nils-herrmann/clean_scirepeval huggingface dataset:
Membrane budding and scission by the ESCRT machinery: it's all in the neck

The endosomal sorting complexes required for transport (ESCRTs) catalyse one of the most unusual membrane remodelling events in cell biology. ESCRT-I and ESCRT-II direct membrane budding away from the cytosol by stabilizing bud necks without coating the buds and without being consumed in the buds. ESCRT-III cleaves the bud necks from their cytosolic faces. ESCRT-III-mediated membrane neck cleavage is crucial for many processes, including the biogenesis of multivesicular bodies, viral budding, cytokinesis and, probably, autophagy. Recent studies of ultrastructures induced by ESCRT-III overexpression in cells and the in vitro reconstitution of the budding and scission reactions have led to breakthroughs in understanding these remarkable membrane reactions.
Collaborative Localization and Navigation in Heterogeneous UAV swarms: Demo Abstract

Resilient localization and navigation for autonomous Unmanned Aerial Vehicles (UAVs) still remains a challenge in certain scenarios, like GPS-deprived environments such as indoors or urban canyons. In this work, we explore a heterogeneous UAV swarm design, in which a small number of sensor and computationally powerful UAVs collaborate with the remaining resource-constrained UAVs to guarantee optimal localization accuracy.
Query Optimization in Database Systems

Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logic-based and semantic transformations, fast implementations of basic operations, and combinatorial or heuristic algorithms for generating alternative access plans and choosing among them. These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries. In addition, nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed. The focus, however, is on query optimization in centralized database systems.
Talent Identification and Development in Male Football: A Systematic Review.

Expertise has been extensively studied in several sports over recent years. The specificities of how excellence is achieved in Association Football, a sport practiced worldwide, are being repeatedly investigated by many researchers through a variety of approaches and scientific disciplines.



The aim of this review was to identify and synthesise the most significant literature addressing talent identification and development in football. We identified the most frequently researched topics and characterised their methodologies.



A systematic review of Web of Science™ Core Collection and Scopus databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. The following keywords were used: "football" and "soccer". Each word was associated with the terms "talent", "expert*", "elite", "elite athlete", "identification", "career transition" or "career progression". The selection was for the original articles in English containing relevant data about talent development/identification on male footballers.



The search returned 2944 records. After screening against set criteria, a total of 70 manuscripts were fully reviewed. The quality of the evidence reviewed was generally excellent. The most common topics of analysis were (1) task constraints: (a) specificity and volume of practice; (2) performers' constraints: (a) psychological factors; (b) technical and tactical skills; (c) anthropometric and physiological factors; (3) environmental constraints: (a) relative age effect; (b) socio-cultural influences; and (4) multidimensional analysis. Results indicate that the most successful players present technical, tactical, anthropometric, physiological and psychological advantages that change non-linearly with age, maturational status and playing positions. These findings should be carefully considered by those involved in the identification and development of football players.



This review highlights the need for coaches and scouts to consider the players' technical and tactical skills combined with their anthropometric and physiological characteristics scaled to age. Moreover, research addressing the psychological and environmental aspects that influence talent identification and development in football is currently lacking. The limitations detected in the reviewed studies suggest that future research should include the best performers and adopt a longitudinal and multidimensional perspective.
Ergative Case and the Transitive Subject : A View from Nez Perce

Ergative case, the special case of transitive subjects, rai ses questions not only for the theory of case but also for theories of subjectho od and transitivity. This paper analyzes the case system of Nez Perce, a ”three-way erg tiv ” language, with an eye towards a formalization of the category of transitive subject . I show that it is object agreement that is determinative of transitivity, an d hence of ergative case, in Nez Perce. I further show that the transitivity condition on ergative case must be coupled with a criterion of subjecthood that makes reference to participation in subject agreement, not just to origin in a high argument-structural position. These two results suggest a formalization of the transitive subject as that ar gument uniquely accessing both high and low agreement information, the former through its (agreement-derived) connection with T and the latter through its origin in the spe cifi r of a head associated with object agreement (v). In view of these findings, I ar gue that ergative case morphology should be analyzed not as the expression of a synt ctic primitive but as the morphological spell-out of subject agreement and objec t agreement on a nominal.
Multicore bundle adjustment

