nils-herrmann/clean_scirepeval huggingface dataset:
RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively.


AVAILABILITY
icwww.epfl.ch/~stamatak
Automatically Rigging Multi-component Characters

Rigging an arbitrary 3D character by creating an animation skeleton is a time-consuming process even for experienced animators. In this paper, we present an algorithm that automatically creates animation rigs for multicomponent 3D models, as they are typically found in online shape databases. Our algorithm takes as input a multi-component model and an input animation skeleton with associated motion data. It then creates a target skeleton for the input model, calculates the rigid skinning weights, and a mapping between the joints of the target skeleton and the input animation skeleton. The automatic approach does not need additional semantic information, such as component labels or user-provided correspondences, and succeeds on a wide range of models where the number of components is significantly different. It implicitly handles large scale and proportional differences between input and target skeletons and can deal with certain morphological differences, e.g., if input and target have different numbers of limbs. The output of our algorithm can be directly used in a retargeting system to create a plausible animated character.
Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection

This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions were paraphrases of each other. This approach achieved a MAP of 45.7% out of max achievable 67.0% on the test set.
Let's (not) stick together: pairwise similarity biases cross-validation in activity recognition

The ability to generalise towards either new users or unforeseen behaviours is a key requirement for activity recognition systems in ubiquitous computing. Differences in recognition performance for the two application cases can be significant, and user-dependent performance is typically assumed to be an upper bound on performance. We demonstrate that this assumption does not hold for the widely used cross-validation evaluation scheme that is typically employed both during system bootstrapping and for reporting results. We describe how the characteristics of segmented time-series data render random cross-validation a poor fit, as adjacent segments are not statistically independent. We develop an alternative approach -- meta-segmented cross validation -- that explicitly circumvents this issue and evaluate it on two data-sets. Results indicate a significant drop in performance across a variety of feature extraction and classification methods if this bias is removed, and that prolonged, repetitive activities are particularly affected.
Robust misinterpretation of confidence intervals.

Null hypothesis significance testing (NHST) is undoubtedly the most common inferential technique used to justify claims in the social sciences. However, even staunch defenders of NHST agree that its outcomes are often misinterpreted. Confidence intervals (CIs) have frequently been proposed as a more useful alternative to NHST, and their use is strongly encouraged in the APA Manual. Nevertheless, little is known about how researchers interpret CIs. In this study, 120 researchers and 442 students-all in the field of psychology-were asked to assess the truth value of six particular statements involving different interpretations of a CI. Although all six statements were false, both researchers and students endorsed, on average, more than three statements, indicating a gross misunderstanding of CIs. Self-declared experience with statistics was not related to researchers' performance, and, even more surprisingly, researchers hardly outperformed the students, even though the students had not received any education on statistical inference whatsoever. Our findings suggest that many researchers do not know the correct interpretation of a CI. The misunderstandings surrounding p-values and CIs are particularly unfortunate because they constitute the main tools by which psychologists draw conclusions from data.
Building a Robust Implementation of Bearing-only Inertial SLAM for a UAV

This paper presents the on-going design and implementation of a robust inertial sensor based Simultaneous Localisation And Mapping (SLAM) algorithm for an Unmanned Aerial Vehicle (UAV) using bearing-only observations. A single colour vision camera is used to observe the terrain from which image points corresponding to features in the environment are extracted. The SLAM algorithm estimates the complete 6-DoF motion of the UAV along with the three-dimensional position of the features in the environment.  Data association is achieved using a multi-hypothesis innovation gate based on the spatial uncertainty of each feature. Results are presented by running the algorithm off-line using inertial sensor and vision data collected during a flight test of a UAV.
Miniaturized Circular Polarized TE $_{10}$-Mode Substrate-Integrated-Waveguide Antenna

We propose a miniaturized circularly polarized (CP) antenna based on a substrate integrated waveguide (SIW) working in the dominant TE10 mode. To reduce the overall size, only 1/8 of the SIW cavity is used as the radiation aperture. The two cavities are orthogonally placed to allow CP operation. Because of the via walls on the SIW, the two cavities can be closely placed, which reduces the overall size as well. The performance of the devices is demonstrated using the simulated and measurement results. The 10-dB impedance bandwidth is 1.63-1.66 GHz, and the 3-dB axial-ratio bandwidth is 1.64-1.65 GHz. The measured peak gain and axial ratio are 0.7 dBi and 2.3 dB at 1.645 GHz, respectively .
CEM-RL: Combining evolutionary and gradient-based methods for policy search

Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search. The former is widely applicable and rather stable, but suffers from low sample efficiency. By contrast, the latter is more sample efficient, but the most sample efficient variants are also rather unstable and highly sensitive to hyper-parameter setting. So far, these families of methods have mostly been compared as competing tools. However, an emerging approach consists in combining them so as to get the best of both worlds. Two previously existing combinations use either an ad hoc evolutionary algorithm or a goal exploration process together with the Deep Deterministic Policy Gradient (ddpg) algorithm, a sample efficient off-policy deep RL algorithm. In this paper, we propose a different combination scheme using the simple cross-entropy method (cem) and Twin Delayed Deep Deterministic policy gradient (td3), another off-policy deep RL algorithm which improves over ddpg. We evaluate the resulting method, cem-rl, on a set of benchmarks classically used in deep RL. We show that cem-rl benefits from several advantages over its competitors and offers a satisfactory trade-off between performance and sample efficiency.
Building security and trust in online banking

Growing threats to online banking security (e.g. phishing, personal identify fraud) and the personal nature of the data make the balance between security, trust and usability vital. However, there is little published research about what influences users' perceptions of online banking security and trust. This study identifies that the type of authentication system used can affect users' subsequent perceived control, situational awareness and trust. The results from a questionnaire and in-depth interviews with 86 participants were triangulated to compare two different authentication processes, namely, a 'security box' (i.e. random system generated passwords at the users' location) and 'fixed passwords' (i.e. user owned and constant). The security box and login procedures were perceived significantly more trustworthy and secure at any location than 'fixed passwords'. Four main concepts were identified: "trust" "the authentication system", "location" and "control". The implications of these findings for HCI are discussed.
The Complexity of Temporal Logic Model Checking

Temporal logic. Logical formalisms for reasoning about time and the timing of events appear in several fields: physics, philosophy, linguistics, etc. Not surprisingly, they also appear in computer science, a field where logic is ubiquitous. Here temporal logics are used in automated reasoning, in planning, in semantics of programming languages, in artificial intelligence, etc. There is one area of computer science where temporal logic has been unusually successful: the specification and verification of programs and systems, an area we shall just call “programming” for simplicity. In today’s curricula, thousands of programmers first learn about temporal logic in a course on model checking! Temporal logic and programming. Twenty five years ago, Pnueli identified temporal logic as a very convenient formal language in which to state, and reason about, the behavioral properties of parallel programs and more generally reactive systems [Pnu77, Pnu81]. Indeed, correctness for these systems typically involves reasoning upon related events at different moments of a system execution [OL82]. Furthermore, when it comes to liveness properties, the expected behavior of reactive systems cannot be stated as a static property, or as an invariant one. Finally, temporal logic is well suited to expressing the whole variety of fairness properties that play such a prominent role in distributed systems [Fra86]. For these applications, one usually restricts oneself to propositional temporal logic: on the one hand, this does not appear to be a severe limitation in practice, and on the other hand, this restriction allows decision procedures for validity and entailment, so that, at least in principle, the above-mentioned reasoning can be automated. Model checking. Generally speaking, model checking is the algorithmic verification that a given logic formula holds in a given structure (the model
DETERMINING TRENDS IN GLOBAL CRIME AND JUSTICE : AN OVERVIEW OF RESULTS FROM THE UNITED NATIONS SURVEYS OF CRIME TRENDS AND OPERATIONS OF CRIMINAL JUSTICE SYSTEMS

Effectively measuring comparative developments in crime and justice trends from a global perspective remains a key challenge for international policy makers. The ability to compare crime levels across countries enables policy makers to determine where interventions should occur and improves understanding of the key causes of crime in different societies across the globe. Nevertheless, there are significant challenges to comparative work in the field of criminal justice, not least of which is the ability to quantify accurately levels of crime across countries. Taking into account the methodological weaknesses of using crosscountry data sources, the present article provides conclusions obtained by analysing the large amount of data available from the various United Nations surveys of crime trends and operations of criminal justice systems. “Not everything that can be counted, counts. And not everything that counts can be counted.” Albert Einstein
Emission of polarization-entangled microwave photons from a pair of quantum dots.

