nils-herrmann/clean_scirepeval huggingface dataset:
A Map of Recent Positive Selection in the Human Genome

The identification of signals of very recent positive selection provides information about the adaptation of modern humans to local conditions. We report here on a genome-wide scan for signals of very recent positive selection in favor of variants that have not yet reached fixation. We describe a new analytical method for scanning single nucleotide polymorphism (SNP) data for signals of recent selection, and apply this to data from the International HapMap Project. In all three continental groups we find widespread signals of recent positive selection. Most signals are region-specific, though a significant excess are shared across groups. Contrary to some earlier low resolution studies that suggested a paucity of recent selection in sub-Saharan Africans, we find that by some measures our strongest signals of selection are from the Yoruba population. Finally, since these signals indicate the existence of genetic variants that have substantially different fitnesses, they must indicate loci that are the source of significant phenotypic variation. Though the relevant phenotypes are generally not known, such loci should be of particular interest in mapping studies of complex traits. For this purpose we have developed a set of SNPs that can be used to tag the strongest approximately 250 signals of recent selection in each population.
Unlocking the expressivity of point lights

Small point lights (e.g., LEDs) are used as indicators in a wide variety of devices today, from digital watches and toasters, to washing machines and desktop computers. Although exceedingly simple in their output - varying light intensity over time - their design space can be rich. Unfortunately, a survey of contemporary uses revealed that the vocabulary of lighting expression in popular use today is small, fairly unimaginative, and generally ambiguous in meaning. In this paper, we work through a structured design process that points the way towards a much richer set of expressive forms and more effective communication for this very simple medium. In this process, we make use of five different data gathering and evaluation components to leverage the knowledge, opinions and expertise of people outside our team. Our work starts by considering what information is typically conveyed in this medium. We go on to consider potential expressive forms -- how information might be conveyed. We iteratively refine and expand these sets, concluding with ideas gathered from a panel of designers. Our final step was to make use of thousands of human judgments, gathered in a crowd-sourced fashion (265 participants), to measure the suitability of different expressive forms for conveying different information content. This results in a set of recommended light behaviors that mobile devices, such as smartphones, could readily employ.
An approach to abstractive text summarization

Abstractive summarization is the technique of generating a summary of a text from its main ideas, not by copying verbatim most salient sentences from text. This is an important and challenge task in natural language processing. In this paper, we propose an approach to abstractive text summarization based on discourse rules, syntactic constraints, and word graph. Discourse rules and syntactic constraints are used in the process of generating sentences from keywords. Word graph is used in the sentence combination process to represent word relations in the text and to combine several sentences into one. Experimental results show that our approach is promising in solving the abstractive summarization task.
Uncovering Privacy Leakage in BLE Network Traffic of Wearable Fitness Trackers

There has been a tremendous increase in popularity and adoption of wearable fitness trackers. These fitness trackers predominantly use Bluetooth Low Energy (BLE) for communicating and syncing the data with user's smartphone. This paper presents a measurement-driven study of possible privacy leakage from BLE communication between the fitness tracker and the smartphone. Using real BLE traffic traces collected in the wild and in controlled experiments, we show that majority of the fitness trackers use unchanged BLE address while advertising, making it feasible to track them. The BLE traffic of the fitness trackers is found to be correlated with the intensity of user's activity, making it possible for an eavesdropper to determine user's current activity (walking, sitting, idle or running) through BLE traffic analysis. Furthermore, we also demonstrate that the BLE traffic can represent user's gait which is known to be distinct from user to user. This makes it possible to identify a person (from a small group of users) based on the BLE traffic of her fitness tracker. As BLE-based wearable fitness trackers become widely adopted, our aim is to identify important privacy implications of their usage and discuss prevention strategies.
Foldable and self-deployable pocket sized quadrotor

Aerial robots provide valuable support in several high-risk scenarios thanks to their capability to quickly fly to locations dangerous or even inaccessible to humans. In order to fully benefit from these features, aerial robots should be easy to transport and rapid to deploy. With this aim, this paper focuses on the development of a novel pocket sized quadrotor with foldable arms. The quadrotor can be packaged for transportation by folding its arms around the main frame. Before flight, the quadrotor's arms self-deploy in 0.3 seconds thanks to the torque generated by the propellers. The paper describes the design strategies used for developing lightweight, stiff and self-deployable foldable arms for miniature quadrotors. The arms are manufactured according to an origami technique with a foldable multi-layer material. A prototype of the quadrotor is presented as a proof of concept and performance of the system is assessed.
Practical and flexible path planning for car-like mobile robot using maximal-curvature cubic spiral

