filter design consisting of component bloom filters, stored one per flash page, that helps to localize reads and writes on flash. We use two real-world data traces taken from representative bloom filter applications to drive and evaluate our design. BLOOMFLASH achieves bloom filter access times in the range of few tens of microseconds, thus allowing up to order of tens of thousands operations per sec.
Hybrid Electric Vehicles: Architecture and Motor Drives

Electric traction is one of the most promising technologies that can lead to significant improvements in vehicle performance, energy utilization efficiency, and polluting emissions. Among several technologies, hybrid electric vehicle (HEV) traction is the most promising technology that has the advantages of high performance, high fuel efficiency, low emissions, and long operating range. Moreover, the technologies of all the component hardware are technically and markedly available. At present, almost all the major automotive manufacturers are developing hybrid electric vehicles, and some of them have marketed their productions, such as Toyota and Honda. This paper reviews the present technologies of HEVs in the range of drivetrain configuration, electric motor drives, and energy storages
Pharmacotherapy for borderline personality disorder: Cochrane systematic review of randomised trials.

Many patients with borderline personality disorder receive pharmacological treatment, but there is uncertainty about the usefulness of such therapies.



To evaluate the evidence of effectiveness of pharmacotherapy in treating different facets of the psychopathology of borderline personality disorder.



A Cochrane Collaboration systematic review and meta-analysis of randomised comparisons of drug v. placebo, drug v. drug, or single drug v. combined drug treatment in adult patients with borderline personality disorder was conducted. Primary outcomes were overall disorder severity as well as specific core symptoms. Secondary outcomes comprised associated psychiatric pathology and drug tolerability.



Twenty-seven trials were included in which first- and second-generation antipsychotics, mood stabilisers, antidepressants and omega-3 fatty acids were tested. Most beneficial effects were found for the mood stabilisers topiramate, lamotrigine and valproate semisodium, and the second-generation antipsychotics aripiprazole and olanzapine. However, the robustness of findings is low, since they are based mostly on single, small studies. Selective serotonin reuptake inhibitors so far lack high-level evidence of effectiveness.



The current evidence from randomised controlled trials suggests that drug treatment, especially with mood stabilisers and second-generation antipsychotics, may be effective for treating a number of core symptoms and associated psychopathology, but the evidence does not currently support effectiveness for overall severity of borderline personality disorder. Pharmacotherapy should therefore be targeted at specific symptoms.
BGP security in partial deployment: is the juice worth the squeeze?

As the rollout of secure route origin authentication with the RPKI slowly gains traction among network operators, there is a push to standardize secure path validation for BGP (i.e., S*BGP: S-BGP, soBGP, BGPSEC, etc.). Origin authentication already does much to improve routing security. Moreover, the transition to S*BGP is expected to be long and slow, with S*BGP coexisting in "partial deployment" alongside BGP for a long time. We therefore use theoretical and experimental approach to study the security benefits provided by partially-deployed S*BGP, vis-a-vis those already provided by origin authentication. Because routing policies have a profound impact on routing security, we use a survey of 100 network operators to find the policies that are likely to be most popular during partial S*BGP deployment. We find that S*BGP provides only meagre benefits over origin authentication when these popular policies are used. We also study the security benefits of other routing policies, provide prescriptive guidelines for partially-deployed S*BGP, and show how interactions between S*BGP and BGP can introduce new vulnerabilities into the routing system.
InvisiMem: Smart memory defenses for memory bus side channel

A practically feasible low-overhead hardware design that provides strong defenses against memory bus side channel remains elusive. This paper observes that smart memory, memory with compute capability and a packetized interface, can dramatically simplify this problem. InvisiMem expands the trust base to include the logic layer in the smart memory to implement cryptographic primitives, which aid in addressing several memory bus side channel vulnerabilities efficiently. This allows the secure host processor to send encrypted addresses over the untrusted memory bus, and thereby eliminates the need for expensive address obfuscation techniques based on Oblivious RAM (ORAM). In addition, smart memory enables efficient solutions for ensuring freshness without using expensive Merkle trees, and mitigates memory bus timing channel using constant heart-beat packets. We demonstrate that InvisiMem designs have one to two orders of magnitude of lower overheads for performance, space, energy, and memory bandwidth, compared to prior solutions.
Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets

The Frank-Wolfe method (a.k.a. conditional gradient algorithm) for smooth optimization has regained much interest in recent years in the context of large scale optimization and machine learning. A key advantage of the method is that it avoids projections the computational bottleneck in many applications replacing it by a linear optimization step. Despite this advantage, the known convergence rates of the FW method fall behind standard first order methods for most settings of interest. It is an active line of research to derive faster linear optimization-based algorithms for various settings of convex optimization. In this paper we consider the special case of optimization over strongly convex sets, for which we prove that the vanila FW method converges at a rate of 1 t2 . This gives a quadratic improvement in convergence rate compared to the general case, in which convergence is of the order 1t , and known to be tight. We show that various balls induced by `p norms, Schatten norms and group norms are strongly convex on one hand and on the other hand, linear optimization over these sets is straightforward and admits a closed-form solution. We further show how several previous fastrate results for the FW method follow easily from our analysis.