nils-herrmann/clean_scirepeval huggingface dataset:
Why Are Autism Spectrum Conditions More Prevalent in Males?

Autism Spectrum Conditions (ASC) are much more common in males, a bias that may offer clues to the etiology of this condition. Although the cause of this bias remains a mystery, we argue that it occurs because ASC is an extreme manifestation of the male brain. The extreme male brain (EMB) theory, first proposed in 1997, is an extension of the Empathizing-Systemizing (E-S) theory of typical sex differences that proposes that females on average have a stronger drive to empathize while males on average have a stronger drive to systemize. In this first major update since 2005, we describe some of the evidence relating to the EMB theory of ASC and consider how typical sex differences in brain structure may be relevant to ASC. One possible biological mechanism to account for the male bias is the effect of fetal testosterone (fT). We also consider alternative biological theories, the X and Y chromosome theories, and the reduced autosomal penetrance theory. None of these theories has yet been fully confirmed or refuted, though the weight of evidence in favor of the fT theory is growing from converging sources (longitudinal amniocentesis studies from pregnancy to age 10 years old, current hormone studies, and genetic association studies of SNPs in the sex steroid pathways). Ultimately, as these theories are not mutually exclusive and ASC is multi-factorial, they may help explain the male prevalence of ASC.
A new brain region for coordinating speech articulation

HUMAN speech requires complex planning and coordination of mouth and tongue movements. Certain types of brain injury can lead to a condition known as apraxia of speech, in which patients are impaired in their ability to coordinate speech movements but their ability to perceive speech sounds, including their own errors, is unaffected1,3. The brain regions involved in coordinating speech, however, remain largely unknown. In this study, brain lesions of 25 stroke patients with a disorder in the motor planning of articulatory movements were compared with lesions of 19 patients without such deficits. A robust double dissociation was found between these two groups. All patients with articulatory planning deficits had lesions that included a discrete region of the left precentral gyms of the insula, a cortical area beneath the frontal and temporal lobes. This area was completely spared in all patients without these articulation deficits. Thus this area seems to be specialized for the motor planning of speech.
A Scalable Global Model for Summarization

We present an Integer Linear Program for exact inference under a maximum coverage model for automatic summarization. We compare our model, which operates at the subsentence or “concept”-level, to a sentencelevel model, previously solved with an ILP. Our model scales more efficiently to larger problems because it does not require a quadratic number of variables to address redundancy in pairs of selected sentences. We also show how to include sentence compression in the ILP formulation, which has the desirable property of performing compression and sentence selection simultaneously. The resulting system performs at least as well as the best systems participating in the recent Text Analysis Conference, as judged by a variety of automatic and manual content-based metrics.
A Circuit for Energy Harvesting Using On-Chip Solar Cells

This paper addresses on-chip solar energy harvesting and proposes a circuit that can be employed to generate high voltages from integrated photodiodes. The proposed circuit uses a switched-inductor approach to avoid stacking photodiodes to generate high voltages. The effect of parasitic photodiodes present in integrated circuits (ICs) is addressed and a solution to minimize their impact is presented. The proposed circuit employs two switch transistors and two off-chip components: an inductor and a capacitor. A theoretical analysis of a switched-inductor dc-dc converter is carried out and a mathematical model of the energy harvester is developed. Measurements taken from a fabricated IC are presented and shown to be in good agreement with hardware measurements. Measurement results show that voltages of up to 2.81 V (depending on illumination and loading conditions) can be generated from a single integrated photodiode. The energy harvester circuit achieves a maximum conversion efficiency of 59%.
Spam detection using text clustering

We propose a new spam detection technique using the text clustering based on vector space model. Our method computes disjoint clusters automatically using a spherical k-means algorithm for all spam/non-spam mails and obtains centroid vectors of the clusters for extracting the cluster description. For each centroid vectors, the label (`spam' or `non-spam') is assigned by calculating the number of spam email in the cluster. When new mail arrives, the cosine similarity between the new mail vector and centroid vector is calculated. Finally, the label of the most relevant cluster is assigned to the new mail. By using our method, we can extract many kinds of topics in spam/non-spam email and detect the spam email efficiently. In this paper, we describe the our spam detection system and show the result of our experiments using the Ling-Spam test collection
Safe semi-autonomous control with enhanced driver modeling

