nils-herrmann/clean_scirepeval huggingface dataset:
Distributed and overlapping representations of faces and objects in ventral temporal cortex.

The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.
Real-time face detection based on YOLO

As a target detection system, YOLO has a fast detection speed and is suitable for target detection in real-time environment. Compared with other similar target detection systems, it has better detection accuracy and faster detection time. This paper is based on YOLO network and applied to face detection. In this paper, YOLO target detection system is applied to face detection. Experimental results show that the face detection method based on YOLO has stronger robustness and faster detection speed. Still in a complex environment can guarantee the high detection accuracy. At the same time, the detection speed can meet real-time detection requirements
Dependency Grammar and Dependency Parsing

Despite a long and venerable tradition in descriptive lingu stics, dependency grammar has until recently played a fairly marginal role both in t heoretical linguistics and in natural language processing. The increasing interes t in dependency-based representations in natural language parsing in recent year s appears to be motivated both by the potential usefulness of bilexical relations in d isambiguation and by the gains in efficiency that result from the more constrained par sing problem for these representations. In this paper, we will review the state of the art in dependenc y-based parsing, starting with the theoretical foundations of dependency gr ammar and moving on to consider both grammar-driven and data-driven methods fo r dependency parsing. We will limit our attention to systems for dependency parsin g i a narrow sense, i.e. systems where the analysis assigned to an input sentenc akes the form of a dependency structure. This means that we will not discuss sy tems that exploit dependency relations for the construction of another type o f r presentation, such as the head-driven parsing models of Collins (1997, 1999). M oreover, we will restrict ourselves to systems for full parsing, which means that we will not deal with systems that produce a partial or underspecified repres ntation of dependency structure, such as Constraint Grammar parsers (Karlsson, 1 990; Karlsson et al., 1995).
Deep learning from temporal coherence in video

This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.
Cancer genome landscapes.

Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.
Claim Synthesis via Predicate Recycling

Computational Argumentation has two main goals the detection and analysis of arguments on the one hand, and the synthesis of arguments on the other. Much attention has been given to the former, but considerably less to the latter. A key component in synthesizing arguments is the synthesis of claims. One way to do so is by employing argumentation mining to detect claims within an appropriate corpus. In general, this appears to be a hard problem. Thus, it is interesting to explore if for the sake of synthesis there may be other ways to generate claims. Here we explore such a method: we extract the predicate of simple, manuallydetected, claims, and attempt to generate novel claims from them. Surprisingly, this simple method yields fairly good results.
Extreme View Synthesis

We present Extreme View Synthesis, a solution for novel view extrapolation when the number of input images is small. Occlusions and depth uncertainty, in this context, are two of the most pressing issues, and worsen as the degree of extrapolation increases. State-of-the-art methods approach this problem by leveraging explicit geometric constraints, or learned priors. Our key insight is that only by modeling both depth uncertainty and image priors can the extreme cases be solved. We first generate a depth probability volume for the novel view and synthesize an estimate of the sought image. Then, we use learned priors combined with depth uncertainty, to refine it. Our method is the first to show visually pleasing results for baseline magnifications of up to 30×.
Image Classification for Ultrasound Fetal Images with Increased Nuchal Translucency during First Trimester Using SVM Classifier

Increased Nuchal Translucency is an indicator of increased risk for Down syndrome, which is identified by measuring Nuchal Translucency from ultrasound fetal images during 11 to 13 +6 weeks of gestation. Increased NT is associated with chromosomal abnormalities. In this study an efficient classification system based on Discrete Wavelet Transform (DWT) is proposed to detect the normal and abnormal images with NT. Feature extraction is an essential pre-processing step for pattern recognition and machine learning problems. In order to classify the ultrasound image accurately, the texture features must be extracted effectively. In the proposed system, wavelet band signature, energy is used as features to classify the ultrasound image for the detection of Down syndrome using Support Vector Machine (SVM) classifier. The experimental results of pre diagnosed database with Discrete wavelet Transform and SVM classifier give best results for classification of Down Syndrome images with Normal NT and abnormal NT.
Flow-Sensitive Type Qualifiers

We present a system for extending standard type systems with flow-sensitive type qualifiers. Users annotate their programs with type qualifiers, and inference checks that the annotations are correct. In our system only the type qualifiers are modeled flow-sensitively---the underlying standard types are unchanged, which allows us to obtain an efficient constraint-based inference algorithm that integrates flow-insensitive alias analysis, effect inference, and ideas from linear type systems to support strong updates. We demonstrate the usefulness of flow-sensitive type qualifiers by finding a number of new locking bugs in the Linux kernel.
Mercator: A scalable, extensible Web crawler

This paper describes Mercator, a scalable, extensible Web crawler written entirely in Java. Scalable Web crawlers are an important component of many Web services, but their design is not well‐documented in the literature. We enumerate the major components of any scalable Web crawler, comment on alternatives and tradeoffs in their design, and describe the particular components used in Mercator. We also describe Mercator's support for extensibility and customizability. Finally, we comment on Mercator's performance, which we have found to be comparable to that of other crawlers for which performance numbers have been published.
Platforms in healthcare innovation ecosystems: The lens of an innovation intermediary

