nils-herrmann/clean_scirepeval huggingface dataset:
Flutter discrimination: neural codes, perception, memory and decision making.

Recent studies combining psychophysical and neurophysiological experiments in behaving monkeys have provided new insights into how several cortical areas integrate efforts to solve a vibrotactile discrimination task. In particular, these studies have addressed how neural codes are related to perception, working memory and decision making in this model. The primary somatosensory cortex drives higher cortical areas where past and current sensory information are combined, such that a comparison of the two evolves into a behavioural decision. These and other observations in visual tasks indicate that decisions emerge from highly-distributed processes in which the details of a scheduled motor plan are gradually specified by sensory information.
Comparative analyses of CTCF and BORIS occupancies uncover two distinct classes of CTCF binding genomic regions

CTCF and BORIS (CTCFL), two paralogous mammalian proteins sharing nearly identical DNA binding domains, are thought to function in a mutually exclusive manner in DNA binding and transcriptional regulation. Here we show that these two proteins co-occupy a specific subset of regulatory elements consisting of clustered CTCF binding motifs (termed 2xCTSes). BORIS occupancy at 2xCTSes is largely invariant in BORIS-positive cancer cells, with the genomic pattern recapitulating the germline-specific BORIS binding to chromatin. In contrast to the single-motif CTCF target sites (1xCTSes), the 2xCTS elements are preferentially found at active promoters and enhancers, both in cancer and germ cells. 2xCTSes are also enriched in genomic regions that escape histone to protamine replacement in human and mouse sperm. Depletion of the BORIS gene leads to altered transcription of a large number of genes and the differentiation of K562 cells, while the ectopic expression of this CTCF paralog leads to specific changes in transcription in MCF7 cells. We discover two functionally and structurally different classes of CTCF binding regions, 2xCTSes and 1xCTSes, revealed by their predisposition to bind BORIS. We propose that 2xCTSes play key roles in the transcriptional program of cancer and germ cells.
Learning low-precision neural networks without Straight-Through Estimator(STE)

The Straight-Through Estimator (STE) is widely used for back-propagating gradients through the quantization function, but the STE technique lacks a complete theoretical understanding. We propose an alternative methodology called alpha-blending (AB), which quantizes neural networks to low-precision using stochastic gradient descent (SGD). Our method (AB) avoids STE approximation by replacing the quantized weight in the loss function by an affine combination of the quantized weight w_q and the corresponding full-precision weight w with non-trainable scalar coefficient $\alpha$ and $1-\alpha$. During training, $\alpha$ is gradually increased from 0 to 1; the gradient updates to the weights are through the full-precision term, $(1-\alpha)w$, of the affine combination; the model is converted from full-precision to low-precision progressively. To evaluate the method, a 1-bit BinaryNet on CIFAR10 dataset and 8-bits, 4-bits MobileNet v1, ResNet_50 v1/2 on ImageNet dataset are trained using the alpha-blending approach, and the evaluation indicates that AB improves top-1 accuracy by 0.9%, 0.82% and 2.93% respectively compared to the results of STE based quantization.
An Exploratory Study of Current Information Security Training and Awareness Practices in Organizations

Effective information security training and awareness (ISTA) is essential to protect organizational information resources. Our review of industry bestpractice guidelines on ISTA exposed two key deficiencies. First, they are presented at a conceptuallevel without any empirical evidence of their validity. Second, the guidelines are generic (one size fits all) without consideration of the diversity in organizational contexts where they will be applied. Given these deficiencies in ISTA guidance, this paper reports on the findings of an exploratory study into how ISTA is implemented in different organizational contexts in six organizations. The paper identifies three challenges: the lack of motivational aspects in current ISTA program, the competition for employees’ attention and the difficulty in measuring the effectiveness of ISTA program. Several recommendations and suggestions were outlined to overcome these challenges.
Optimizing Video Object Detection via a Scale-Time Lattice

High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e.g. those that require detecting objects from video streams in real time. The key to this problem is to trade accuracy for efficiency in an effective way, i.e. reducing the computing cost while maintaining competitive performance. To seek a good balance, previous efforts usually focus on optimizing the model architectures. This paper explores an alternative approach, that is, to reallocate the computation over a scale-time space. The basic idea is to perform expensive detection sparsely and propagate the results across both scales and time with substantially cheaper networks, by exploiting the strong correlations among them. Specifically, we present a unified framework that integrates detection, temporal propagation, and across-scale refinement on a Scale-Time Lattice. On this framework, one can explore various strategies to balance performance and cost. Taking advantage of this flexibility, we further develop an adaptive scheme with the detector invoked on demand and thus obtain improved tradeoff. On ImageNet VID dataset, the proposed method can achieve a competitive mAP 79.6% at 20 fps, or 79.0% at 62 fps as a performance/speed
Boosting video popularity through recommendation systems