We describe a mechanism for the production of polarization-entangled microwaves using intraband transitions in a pair of quantum dots. This proposal relies neither on spin-orbit coupling nor on control over electron-electron interactions. The quantum correlation of microwave polarizations is obtained from orbital degrees of freedom in an external magnetic field. We calculate the concurrence of emitted microwave photon pairs and show that a maximally entangled Bell pair is obtained in the limit of weak interdot coupling.
Magnetics on silicon: an enabling technology for power supply on chip

Data from the ITRS2003 roadmap for 2010 predicts voltages for microprocessors in hand-held electronics will decrease to 0.8V with current and power increasing to 4A and 3W, respectively. Consequently, low power converters will move to multimegahertz frequencies with a resulting reduction in capacitor and inductor values by factors of 5 and 20, respectively. Values required at 10 MHz, for a low power buck converter, are estimated at 130 nH and 0.6 uF, compatible with the integration of magnetics onto silicon and the concept of power supply-on-chip (PSOC). A review of magnetics-on-silicon shows that inductance values of 20 to 40nH/mm/sup 2/ can be achieved for winding resistances less than 1/spl Omega/. A 1-/spl mu/H inductance can be achieved at 5 MHz with dc resistance of 1/spl Omega/ and a Q of four. Thin film magnetic materials, compatible with semiconductor processing, offer power loss density that is lower than ferrite by a factor of 5 at 10 MHz. Other data reported includes, lowest dc resistance values of 120 m/spl Omega/ for an inductance of 120 nH; highest Q of 15 for an inductance of 350 nH and a current of 1 A for a 1- /spl mu/H inductor. Future technology challenges include reducing losses using high resistivity, laminated magnetic materials, and increasing current carrying capability using high aspect-ratio, electroplated copper conductors. Compatible technologies are available in the power switch, control, and packaging space. Integrated capacitor technology is still a long-term challenge with maximum reported values of 400 nF/cm/sup 2/.
EchoFlex: Hand Gesture Recognition using Ultrasound Imaging

Recent improvements in ultrasound imaging enable new opportunities for hand pose detection using wearable devices. Ultrasound imaging has remained under-explored in the HCI community despite being non-invasive, harmless and capable of imaging internal body parts, with applications including smart-watch interaction, prosthesis control and instrument tuition. In this paper, we compare the performance of different forearm mounting positions for a wearable ultrasonographic device. Location plays a fundamental role in ergonomics and performance since the anatomical features differ among positions. We also investigate the performance decrease due to cross-session position shifts and develop a technique to compensate for this misalignment. Our gesture recognition algorithm combines image processing and neural networks to classify the flexion and extension of 10 discrete hand gestures with an accuracy above 98%. Furthermore, this approach can continuously track individual digit flexion with less than 5% NRMSE, and also differentiate between digit flexion at different joints.
A 600MS/s 30mW 0.13µm CMOS ADC array achieving over 60dB SFDR with adaptive digital equalization

At high conversion speed, time interleaving provides a viable way of achieving analog-to-digital conversion with low power consumption, especially when combined with the successive-approximation-register (SAR) architecture that is known to scale well in CMOS technology. In this work, we showcase a digital background-equalization technique to treat the path-mismatch problem as well as individual ADC nonlinearities in time-interleaved SAR ADC arrays. This approach was first introduced to calibrate the linearity errors in pipelined ADCs , and subsequently extended to treat SAR ADCs . In this prototype, we demonstrate the effectiveness of this technique in a compact SAR ADC array, which achieves 7.5 ENOB and a 65dB SFDR at 600MS/s while dissipating 23.6mW excluding the on-chip DLL, and exhibiting one of the best conversion FOMs among ADCs with similar sample rates and resolutions .
Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution

In this paper, we consider the image super-resolution (SR) problem. The main challenge of image SR is to recover high-frequency details of a low-resolution (LR) image that are important for human perception. To address this essentially ill-posed problem, we introduce a Deep Edge Guided REcurrent rEsidual (DEGREE) network to progressively recover the high-frequency details. Different from most of the existing methods that aim at predicting high-resolution (HR) images directly, the DEGREE investigates an alternative route to recover the difference between a pair of LR and HR images by recurrent residual learning. DEGREE further augments the SR process with edge-preserving capability, namely the LR image and its edge map can jointly infer the sharp edge details of the HR image during the recurrent recovery process. To speed up its training convergence rate, by-pass connections across the multiple layers of DEGREE are constructed. In addition, we offer an understanding on DEGREE from the view-point of sub-band frequency decomposition on image signal and experimentally demonstrate how the DEGREE can recover different frequency bands separately. Extensive experiments on three benchmark data sets clearly demonstrate the superiority of DEGREE over the well-established baselines and DEGREE also provides new state-of-the-arts on these data sets. We also present addition experiments for JPEG artifacts reduction to demonstrate the good generality and flexibility of our proposed DEGREE network to handle other image processing tasks.
Understanding the Intention of Information Contribution to Online Feedback Systems from Social Exchange and Motivation Crowding Perspectives