During semi-autonomous driving, threat assessment is used to determine when controller intervention that overwrites or corrects the driver's input is required. Since today's semi-autonomous systems perform threat assessment by predicting the vehicle's future state while treating the driver's input as a disturbance, controller intervention is limited to just emergency maneuvers. In order to improve vehicle safety and reduce the aggressiveness of maneuvers, threat assessment must occur over longer prediction horizons where driver's behavior cannot be neglected. We propose a framework that divides the problem of semi-autonomous control into two components. The first component reliably predicts the vehicle's potential behavior by using empirical observations of the driver's pose. The second component determines when the semi-autonomous controller should intervene. To quantitatively measure the performance of the proposed approach, we define metrics to evaluate the infor-mativeness of the prediction and the utility of the intervention procedure. A multi-subject driving experiment illustrates the usefulness, with respect to these metrics, of incorporating the driver's pose while designing a semi-autonomous system.
Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application

We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
Modeling and querying data in NoSQL databases

Relational databases are providing storage for several decades now. However for today's interactive web and mobile applications the importance of flexibility and scalability in data model can not be over-stated. The term NoSQL broadly covers all non-relational databases that provide schema-less and scalable model. NoSQL databases which are also termed as Internetage databases are currently being used by Google, Amazon, Facebook and many other major organizations operating in the era of Web 2.0. Different classes of NoSQL databases namely key-value pair, document, column-oriented and graph databases enable programmers to model the data closer to the format as used in their application. In this paper, data modeling and query syntax of relational and some classes of NoSQL databases have been explained with the help of an case study of a news website like Slashdot.
A chain-cluster based routing algorithm for wireless sensor networks

Wireless sensor networks (WSNs) are an emerging technology for monitoring physical world. Different from the traditional wireless networks and ad hoc networks, the energy constraint of WSNs makes energy saving become the most important goal of various routing algorithms. For this purpose, a cluster based routing algorithm LEACH (low energy adaptive clustering hierarchy) has been proposed to organize a sensor network into a set of clusters so that the energy consumption can be evenly distributed among all the sensor nodes. Periodical cluster head voting in LEACH, however, consumes non-negligible energy and other resources. While another chain-based algorithm PEGASIS (powerefficient gathering in sensor information systems) can reduce such energy consumption, it causes a longer delay for data transmission. In this paper, we propose a routing algorithm called CCM (Chain-Cluster based Mixed routing), which makes full use of the advantages of LEACH and PEGASIS, and provide improved performance. It divides a WSN into a few chains and runs in two stages. In the first stage, sensor nodes in each chain transmit data to their own chain head node in parallel, using an improved chain routing protocol. In the second stage, all chain head nodes group as a cluster in a selforganized manner, where they transmit fused data to a voted cluster head using the cluster based routing. Experimental F. Tang (B) · M. Guo · Y. Ma Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China e-mail: tang-fl@cs.sjtu.edu.cn I. You School of Information Science, Korean Bible University, Seoul, South Korea F. Tang · S. Guo School of Computer Science and Engineering, The University of Aizu, Fukushima 965-8580, Japan results demonstrate that our CCM algorithm outperforms both LEACH and PEGASIS in terms of the product of consumed energy and delay, weighting the overall performance of both energy consumption and transmission delay.
An open source power system analysis toolbox

This paper describes the Power System Analysis Toolbox (PSAT), an open source Matlab and GNU/Octave-based software package for analysis and design of small to medium size electric power systems. PSAT includes power flow, continuation power flow, optimal power flow, small-signal stability analysis, and time-domain simulation, as well as several static and dynamic models, including nonconventional loads, synchronous and asynchronous machines, regulators, and FACTS. PSAT is also provided with a complete set of user-friendly graphical interfaces and a Simulink-based editor of one-line network diagrams. Basic features, algorithms, and a variety of case studies are presented in this paper to illustrate the capabilities of the presented tool and its suitability for educational and research purposes.
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation

Visual Context Textual Context Oh my gosh, i’m so buying this shirt. I found a cawaii bird. Stocking up!! Only reason I come to carnival.  Where did you see this for sale? Are you going to collect some feathers? Ayee! what the prices looking like? Oh my God. How the hell do you even eat that? Response Midwest sports There are so many crows here I’d be surprised if I never found one. Only like 10-20% off..I think I’m gonna wait a little longer. They are the greatest things ever chan. I could eat 5!
Current limitations and opportunities in mobile augmented reality applications