We present the design and implementation of new inexact Newton type Bundle Adjustment algorithms that exploit hardware parallelism for efficiently solving large scale 3D scene reconstruction problems. We explore the use of multicore CPU as well as multicore GPUs for this purpose. We show that overcoming the severe memory and bandwidth limitations of current generation GPUs not only leads to more space efficient algorithms, but also to surprising savings in runtime. Our CPU based system is up to ten times and our GPU based system is up to thirty times faster than the current state of the art methods , while maintaining comparable convergence behavior. The code and additional results are available at http://grail.cs. washington.edu/projects/mcba.
Large Inpainting of Face Images With Trainlets

Image inpainting is concerned with the completion of missing data in an image. When the area to inpaint is relatively large, this problem becomes challenging. In these cases, traditional methods based on patch models and image propagation are limited, since they fail to consider a global perspective of the problem. In this letter, we employ a recently proposed dictionary learning framework, coined Trainlets, to design large adaptable atoms from a corpus of various datasets of face images by leveraging the online sparse dictionary learning algorithm. We, therefore, formulate the inpainting task as an inverse problem with a sparse-promoting prior based on the learned global model. Our results show the effectiveness of our scheme, obtaining much more plausible results than competitive methods.
Discovering Urban Functional Zones Using Latent Activity Trajectories

The step of urbanization and modern civilization fosters different functional zones in a city, such as residential areas, business districts, and educational areas. In a metropolis, people commute between these functional zones every day to engage in different socioeconomic activities, e.g., working, shopping, and entertaining. In this paper, we propose a data-driven framework to discover functional zones in a city. Specifically, we introduce the concept of latent activity trajectory (LAT), which captures socioeconomic activities conducted by citizens at different locations in a chronological order. Later, we segment an urban area into disjointed regions according to major roads, such as highways and urban expressways. We have developed a topic-modeling-based approach to cluster the segmented regions into functional zones leveraging mobility and location semantics mined from LAT. Furthermore, we identify the intensity of each functional zone using Kernel Density Estimation. Extensive experiments are conducted with several urban scale datasets to show that the proposed framework offers a powerful ability to capture city dynamics and provides valuable calibrations to urban planners in terms of functional zones.
Traffic Games: Modeling Freeway Traffic with Game Theory

We apply game theory to a vehicular traffic model to study the effect of driver strategies on traffic flow. The resulting model inherits the realistic dynamics achieved by a two-lane traffic model and aims to incorporate phenomena caused by driver-driver interactions. To achieve this goal, a game-theoretic description of driver interaction was developed. This game-theoretic formalization allows one to model different lane-changing behaviors and to keep track of mobility performance. We simulate the evolution of cooperation, traffic flow, and mobility performance for different modeled behaviors. The analysis of these results indicates a mobility optimization process achieved by drivers' interactions.
A reconfigurable in-phase/out-of-phase and power-dividing ratio power divider

This paper presents a varactor-based power divider with reconfigurable power-dividing ratio and reconfigurable in-phase or out-of-phase phase relation between outputs. By properly controlling the tuning varactors, the power divider can be either in phase or out of phase and each with a wide range of tunable power-dividing ratio. The proposed microstrip power divider was prototyped and experimentally characterized. Measured and simulated results are in good agreement.
Structure Aware SLAM using Quadrics and Planes

Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information. On the other hand, state of the art object detection methods provide rich information about entities present in the scene from a single image. This work marries the two and proposes a method for representing generic objects as quadrics which allows object detections to be seamlessly integrated in a SLAM framework. For scene coverage, additional dominant planar structures are modeled as infinite planes. Experiments show that the proposed points-planes-quadrics representation can easily incorporate Manhattan and object affordance constraints, greatly improving camera localization and leading to semantically meaningful maps. The performance of our SLAM system is demonstrated in https://youtu.be/dR-rB9keF8M.
Video benchmarks of human action datasets: a review