This paper presents a nonholonomic path planning method, aiming at taking into considerations of curvature constraint, length minimization, and computational demand, for car-like mobile robot based on cubic spirals. The generated path is made up of at most five segments: at most two maximal-curvature cubic spiral segments with zero curvature at both ends in connection with up to three straight line segments. A numerically efficient process is presented to generate a Cartesian shortest path among the family of paths considered for a given pair of start and destination configurations. Our approach is resorted to minimization via linear programming over the sum of length of each path segment of paths synthesized based on minimal locomotion cubic spirals linking start and destination orientations through a selected intermediate orientation. The potential intermediate configurations are not necessarily selected from the symmetric mean circle for non-parallel start and destination orientations. The novelty of the presented path generation method based on cubic spirals is: (i) Practical: the implementation is straightforward so that the generation of feasible paths in an environment free of obstacles is efficient in a few milliseconds; (ii) Flexible: it lends itself to various generalizations: readily applicable to mobile robots capable of forward and backward motion and Dubins’ car (i.e. car with only forward driving capability); well adapted to the incorporation of other constraints like wall-collision avoidance encountered in robot soccer games; straightforward extension to planning a path connecting an ordered sequence of target configurations in simple obstructed environment.
Early Prediction of Cardiac Arrest (Code Blue) using Electronic Medical Records

Code Blue is an emergency code used in hospitals to indicate when a patient goes into cardiac arrest and needs resuscitation. When Code Blue is called, an on-call medical team staffed by physicians and nurses is paged and rushes in to try to save the patient's life. It is an intense, chaotic, and resource-intensive process, and despite the considerable effort, survival rates are still less than 20% . Research indicates that patients actually start showing clinical signs of deterioration some time before going into cardiac arrest [1][2[][3], making early prediction, and possibly intervention, feasible. In this paper, we describe our work, in partnership with NorthShore University HealthSystem, that preemptively flags patients who are likely to go into cardiac arrest, using signals extracted from demographic information, hospitalization history, vitals and laboratory measurements in patient-level electronic medical records. We find that early prediction of Code Blue is possible and when compared with state of the art existing method used by hospitals (MEWS - Modified Early Warning Score)[4], our methods perform significantly better. Based on these results, this system is now being considered for deployment in hospital settings.
A lightweight anomaly detection framework for medical wireless sensor networks

In this paper, we focus on online detection and isolation of erroneous values reported by medical wireless sensors. We propose a lightweight approach for online anomaly detection in collected data, able to raise alarms only when patients enter in emergency situation and to discard faulty measurements. The proposed approach is based on Haar wavelet decomposition and Hampel filter for spatial analysis, and on boxplot for temporal analysis. Our objective is to reduce false alarms resulted from unreliable measurements. We apply our proposed approach on real physiological data set. Our experimental results prove the effectiveness of our approach to achieve good detection accuracy with low false alarm rate.
Laser Scanner Based Slam in Real Road and Traffic Environment

In this paper we will present a SLAM algorithm we have recently developed for our needs in autonomous automotive applications. Our approach has the particularity of making use exclusively of laser scanners to achieve our goals without using any other type of sensors or source of information. We concentrated on developing a self-contained system that could be placed on any kind of mobile platform and work in any kind of dynamic environment; this is why too at this point our approach does not make use of any model of the vehicle. Our SLAM system has been tested with success both on a car at full speed on a road and a human evolving indoors. We will present here the challenges we face that pushed us to develop the algorithm, the solutions we are exploring, discuss experimental results and suggest areas of future work.
Active-source-pump (ASP) technique for ESD design window expansion and ultra-thin gate oxide protection in sub-90nm technologies

This paper presents a novel active-source-pump (ASP) circuit technique to significantly lower the ESD sensitivity of ultrathin gate inputs in advanced sub-90nm CMOS technologies. As demonstrated by detailed experimental analysis, an ESD design window expansion of more than 100% can be achieved. This revives conventional ESD solutions for ultrasensitive input protection also enabling low-capacitance RF protection schemes with a high ESD design flexibility at IC-level. ASP IC application examples, and the impact of ASP on normal RF operation performance, are discussed.
Bosphorus Database for 3D Face Analysis