Mobile AR has evolved from the bulkiness of head-mounted device and backpack device to smart device (smartphone, tablet etc.). To date, the current implementation has made what AR is today. However, the advancement of AR technology has met with limitation and challenges on its own, which resulted in not able to reach mass-market. This paper in turn presents current limitations and challenges that need to overcome. We have done a review based on past research papers on limitation in technical (hardware, algorithms and interaction technique) and non-technical (social acceptance, privacy and usefulness) aspects of developing and implementing mobile augmented reality applications. We also presented some future opportunities in mobile AR applications.
Construction of neural nets using the radon transform

The authors present a method for constructing a feedforward neural net implementing an arbitrarily good approximation to any L/sub 2/ function over (-1, 1)/sup n/. The net uses n input nodes, a single hidden layer whose width is determined by the function to be implemented and the allowable mean square error, and a linear output neuron. Error bounds and an example are given for the method.<<ETX>>
A Light-weight Compaction Tree to Reduce I / O Amplification toward Efficient Key-Value Stores

Log-Structure merge tree (LSM-tree) has been one of the mainstream indexes in key-value systems supporting a variety of write-intensive Internet applications in today’s data centers. However, the performance of LSM-tree is seriously hampered by constantly occurring compaction procedures, which incur significant write amplification and degrade the write throughput. To alleviate the performance degradation caused by compactions, we introduce a light-weight compaction tree (LWCtree), a variant of LSM-tree index optimized for minimizing the write amplification and maximizing the system throughput. The light-weight compaction drastically decreases write amplification by appending data in a table and only merging the metadata that has much smaller size. We implement three key-value LWC-stores on different storage mediums including Shingled Magnetic Recording (SMR) drives, Solid State Drives (SSD) and conventional HDDs, using our proposed LWC-tree. The LWCstore is particularly optimized for SMR drives as it eliminates the multiplicative I/O amplification from both LSM-trees and SMR devices. Due to light-weight compaction procedures, the LWCstore reduces the write amplification by up to 5× compared to the popular LevelDB key-value store. Moreover, the write throughput of the LWC-tree on SMR drives is significantly improved by up to 467% even compared with LevelDB on HDDs. Furthermore, the LWC-tree has wide applicability and it delivers impressive performance improvement in various conditions, including different storage mediums (i.e., SMR, HDD, SSD), varying value sizes and access patterns (i.e., uniform, Zipfian and latest key distribution).
A low-cost harmonic radar for tracking very small tagged amphibians

A low-cost, hand-held harmonic radar is described for tracking tagged amphibians weighting less than a gram during a cryptic period of their life history. The radar is based on inexpensive, commercial 5.8 GHz wireless communications and 11.6 GHz satellite television technology. The performance of the system was accurately predicted from laboratory measurements by defining an appropriate harmonic tag conversion efficiency. The harmonic radar has a demonstrated maximum tag detection range of 20 ft. The best performance was achieved with an asymmetric, dielectric sleeved dipole with a 1:2 arm length ratio.
Driving coach: A smartphone application to evaluate driving efficient patterns

In spite of several technical advances made in recent years by the automotive industry, the driver's behaviour still influences significantly the overall fuel consumption. With the rise of smartphones adoption, there are also new opportunities to increase the awareness to this issue. The main aim of this paper is to present a new smartphone application that will help drivers reduce the fuel consumption of their vehicles. This is accomplished by using the smartphone's sensors and the vehicle state to detect the driving pattern and suggest new behaviours in real time that will lead to a more efficient driving experience. The preliminary results show the potential for significant energy savings and their relevance for changing the drivers' behaviour.
Do you identify as a gamer? Gender, race, sexuality, and gamer identity

The demand for minority representation in video games often focuses on proving that members of marginalized groups are gamers. In turn, it is asserted that the gaming industry should focus on appealing to these players via targeted content. Being targeted as a gamer, however, does not a gamer make. Identity as a gamer intersects with other identities like gender, race, and sexuality. Negative connotations about gaming lead people to not identify as gamers, and even to not play video games. This article concludes, based on interview data, that those invested in diversity in video games must focus their attention on the construction of the medium, and not the construction of the audience as such. This shift in academic attention is necessary to develop arguments for representation in games that do not rely on marking groups as specific kinds of gaming markets via identifiers like gender, race, and sexuality.
Optimization of SSVEP brain responses with application to eight-command Brain–Computer Interface