While search engines are the major sources of content discovery on online content providers and e-commerce sites, their capability is limited since textual descriptions cannot fully describe the semantic of content such as videos. Recommendation systems are now widely used in online content providers and e-commerce sites and play an important role in discovering content. In this paper, we describe how one can boost the popularity of a video through the recommendation system in YouTube. We present a model that captures the view propagation between videos through the recommendation linkage and quantifies the influence that a video has on the popularity of another video. Furthermore, we identify that the similarity in titles and tags is an important factor in forming the recommendation linkage between videos. This suggests that one can manipulate the metadata of a video to boost its popularity.
Folded Rotman lens multibeam antenna in SIW technology at 24 GHz

We introduce here a new dual-layer multibeam antenna with a folded Rotman lens used as a compact beam forming network in SIW technology. The objective is to reduce the overall size of the antenna system by folding the Rotman lens on two layers along the array port contour and using a transition based on an exotic reflector and several coupling vias holes. To validate the proposed concepts, an antenna system has been designed at 24.15 GHz. The radiating structure is a SIW slotted waveguide array made of fifteen resonant waveguides. The simulated results show very good scanning performances over ±47°. It is also demonstrated that the proposed transition can lead to a size reduction of about 50% for the lens, and more than 33% for the overall size of the antenna.
Anxiety and Depression in COPD: Current Understanding, Unanswered Questions, and Research Needs.

Approximately 60 million people in the United States live with one of four chronic conditions: heart disease, diabetes, chronic respiratory disease, and major depression. Anxiety and depression are very common comorbidities in COPD and have significant impact on patients, their families, society, and the course of the disease.



We report the proceedings of a multidisciplinary workshop on anxiety and depression in COPD that aimed to shed light on the current understanding of these comorbidities, and outline unanswered questions and areas of future research needs.



Estimates of prevalence of anxiety and depression in COPD vary widely but are generally higher than those reported in some other advanced chronic diseases. Untreated and undetected anxiety and depressive symptoms may increase physical disability, morbidity, and health-care utilization. Several patient, physician, and system barriers contribute to the underdiagnosis of these disorders in patients with COPD. While few published studies demonstrate that these disorders associated with COPD respond well to appropriate pharmacologic and nonpharmacologic therapy, only a small proportion of COPD patients with these disorders receive effective treatment.



Future research is needed to address the impact, early detection, and management of anxiety and depression in COPD.
Bioconversion of lignocellulose: inhibitors and detoxification

Bioconversion of lignocellulose by microbial fermentation is typically preceded by an acidic thermochemical pretreatment step designed to facilitate enzymatic hydrolysis of cellulose. Substances formed during the pretreatment of the lignocellulosic feedstock inhibit enzymatic hydrolysis as well as microbial fermentation steps. This review focuses on inhibitors from lignocellulosic feedstocks and how conditioning of slurries and hydrolysates can be used to alleviate inhibition problems. Novel developments in the area include chemical in-situ detoxification by using reducing agents, and methods that improve the performance of both enzymatic and microbial biocatalysts.
Performance characteristics of MAUI: an intelligent memory system architecture

Combining ideas from several previous proposals, such as Active Pages, DIVA, and ULMT, we present the Memory Arithmetic Unit and Interface (MAUI) architecture. Because the "intelligence" of the MAUI intelligent memory system architecture is located in the memory-controller, logic and DRAM are not required to be integrated into a single chip, and use of off-the-shelf DRAMs is permitted. The MAUI's computational engine performs memory-bound SIMD computations close to the memory system, enabling more efficient memory pipelining. A simulator modeling the MAUI architecture was added to the SimpleScalar v4.0 tool-set. Not surprisingly, simulations show that application speedup increases as the memory system speed increases and the dataset size increases. Simulation results show single-threaded application speedup of over 100% is possible, and suggest that a total system speedup of about 300% is possible in a multi-threaded environment.
Deep Feature Pyramid Reconfiguration for Object Detection