Vision-based Human activity recognition is becoming a trendy area of research due to its wide application such as security and surveillance, human–computer interactions, patients monitoring system, and robotics. In the past two decades, there are several publically available human action, and activity datasets are reported based on modalities, view, actors, actions, and applications. The objective of this survey paper is to outline the different types of video datasets and highlights their merits and demerits under practical considerations. Based on the available information inside the dataset we can categorise these datasets into RGB (Red, Green, and Blue) and RGB-D(depth). The most prominent challenges involved in these datasets are occlusions, illumination variation, view variation, annotation, and fusion of modalities. The key specification of these datasets is discussed such as resolutions, frame rate, actions/actors, background, and application domain. We have also presented the state-of-the-art algorithms in a tabular form that give the best performance on such datasets. In comparison with earlier surveys, our works give a better presentation of datasets on the well-organised comparison, challenges, and latest evaluation technique on existing datasets.
Consequences of poor power quality - an overview

Electricity, as a commercialised commodity, has gained greater importance in the last two decades. The customers use more sensitive and sophisticated electronic devices which demand a reliable high quality power supply. On the contrary, these devices produce distortions in the power supply due to their nonlinear characteristics. In this paper, a comprehensive survey on power quality (PQ) related problems, as experienced by the customers in different countries of the world, are highlighted. Also, an investigation is done to find out the technical and economical consequences of poor power quality. At present, no definite standard is available concerning various power quality aspects at the point of connection (POC). Therefore, it is a dilemma for the network operators, the customers and the equipment manufacturers, how to share the responsibility at the POC where they meet each other. As the consequences of poor power quality might have large financial impacts, more initiatives are expected from the regulators and the connected parties to take corrective actions for maintaining better quality of the power supply at the POC.
Morphable Counters: Enabling Compact Integrity Trees For Low-Overhead Secure Memories

Securing off-chip main memory is essential for protection from adversaries with physical access to systems. However, current secure-memory designs incur considerable performance overheads – a major cause being the multiple memory accesses required for traversing an integrity-tree, that provides protection against man-in-the-middle attacks or replay attacks. In this paper, we provide a scalable solution to this problem by proposing a compact integrity tree design that requires fewer memory accesses for its traversal. We enable this by proposing new storage-efficient representations for the counters used for encryption and integrity-tree in secure memories. Our Morphable Counters are more cacheable on-chip, as they provide more counters per cacheline than existing split counters. Additionally, they incur lower overheads due to counter-overflows, by dynamically switching between counter representations based on usage pattern. We show that using Morphable Counters enables a 128-ary integrity-tree, that can improve performance by 6.3% on average (up to 28.3%) and reduce system energy-delay product by 8.8% on average, compared to an aggressive baseline using split counters with a 64-ary integrity-tree. These benefits come without any additional storage or reduction in security and are derived from our compact counter representation, that reduces the integrity-tree size for a 16GB memory from 4MB in the baseline to 1MB. Compared to recently proposed VAULT, our design provides a speedup of 13.5% on average (up to 47.4%).
The Effect of Primary , Secondary , and Tertiary Wastewater Treatment Processes on Antibiotic Resistance Gene ( ARG ) Concentrations in Solid and Dissolved Wastewater Fractions

Wastewater treatment plants (WWTPs) have been identified as potential sources of antibiotic resistance genes (ARGs) but the effects of tertiary wastewater treatment processes on ARGs have not been well characterized. Therefore, the objective of this study was to determine the fate of ARGs throughout a tertiary-stage WWTP. Two ARGs, sul1 and bla, were quantified via quantitative polymerase chain reaction (qPCR) in solids and dissolved fractions of raw sewage, activated sludge, secondary effluent and tertiary effluent from a full-scale WWTP. Tertiary media filtration and chlorine disinfection were studied further with the use of a pilot-scale media filter. Results showed that both genes were reduced at each successive stage of treatment in the dissolved fraction. The solids-associated ARGs increased during activated sludge stage and were reduced in each subsequent stage. Overall reductions were approximately four log10 with the tertiary media filtration and disinfection providing the largest decrease. The majority of ARGs were solids-associated except for in the tertiary effluent. There was no evidence for positive selection of ARGs during treatment. The removal of ARGs by chlorine was improved by filtration compared to unfiltered, chlorinated secondary effluent. This study demonstrates that tertiary-stage WWTPs with disinfection can provide superior removal of ARGs compared to secondary treatment alone.
Graph-signal reconstruction and blind deconvolution for diffused sparse inputs