The online feedback system (OFS) has been touted to be an effective artifact for electronic word-of-mouth (EWOM). Accumulating sufficient detailed consumption information in the OFS is essential to the success of OFS. Yet, past research has focused on the effects of OFS on building trust and promoting sales and little knowledge about information provision to OFS has been developed. This study attempts to fill this gap by developing and testing a theoretical model to identify the possible antecedents that lead to the intention of consumers' information contribution to OFS. The model employs social exchange theory to identify benefit and cost factors influencing consumer intention, and motivation crowding theory to explore the moderating effects from environmental interventions that are embodied in OFS. Our preliminary results in general provide empirical support for the model. Practical implications are offered to OFS designers for system customization
Constraint Relaxation and Chunk Decomposition in Insight Problem Solving

Insight problem solving is characterized by impasses, states of mind in which the thinker does not know what to do next. The authors hypothesized that impasses are broken by changing the problem representation, and 2 hypothetical mechanisms for representational change are described: the relaxation of constraints on the solution and the decomposition of perceptual chunks. These 2 mechanisms generate specific predictions about the relative difficulty of individual problems and about differential transfer effects. The predictions were tested in 4 experiments using matchstick arithmetic problems. The results were consistent with the predictions. Representational change is a more powerful explanation for insight than alternative hypotheses, if the hypothesized change processes are specified in detail. Overcoming impasses in insight is a special case of the general need to override the imperatives of past experience in the face of novel conditions.
Time-Frequency Training OFDM with High Spectral Efficiency and Reliable Performance in High Speed Environments

Orthogonal frequency division multiplexing (OFDM) is widely recognized as the key technology for the next generation broadband wireless communication (BWC) systems. Besides high spectral efficiency, reliable performance over fast fading channels is becoming more and more important for OFDM-based BWC systems, especially when high speed cars, trains and subways are playing an increasingly indispensable role in our daily life. The time domain synchronous OFDM (TDS-OFDM) has higher spectral efficiency than the standard cyclic prefix OFDM (CP-OFDM), but suffers from severe performance loss over high speed mobile channels since the required iterative interference cancellation between the training sequence (TS) and the OFDM data block. In this paper, a fundamentally distinct OFDM-based transmission scheme called time-frequency training OFDM (TFT-OFDM) is proposed, whereby every TFT-OFDM symbol has training information both in the time and frequency domains. Unlike TDS-OFDM or CP-OFDM where the channel estimation is solely dependent on either time-domain TS or frequency-domain pilots, the joint time-frequency channel estimation for TFT-OFDM utilizes the time-domain TS without interference cancellation to merely acquire the path delay information of the channel, while the path coefficients are estimated by using the frequency-domain grouped pilots. The redundant grouped pilots only occupy about 3% of the total subcarriers, thus TFT-OFDM still has much higher spectral efficiency than CP-OFDM by about 8.5% in typical applications. Simulation results also demonstrate that TFT-OFDM outperforms CP-OFDM and TDS-OFDM in high speed mobile environments.
Cyclic deadlock prediction and avoidance for zone-controlled AGV system

Automated guided vehicles (AGVs) are now becoming popular in automated materials handling systems, flexible manufacturing systems and even containers handling applications at seaports. Its performance affects the efficiency of the entire system. Deadlock formation is a serious problem as it stalls the AGVS. The objective of this paper is to develop an efficient AGV deadlock prediction and avoidance algorithm for container port operations. Deadlock in a broad sense is a situation in which at least a part of the system stalls. There are a lot of situations in which the system may stall and most of these situations can be avoided through the control and navigation system. This paper discusses the development of an efficient strategy for predicting and avoiding the deadlocks in a large scale AGVS A variety of deadlock-detecting algorithms are available, but these methods work mainly for manufacturing system where the network layout is simple and uses only a small number of AGVs. The AGVS under study has complex layout and involves close to 80 AGVs. The proposed solution is implemented using the Automod simulation software, and the performance of the technique is presented. Simulation shows that all the potential deadlock situations can be detected and avoided via this methodology. Also, the proposed strategy is computationally efficient and is able to provide the movement decision to the vehicles within the 1.5-s sampling time. r 2002