State-of-the-art object detectors usually learn multi-scale representations to get better results by employing feature pyramids. However, the current designs for feature pyramids are still inefficient to integrate the semantic information over different scales. In this paper, we begin by investigating current feature pyramids solutions, and then reformulate the feature pyramid construction as the feature reconfiguration process. Finally, we propose a novel reconfiguration architecture to combine low-level representations with high-level semantic features in a highly-nonlinear yet efficient way. In particular, our architecture which consists of global attention and local reconfigurations, is able to gather task-oriented features across different spatial locations and scales, globally and locally. Both the global attention and local reconfiguration are lightweight, in-place, and end-to-end trainable. Using this method in the basic SSD system, our models achieve consistent and significant boosts compared with the original model and its other variations, without losing real-time processing speed.
Major Self-Mutilations : Castration and Enucleation Prabhat

Pathological self-mutilation has been prevalent throughout history and in many cultures. Major self mutilations autocastration, eye enucleation and limb amputation are rarer than minor self-mutilations like wrist cutting, head banging etc. Because of their gruesome nature, major self-mutilations invoke significant negative emotions among therapists and caregivers. Unfortunately, till date, there is very little research in this field. In the absence of robust neurobiological understanding and speculative psychodynamic theories, the current understanding is far from satisfactory. At the same time, the role of culture and society cannot be completely ignored while understanding major self-mutilations. Literature from western culture describes this as an act of repentance towards past bad thoughts or acts in contrast to the traditional eastern culture that praises it as an act of sacrifice for achieving superiority and higher goals in the society. The authors present here two cases of major self-mutilation i.e. autocastration and autoenucleation both of which occurred in patients suffering from schizophrenia. They have also reviewed the existing literature and current understanding of this phenomenon (German J Psychiatry 2010; 13 (4): 164-170).
How to Situate Cognition : Letting Nature Take its Course *

1. The Situation in Cognition The situated cognition movement in the cognitive sciences, like those sciences themselves, is a loose-knit family of approaches to understanding the mind and cognition. While it has both philosophical and psychological antecedents in thought stretching back over the last century (see Gallagher, this volume, Clancey, this volume,), it has developed primarily since the late 1970s as an alternative to, or a modification of, the then predominant paradigms for exploring the mind within the cognitive sciences. For this reason it has been common to characterize situated cognition in terms of what it is not, a cluster of "anti-isms". Situated cognition has thus been described as opposed to Platonism, Cartesianism, individualism, representationalism, and even
Broken hearts or broken bonds. Love and death in historical perspective.

Psychological theories and practices frequently neglect the extent to which their subject matter is historically and culturally defined. This issue is explored in the context of theories and therapies related to bereavement. Contemporary orientations emphasize the importance of breaking bonds with the deceased and the return of survivors to autonomous lifestyles. Placing the orientation in cultural and historical context reveals that it is largely a product of a modernist worldview. Within the romanticist ethos of the preceding century, such breaking of bonds would destroy one's identity and the meaning of life. In light of contemporary variations in subcultural meanings and values, a postmodern view is suggested in which reflexive responsibility is focal.
3D Morphable Models as Spatial Transformer Networks

In this paper, we show how a 3D Morphable Model (i.e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM-STN) within a convolutional neural network. This is an extension of the original spatial transformer network in that we are able to interpret and normalise 3D pose changes and self-occlusions. The trained localisation part of the network is independently useful since it learns to fit a 3D morphable model to a single image. We show that the localiser can be trained using only simple geometric loss functions on a relatively small dataset yet is able to perform robust normalisation on highly uncontrolled images including occlusion, self-occlusion and large pose changes.
MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment

This research strives for natural language moment retrieval in long, untrimmed video streams. The problem nevertheless is not trivial especially when a video contains multiple moments of interests and the language describes complex temporal dependencies, which often happens in real scenarios. We identify two crucial challenges: semantic misalignment and structural misalignment. However, existing approaches treat different moments separately and do not explicitly model complex moment-wise temporal relations. In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network. MAN naturally assigns candidate moment representations aligned with language semantics over different temporal locations and scales. Most importantly, we propose to explicitly model momentwise temporal relations as a structured graph and devise an iterative graph adjustment network to jointly learn the best structure in an end-to-end manner. We evaluate the proposed approach on two challenging public benchmarks Charades-STA and DiDeMo, where our MAN significantly outperforms the state-of-the-art by a large margin.
Personalized music recommendation algorithm based on tag information