Healthcare innovation has made progressive strides. Innovative solutions now tend to incorporate device integration, data collection and data analysis linked across a diverse range of actors building platform-centric healthcare ecosystems. The interconnectedness and inter-disciplinarity of the ecosystems bring with it a number of vital issues around how to strategically manage such a complex system. This paper highlights the importance of innovation intermediaries particularly in a platform-centric ecosystem such as the healthcare industry. It serves as a reminder of why it is important for healthcare technologists to consider proactive ways to contribute to the innovation ecosystem by creating devices with the platform perspective in mind.
A Binocular, Foveated Active Vision System

This report documents the design and implementation of a binocular, foveated active vision system as part of the Cog project at the MIT Artificial Intelligence Laboratory. The active vision system features a 3 degree of freedom mechanical platform that supports four color cameras, a motion control system, and a parallel network of digital signal processors for image processing. To demonstrate the capabilities of the system, we present results from four sample visual-motor tasks. The author receives support from a National Defense Science and Engineering Graduate Fellowship. Support for this project is provided in part by an ONR/ARPA Vision MURI Grant (No. N00014-95-1-0600).
The influence of personality on Facebook usage, wall postings, and regret

The Five Factor Model of personality has been used extensively in the management and psychology fields to predict attitudes and behaviors. Only recently have researchers begun to examine the role of psychological factors in influencing an individual’s use of technology platforms, such as Facebook. This study uses both a survey of Facebook users and actual Facebook data to uncover why some individuals are more involved in Facebook than others. 219 undergraduate students participated in a survey that assessed their personality and their reported usage of Facebook. Of these, 143 voluntarily befriended the investigator, which gave her access to their actual Facebook sites and objective data on their number of friends, photos, and wall postings. Results showed personality to explain significant amounts of variance over and above gender and Facebook experience in terms of actual number of Facebook friends, the nature of their wall postings and on their level of regret for inappropriate Facebook content.
A Modified AES Based Algorithm for Image Encryption

With the fast evolution of digital data exchange, security information becomes much important in data storage and transmission. Due to the increasing use of images in industrial process, it is essential to protect the confidential image data from unauthorized access. In this paper, we analyze the Advanced Encryption Standard (AES), and we add a key stream generator (A5/1, W7) to AES to ensure improving the encryption performance; mainly for images characterised by reduced entropy. The implementation of both techniques has been realized for experimental purposes. Detailed results in terms of security analysis and implementation are given. Comparative study with traditional encryption algorithms is shown the superiority of the modified algorithm. Cryptography, Encryption, Advanced Encryption Standard (AES), ECB mode, statistical analysis, key stream generator.
Assessing attractiveness in online dating profiles

Online dating systems play a prominent role in the social lives of millions of their users, but little research has considered how users perceive one another through their personal profiles. We examined how users perceive attractiveness in online dating profiles, which provide their first exposure to a potential partner. Participants rated whole profiles and profile components on such qualities as how attractive, extraverted, and genuine and trustworthy they appeared. As past research in the psychology of attraction would suggest, the attractiveness and other qualities of the photograph were the strongest predictors of whole profile attractiveness, but they were not alone: the free-text component also played an important role in predicting overall attractiveness. In turn, numerous other qualities predicted the attractiveness ratings of photos and free-text components, albeit in different ways for men and women. The fixed-choice elements of a profile, however, were unrelated to attractiveness.
Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

The United States spends more than $1B each year on initiatives such as the Amer-ican Community Survey (ACS), a labor-intensive door-to-door study that measures demographic factors 1. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative. Here, we present a method that determines socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles , which enumerated 22M automobiles in total (8% of all automobiles in the US), was used to accurately estimate income, race, education, and voting patterns, with single-precinct resolution. The average US precinct contains approximately 1000 people.
An Injectable 64 nW ECG Mixed-Signal SoC in 65 nm for Arrhythmia Monitoring

A syringe-implantable electrocardiography (ECG) monitoring system is proposed. The noise optimization and circuit techniques in the analog front-end (AFE) enable 31 nA current consumption while a minimum energy computation approach in the digital back-end reduces digital energy consumption by 40%. The proposed SoC is fabricated in 65 nm CMOS and consumes 64 nW while successfully detecting atrial fibrillation arrhythmia and storing the irregular waveform in memory in experiments using an ECG simulator, a live sheep, and an isolated sheep heart.
Social signal processing: Survey of an emerging domain

The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence – the ability to recognize human social signals and social behaviours like turn taking, politeness, and disagreement – in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.
Pad: an alternative approach to the computer interface