This paper investigates the problems of signal reconstruction and blind deconvolution for graph signals that have been generated by an originally sparse input diffused through the network via the application of a graph filter operator. Assuming that the support of the sparse input signal is unknown, and that the diffused signal is observed only at a subset of nodes, we address the related problems of: 1) identifying the input and 2) interpolating the values of the diffused signal at the non-sampled nodes. We first consider the more tractable case where the coefficients of the diffusing graph filter are known and then address the problem of joint input and filter identification. The corresponding blind identification problems are formulated, novel convex relaxations are discussed, and modifications to incorporate a priori information on the sparse inputs are provided.
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

Modern robotics applications that involve human-robot interaction require robots to be able to communicate with humans seamlessly and effectively. Natural language provides a flexible and efficient medium through which robots can exchange information with their human partners. Significant advancements have been made in developing robots capable of interpreting free-form instructions, but less attention has been devoted to endowing robots with the ability to generate natural language. We propose a model that enables robots to generate natural language instructions that allow humans to navigate a priori unknown environments. We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning. We then "translate" this information into a natural language instruction using a neural sequence-to-sequence model that learns to generate free-form instructions from natural language corpora. We evaluate our method on a benchmark route instruction dataset and achieve a BLEU score of 72.18% compared to human-generated reference instructions. We additionally conduct navigation experiments with human participants demonstrating that our method generates instructions that people follow as accurately and easily as those produced by humans.
FIGHTING EVASIVE MALWARE WITH DVASION by

2016 Thesis directed by: Professor Rajeev Barua Department of Electrical and Computer Engineering Malware is a foundational component of cyber crime that enables an attacker to modify the normal operation of a computer or access sensitive, digital information. Despite the extensive research performed to identify such programs, existing schemes fail to detect evasive malware, an increasingly popular class of malware that can alter its behavior at run-time, making it difficult to detect using today’s state of the art malware analysis systems. In this thesis, we present DVasion, a comprehensive strategy that exposes such evasive behavior through a multi-execution technique. DVasion successfully detects behavior that would have been missed by traditional, single-execution approaches, while addressing the limitations of previously proposed multi-execution systems. We demonstrate the accuracy of our system through strong parallels with existing work on evasive malware, as well as uncover the hidden behavior within 167 of 1,000 samples.
Visualisation of exudates in fundus images using radar chart and color auto correlogram technique

Fundus images provide an opportunity for early detection of diabetes. Generally, retina fundus images of diabetic patients exhibit exudates, which are lesions indicative of Diabetic Retinopathy (DR). Therefore, computational tools can be considered to be used in assisting ophthalmologists and medical doctor for the early screening of the disease. Hence in this paper, we proposed visualisation of exudates in fundus images using radar chart and Color Auto Correlogram (CAC) technique. The proposed technique requires that the Optic Disc (OD) from the fundus image be removed. Next, image normalisation was performed to standardise the colors in the fundus images. The exudates from the modified image are then extracted using Artificial Neural Network (ANN) and visualised using radar chart and CAC technique. The proposed technique was tested on 149 images of the publicly available MESSIDOR database. Experimental results suggest that the method has potential to be used for early indication of DR, by visualising the overlap between CAC features of the fundus images.
Mechanics Modeling of Tendon-Driven Continuum Manipulators

Continuum robotic manipulators articulate due to their inherent compliance. Tendon actuation leads to compression of the manipulator, extension of the actuators, and is limited by the practical constraint that tendons cannot support compression. In light of these observations, we present a new linear model for transforming desired beam configuration to tendon displacements and vice versa. We begin from first principles in solid mechanics by analyzing the effects of geometrically nonlinear tendon loads. These loads act both distally at the termination point and proximally along the conduit contact interface. The resulting model simplifies to a linear system including only the bending and axial modes of the manipulator as well as the actuator compliance. The model is then manipulated to form a concise mapping from beam configuration-space parameters to n redundant tendon displacements via the internal loads and strains experienced by the system. We demonstrate the utility of this model by implementing an optimal feasible controller. The controller regulates axial strain to a constant value while guaranteeing positive tendon forces and minimizing their magnitudes over a range of articulations. The mechanics-based model from this study provides insight as well as performance gains for this increasingly ubiquitous class of manipulators.