With the development of the mobile Internet and streaming media technology, digital music has been accepted by many people. However, in the face of the massive music data on the Internet, users want to find favorite music is like looking for a needle in a haystack via traditional music indexing techniques. In this paper, an improved algorithm for personalized music recommendation based on tag information is proposed. Firstly, we use the improved user-based collaborative filtering algorithm to deal with the user s long-term preferences. Secondly, according to the user-tag-music relationships, getting the music that associated with the user via recommendation algorithm based on bipartite graph. Experiments on real dataset demonstrate the efficiency and effectiveness of the improved algorithm, getting better results and have practical significance.
Bumps and Bruises: Mining Presidential Campaign Announcements on Twitter

Online social media plays an increasingly significant role in shaping the political discourse during elections worldwide. In the 2016 U.S. presidential election, political campaigns strategically designed candidacy announcements on Twitter to produce a significant increase in online social media attention. We use large-scale online social media communications to study the factors of party, personality, and policy in the Twitter discourse following six major presidential campaign announcements for the 2016 U.S. presidential election. We observe that all campaign announcements result in an instant bump in attention, with up to several orders of magnitude increase in tweets. However, we find that Twitter discourse as a result of this bump in attention has overwhelmingly negative sentiment. The bruising criticism, driven by crosstalk from Twitter users of opposite party affiliations, is organized by hashtags such as #NoMoreBushes and #WhyImNotVotingForHillary. We analyze how people take to Twitter to criticize specific personality traits and policy positions of presidential candidates.
Analysis of Android Inter-App Security Vulnerabilities Using COVERT

The state-of-the-art in securing mobile software systems are substantially intended to detect and mitigate vulnerabilities in a single app, but fail to identify vulnerabilities that arise due to the interaction of multiple apps, such as collusion attacks and privilege escalation chaining, shown to be quite common in the apps on the market. This paper demonstrates COVERT, a novel approach and accompanying tool-suite that relies on a hybrid static analysis and lightweight formal analysis technique to enable compositional security assessment of complex software. Through static analysis of Android application packages, it extracts relevant security specifications in an analyzable formal specification language, and checks them as a whole for inter-app vulnerabilities. To our knowledge, COVERT is the first formally-precise analysis tool for automated compositional analysis of Android apps. Our study of hundreds of Android apps revealed dozens of inter-app vulnerabilities, many of which were previously unknown. A video highlighting the main features of the tool can be found at: http://youtu.be/bMKk7OW7dGg.
Effects of social comparison direction, threat, and self-esteem on affect, self-evaluation, and expected success.

Two studies explored the conditions under which social comparisons are used to manage negative affect and naturalistic threats. Study 1 examined induced mood and dispositional self-esteem as determinants of affective responses to upward and downward comparisons. Consistent with a mood repair prediction, only low-self-esteem Ss in whom a negative mood had been induced reported improved mood after exposure to downward comparison information. Study 2 examined the impact of naturalistic threats on responses to comparison information. Relative to a no-comparison baseline, low-self-esteem Ss who had experienced a recent academic setback reported more favorable self-evaluations and greater expectations of future success in college after exposure to downward comparison information. These results remained significant after controlling statistically for general distress. Implications for downward comparison theory are discussed.
Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential.

The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases.



Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies.



Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community.
A Class-$E$ RF Power Amplifier With a Flat-Top Transistor-Voltage Waveform

This paper shows a new class-<formula formulatype="inline"><tex Notation="TeX">$E$</tex></formula> amplifier topology with the objective to increase the nominal class-<formula formulatype="inline"><tex Notation="TeX">$E$</tex></formula> output power for a given voltage and current stress on the power transistor. To obtain that result, a parallel LC resonator is added to the load network, tuned to the second harmonic of the switching frequency. A class-<formula formulatype="inline"><tex Notation="TeX">$E$</tex></formula> power amplifier is obtained whose transistor-voltage waveform peak value is 81% of the peak value of the voltage of a nominal class- <formula formulatype="inline"><tex Notation="TeX">$E$</tex></formula> amplifier using the same dc supply voltage. In this amplifier, the peak voltage across the transistor is 3.0 times the dc supply voltage, instead of the 3.6 times associated with nominal class-<formula formulatype="inline"><tex Notation="TeX">$E$</tex></formula> amplifiers. A normalized design is presented, and the behavior of the circuit is analyzed with simulation showing that the ratio of output power versus transistor peak voltage times peak current is 20.4% better than the nominal class <formula formulatype="inline"><tex Notation="TeX">$E$</tex></formula>. The proposed converter and normalized design approach are verified by simulations and measurements done on an experimental prototype.