We believe that navigation in information spaces is best supported by tapping into our natural spatial and geographic ways of thinking. To this end, we are developing a new computer interface model called Pad. The ongoing Pad project uses a spatial metaphor for computer interface design. It provides an intuitive base for the support of such applications as electronic marketplaces, information services, and on-line collaboration. Pad is an in nite two dimensional information plane that is shared among users, much as a network le system is shared. Objects are organized geographically; every object occupies a well de ned region on the Pad surface. For navigation, Pad uses \portals" magnifyingglasses that can peer into and roam over di erent parts of this single in nite shared desktop; links to speci c items are established and broken continually as the portal's view changes. Portals can recursively look onto other portals. This paradigm enables the sort of peripheral activity generally found in real physical working environments. The apparent size of an object to any user determines the amount of detail it presents. Di erent users can share and view multiple applications while assigning each a desired degree of interaction. Documents can be visually nested and zoomed as they move back and forth between primary and secondary working attention. Things can be peripherally accessible. In this paper we describe the Pad interface. We discuss how to e ciently implement its graphical aspects, and we illustrate some of our initial applications.  Siggraph Copyright
Operating System Profiling via Latency Analysis

Operating systems are complex and their behavior depends on many factors. Source code, if available, does not directly help one to understand the OS's behavior, as the behavior depends on actual workloads and external inputs. Runtime profiling is a key technique to prove new concepts, debug problems, and optimize performance. Unfortunately, existing profiling methods are lacking in important areas---they do not provide enough information about the OS's behavior, they require OS modification and therefore are not portable, or they incur high overheads thus perturbing the profiled OS.
 We developed OSprof: a versatile, portable, and efficient OS profiling method based on latency distributions analysis. OSprof automatically selects important profiles for subsequent visual analysis. We have demonstrated that a suitable workload can be used to profile virtually any OS component. OSprof is portable because it can intercept operations and measure OS behavior from user-level or from inside the kernel without requiring source code. OSprof has typical CPU time overheads below 4%. In this paper we describe our techniques and demonstrate their usefulness through a series of profiles conducted on Linux, FreeBSD, and Windows, including client/server scenarios. We discovered and investigated a number of interesting interactions, including scheduler behavior, multi-modal I/O distributions, and a previously unknown lock contention, which we fixed.
Autoenucleation in a 84-year-old dementia patient

Autoenucleation is a severe, rare form of self-mutilation. The majority of cases have been reported in the 15- to 60-year age group, usually in psychiatric patients with a history of depression or schizophrenia, sometimes caused by drug abuse. Case report We report a case of left-sided autoenucleation in an 84-year-old dementia patient suffering from reactive depression. Medical reports mentioned a suicide attempt 2 weeks prior to the incident, whereupon the patient was admitted to the locked ward of a psychiatric hospital. During one night of inpatient stay, the patient manually autoenucleated his left eye. Inspection of the enucleated organ revealed a completely intact globe with an attached optic nerve 5.5 cm in length. The orbit was filled with a massive haematoma. A contrast-enhanced computed tomography (CT) scan showed an orbital haematoma, absence of the left globe and optic nerve and a chiasmatic lesion, accompanied by an intracranial bleeding into the subarachnoid space. Primary wound closure was performed without complications. Visual acuity of the right eye could not be tested due to the patient's lack of cooperation.  To the best of our knowledge, this is the only reported case of an elderly patient with primary dementia who performed autoenucleation. Other aspects, such as patient history, suicide attempt, manual eye extraction and chiasma lesion are similar to cases reported earlier. The identification and evaluation of intracranial bleedings and chiasmatic lesions that can be associated with autoenucleation requires a contrast-enhanced CT, especially if a long optic nerve fragment is attached to the enucleated globe.
On temporal-spatial realism in the virtual reality environment

The Polhemus Isotrak is often used as an orientation and position tracking device in virtual reality environments. When it is used to dynamically determine the user’s viewpoint and line of sight ( e.g. in the case of a head mounted display) the noise and delay in its measurement data causes temporal-spatial distortion, perceived by the user as jittering of images and lag between head movement and visual feedback. To tackle this problem, we first examined the major cause of the distortion, and found that the lag felt by the user is mainly due to the delay in orientation data, and the jittering of images is caused mostly by the noise in position data. Based on these observations, a predictive Kalman filter was designed to compensate for the delay in orientation data, and an anisotropic low pass filter was devised to reduce the noise in position data. The effectiveness and limitations of both approaches were then studied, and the results shown to be satisfactory.
Scientific Paper Summarization Using Citation Summary Networks

Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others’ viewpoint of the target article’s contributions and the study of its citation summary network using a clustering approach.
Prior-free and prior-dependent regret bounds for Thompson Sampling

We consider the stochastic multi-armed bandit problem with a prior distribution on the reward distributions. We are interested in studying prior-free and prior-dependent regret bounds, very much in the same spirit than the usual distribution-free and distribution-dependent bounds for the non-Bayesian stochastic bandit. We first show that Thompson Sampling attains an optimal prior-free bound in the sense that for any prior distribution its Bayesian regret is bounded from above by 14√nK. This result is unimprovable in the sense that there exists a prior distribution such that any algorithm has a Bayesian regret bounded from below by 1 over 20√nK. We also study the case of priors for the setting of Bubeck et al. [2013] (where the optimal mean is known as well as a lower bound on the smallest gap) and we show that in this case the regret of Thompson Sampling is in fact uniformly bounded over time, thus showing that Thompson Sampling can greatly take advantage of the nice properties of these priors.