A new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions is presented in this paper. This database is unique from three aspects: i) the facial expressions are composed of judiciously selected subset of Action Units as well as the six basic emotions, and many actors/actresses are incorporated to obtain more realistic expression data; ii) a rich set of head pose variations are available; and iii) different types of face occlusions are included. Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis.
Data Quality: Some Comments on the NASA Software Defect Datasets

Self-evidently empirical analyses rely upon the quality of their data. Likewise, replications rely upon accurate reporting and using the same rather than similar versions of datasets. In recent years, there has been much interest in using machine learners to classify software modules into defect-prone and not defect-prone categories. The publicly available NASA datasets have been extensively used as part of this research. This short note investigates the extent to which published analyses based on the NASA defect datasets are meaningful and comparable. We analyze the five studies published in the IEEE Transactions on Software Engineering since 2007 that have utilized these datasets and compare the two versions of the datasets currently in use. We find important differences between the two versions of the datasets, implausible values in one dataset and generally insufficient detail documented on dataset preprocessing. It is recommended that researchers 1) indicate the provenance of the datasets they use, 2) report any preprocessing in sufficient detail to enable meaningful replication, and 3) invest effort in understanding the data prior to applying machine learners.
Dissecting GPU Memory Hierarchy Through Microbenchmarking

Memory access efficiency is a key factor in fully utilizing the computational power of graphics processing units (GPUs). However, many details of the GPU memory hierarchy are not released by GPU vendors. In this paper, we propose a novel fine-grained microbenchmarking approach and apply it to three generations of NVIDIA GPUs, namely Fermi, Kepler, and Maxwell, to expose the previously unknown characteristics of their memory hierarchies. Specifically, we investigate the structures of different GPU cache systems, such as the data cache, the texture cache and the translation look-aside buffer (TLB). We also investigate the throughput and access latency of GPU global memory and shared memory. Our microbenchmark results offer a better understanding of the mysterious GPU memory hierarchy, which will facilitate the software optimization and modelling of GPU architectures. To the best of our knowledge, this is the first study to reveal the cache properties of Kepler and Maxwell GPUs, and the superiority of Maxwell in shared memory performance under bank conflict.
A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes

During the last years a wide range of algorithms and devices have been made available to easily acquire range images. The increasing abundance of depth data boosts the need for reliable and unsupervised analysis techniques, spanning from part registration to automated segmentation. In this context, we focus on the recognition of known objects in cluttered and incomplete 3D scans. Locating and fitting a model to a scene are very important tasks in many scenarios such as industrial inspection, scene understanding, medical imaging and even gaming. For this reason, these problems have been addressed extensively in the literature. Several of the proposed methods adopt local descriptor-based approaches, while a number of hurdles still hinder the use of global techniques. In this paper we offer a different perspective on the topic: We adopt an evolutionary selection algorithm that seeks global agreement among surface points, while operating at a local level. The approach effectively extends the scope of local descriptors by actively selecting correspondences that satisfy global consistency constraints, allowing us to attack a more challenging scenario where model and scene have different, unknown scales. This leads to a novel and very effective pipeline for 3D object recognition, which is validated with an extensive set of experiments and comparisons with recent techniques at the state of the art.
Simulating Our LifeSteps by Example

During the past few decades, a number of effective methods for indexing, query processing, and knowledge discovery in moving object databases have been proposed. An interesting research direction that has recently emerged handles semantics of movement instead of raw spatio-temporal data. Semantic annotations, such as “stop,” “move,” “at home,” “shopping,” “driving,” and so on, are either declared by the users (e.g., through social network apps) or automatically inferred by some annotation method and are typically presented as textual counterparts along with spatial and temporal information of raw trajectories. It is natural to argue that such “spatio-temporal-textual” sequences, called semantic trajectories, form a realistic representation model of the complex everyday life (hence, mobility) of individuals. Towards handling semantic trajectories of moving objects in Semantic Mobility Databases, the lack of real datasets leads to the need to design realistic simulators. In the context of the above discussion, the goal of this work is to realistically simulate the mobility life of a large-scale population of moving objects in an urban environment. Two simulator variations are presented: the core Hermoupolis simulator is parametric driven (i.e., user-defined parameters tune every movement aspect), whereas the expansion of the former, called Hermoupolisby-example, follows the generate-by-example paradigm and is self-tuned by looking inside a real small (sample) dataset. We stress test our proposal and demonstrate its novel characteristics with respect to related work.
A measurement study of interference modeling and scheduling in low-power wireless networks