This study pursues the optimization of the brain responses to small reversing patterns in a Steady-State Visual Evoked Potentials (SSVEP) paradigm, which could be used to maximize the efficiency of applications such as Brain-Computer Interfaces (BCI). We investigated the SSVEP frequency response for 32 frequencies (5-84 Hz), and the time dynamics of the brain response at 8, 14 and 28 Hz, to aid the definition of the optimal neurophysiological parameters and to outline the onset-delay and other limitations of SSVEP stimuli in applications such as our previously described four-command BCI system. Our results showed that the 5.6-15.3 Hz pattern reversal stimulation evoked the strongest responses, peaking at 12 Hz, and exhibiting weaker local maxima at 28 and 42 Hz. After stimulation onset, the long-term SSVEP response was highly non-stationary and the dynamics, including the first peak, was frequency-dependent. The evaluation of the performance of a frequency-optimized eight-command BCI system with dynamic neurofeedback showed a mean success rate of 98%, and a time delay of 3.4s. Robust BCI performance was achieved by all subjects even when using numerous small patterns clustered very close to each other and moving rapidly in 2D space. These results emphasize the need for SSVEP applications to optimize not only the analysis algorithms but also the stimuli in order to maximize the brain responses they rely on.
Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms

We consider a single-product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve) is not known. We consider two instances of this problem: (i) a setting where the demand function is assumed to belong to a known parametric family with unknown parameter values; and (ii) a setting where the demand function is assumed to belong to a broad class of functions that need not admit any parametric representation. In each case we develop policies that learn the demand function “on the fly,” and optimize prices based on that. The performance of these algorithms is measured in terms of the regret: the revenue loss relative to the maximal revenues that can be extracted when the demand function is known prior to the start of the selling season. We derive lower bounds on the regret that hold for any admissible pricing policy, and then show that our proposed algorithms achieve a regret that is “close” to this lower bound. The magnitude of the regret can be interpreted as the economic value of prior knowledge on the demand function, manifested as the revenue loss due to model uncertainty.
A cooperative approach for handshake detection based on body sensor networks

The handshake gesture is an important part of the social etiquette in many cultures. It lies at the core of many human interactions, either in formal or informal settings: exchanging greetings, offering congratulations, and finalizing a deal are all activities that typically either start or finish with a handshake. The automated detection of a handshake can enable wide range of pervasive computing scanarios; in particular, different types of information can be exchanged and processed among the handshaking persons, depending on the physical/logical contexts where they are located and on their mutual acquaintance. This paper proposes a novel handshake detection system based on body sensor networks consisting of a resource-constrained wrist-wearable sensor node and a more capable base station. The system uses an effective collaboration technique among body sensor networks of the handshaking persons which minimizes errors associated with the application of classification algorithms and improves the overall accuracy in terms of the number of false positives and false negatives.
Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action Recognition

Given a video or time series of skeleton data, action recognition systems perform classification using cues such as motion, appearance, and pose. For the past decade, actions have been modeled using low-level feature representations such as Bag of Features. More recent work has shown that mid-level representations that model body part movements (e.g., hand moving forward) can be very effective. However, these mid-level features are usually hand-crafted and the dictionary of representative features is learned using ad-hoc heuristics. While automatic feature learning methods such as supervised sparse dictionary learning or neural networks can be applied to learn feature representation and action classifiers jointly, the resulting features are usually uninterpretable. In contrast, our goal is to develop a principled feature learning framework to learn discriminative and interpretable skeletal motion patterns for action recognition. For this purpose, we propose a novel body-part motion based feature called Moving Poselet, which corresponds to a specific body part configuration undergoing a specific movement. We also propose a simple algorithm for jointly learning Moving Poselets and action classifiers. Experiments on MSR Action3D, MSR DailyActivity3D and Berkeley MHAD datasets show that our two-layer model outperforms other two-layer models using hand-crafted features, and achieves results comparable to those of recent multi-layer Hierarchical Recurrent Neural Network (HRNN) models, which use multiple layers of RNN to model the human body hierarchy.