Accurate interference models are important for use in transmission scheduling algorithms in wireless networks. In this work, we perform extensive modeling and experimentation on two 20-node TelosB motes testbeds -- one indoor and the other outdoor -- to compare a suite of interference models for their modeling accuracies. We first empirically build and validate the physical interference model via a packet reception rate vs. SINR relationship using a measurement driven method. We then similarly instantiate other simpler models, such as hop-based, range-based, protocol model, etc. The modeling accuracies are then evaluated on the two testbeds using transmission scheduling experiments. We observe that while the physical interference model is the most accurate, it is still far from perfect, providing a 90-percentile error about 20-25% (and 80 percentile error 7-12%), depending on the scenario. The accuracy of the other models is worse and scenario-specific. The second best model trails the physical model by roughly 12-18 percentile points for similar accuracy targets. Somewhat similar throughput performance differential between models is also observed when used with greedy scheduling algorithms. Carrying on further, we look closely into the the two incarnations of the physical model -- 'thresholded' (conservative, but typically considered in literature) and 'graded' (more realistic). We show via solving the one shot scheduling problem, that the graded version can improve `expected throughput' over the thresholded version by scheduling imperfect links.
A Music Recommendation System Based on Music Data Grouping and User Interests

With the growth of the World Wide Web, a large amount of music data is available on the Internet. In addition to searching expected music objects for users, it becomes necessary to develop a recommendation service. In this paper, we design the Music Recommendation System (MRS) to provide a personalized service of music recommendation. The music objects of MIDI format are first analyzed. For each polyphonic music object, the representative track is first determined, and then six features are extracted from this track. According to the features, the music objects are properly grouped. For users, the access histories are analyzed to derive user interests. The content-based, collaborative and statistics-based recommendation methods are proposed, which are based on the favorite degrees of the users to the music groups. A series of experiments are carried out to show that our approach is feasible.
Free Will in Scientific Psychology.

Some actions are freer than others, and the difference is palpably important in terms of inner process, subjective perception, and social consequences. Psychology can study the difference between freer and less free actions without making dubious metaphysical commitments. Human evolution seems to have created a relatively new, more complex form of action control that corresponds to popular notions of free will. It is marked by self-control and rational choice, both of which are highly adaptive, especially for functioning within culture. The processes that create these forms of free will may be biologically costly and therefore are only used occasionally, so that people are likely to remain only incompletely self-disciplined, virtuous, and rational.
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics

Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent’s behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the solution of the inverse problem is significantly influenced by both. Current IRL approaches assume that if the transition model is unknown, additional samples from the system’s dynamics are accessible, or the observed behavior provides enough samples of the system’s dynamics to solve the inverse problem accurately. These assumptions are often not satisfied. To overcome this, we present a gradient-based IRL approach that simultaneously estimates the system’s dynamics. By solving the combined optimization problem, our approach takes into account the bias of the demonstrations, which stems from the generating policy. The evaluation on a synthetic MDP and a transfer learning task shows improvements regarding the sample efficiency as well as the accuracy of the estimated reward functions and transition models.
Compact CPW-Fed Tri-Band Printed Antenna With Meandering Split-Ring Slot for WLAN/WiMAX Applications

A novel compact tri-band printed antenna for WLAN and WiMAX applications is presented. The proposed antenna consists of a modified rectangular slot, a pair of symmetrical inverted-L strips, and a Y-shaped monopole radiator with a meandering split-ring slot. Tuning the locations and the sizes of these structures, three distinct current paths can be produced at three independent frequency bands, respectively. Based on this concept, a prototype of the tri-band antenna is further fabricated and measured. The experimental and numerical results show that the antenna has impedance bandwidth (for return loss less than 10 dB) of 430 MHz (2.33-2.76 GHz), 730 MHz (3.05-3.88 GHz), and 310 MHz (5.57-5.88 GHz), which can cover both the WLAN 2.4/5.8-GHz bands and the WiMAX 2.5/3.5-GHz bands.