nils-herrmann/clean_scirepeval huggingface dataset:
Boosted Decision Tree Regression to Predict Yelp Review Stars from Review Text

In this project, we consider the problem of predicting restaurant ratings solely from the text reviews given by the user. This is a challenging text mining problem and it has many applications on online review platforms. The project involves evaluating different feature extraction methods: Bag of words and Vowpal Wabbit. These extracted features are trained using machine learning techniques such as Linear regression and Boosted Decision Tree Regression for rating prediction. The motivation is to find the combination of best machine learning technique and feature extraction method to solve the problem. In addition, we analyzed how the sentiments of people vary with geographical location and time. Further, we used the review text to group users based on their tastes and preferences.
A Generalized Path Integral Control Approach to Reinforcement Learning

With the goal to generate more scalable algorithms with high er efficiency and fewer open parameters, reinforcement learning (RL) has recently moved towar ds combining classical techniques from optimal control and dynamic programming with modern learni ng techniques from statistical estimation theory. In this vein, this paper suggests to use the fr amework of stochastic optimal control with path integrals to derive a novel approach to RL with para meterized policies. While solidly grounded in value function estimation and optimal control b ased on the stochastic Hamilton-JacobiBellman (HJB) equations, policy improvements can be transf ormed into an approximation problem of a path integral which has no open algorithmic parameters o ther than the exploration noise. The resulting algorithm can be conceived of as model-based, sem i-model-based, or even model free, depending on how the learning problem is structured. The upd ate equations have no danger of numerical instabilities as neither matrix inversions nor g radient learning rates are required. Our new algorithm demonstrates interesting similarities with previous RL research in the framework of probability matching and provides intuition why the slig htly heuristically motivated probability matching approach can actually perform well. Empirical eva luations demonstrate significant performance improvements over gradient-based policy learnin g a d scalability to high-dimensional control problems. Finally, a learning experiment on a simul ated 12 degree-of-freedom robot dog illustrates the functionality of our algorithm in a complex robot learning scenario. We believe that Policy Improvement withPath Integrals ( PI2) offers currently one of the most efficient, numerically robust, and easy to implement algorithms for RL based o n trajectory roll-outs.
The facilitated processing of threatening faces: an ERP analysis.

Threatening, friendly, and neutral faces were presented to test the hypothesis of the facilitated perceptual processing of threatening faces. Dense sensor event-related brain potentials were measured while subjects viewed facial stimuli. Subjects had no explicit task for emotional categorization of the faces. Assessing early perceptual stimulus processing, threatening faces elicited an early posterior negativity compared with nonthreatening neutral or friendly expressions. Moreover, at later stages of stimulus processing, facial threat also elicited augmented late positive potentials relative to the other facial expressions, indicating the more elaborate perceptual analysis of these stimuli. Taken together, these data demonstrate the facilitated perceptual processing of threatening faces. Results are discussed within the context of an evolved module of fear (A. Ohman & S. Mineka, 2001).
Lower Lateral Cartilage Cephalic Malposition: An Over-Diagnosed Entity

Lower lateral cartilage malposition is represented by anterior convexity of the lower lateral cartilage (LLC) dome with posterior pinch, as defined by Sheen and Constantian. This anatomic variation consists of cephalic, or upward and inward, rotation of lateral crura, particularly in bulbous tip patients. In most cases, “bulbous pinch” LLC is positioned toward the medial canthus, not laterally, so it is referred to as cephalic displacement. Accordingly, it is recommended to caudally displace cartilage in the majority of rhinoplasty cases in which variation is seen. The purpose of this paper is to measure the exact angle of lateral crura with fixed reference points on the face. We drew and marked LLC contours and vertical/horizontal lines in 40 consecutive rhinoplasty cases. We then divided them into two groups: (1) bulbous pinch and (2) flat LLCs. The right- and left-sided LLC angles to midline and horizontal lines were measured and compared to assess whether there was any significant difference between the two subgroups. There was no significant difference between the angles of LLC rotation in the bulbous and flat LLCs groups, measured both vertically and horizontally. Based on our findings, although cephalic malposition of LLCs may be present in some patients but in the majority of cases the etiology of nasal lateral wall pinching is not cephalic displacement of lateral crura but most probably is due, rather, to severe convexity of the posterior and lateral crura. According to our findings, cephalic malposition is an uncommon anatomic variation of LLCs that has been reported at high frequency (60–70% of their rhinoplasty cases). This finding may help to correct this deformity into a normal anatomic configuration. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
Microsoft Malware Classification Challenge

The Microsoft Malware Classification Challenge was announced in 2015 along with a publication of a huge dataset of nearly 0.5 terabytes, consisting of disassembly and bytecode of more than 20K malware samples. Apart from serving in the Kaggle competition, the dataset has become a standard benchmark for research on modeling malware behaviour. To date, the dataset has been cited in more than 50 research papers. Here we provide a high-level comparison of the publications citing the dataset. The comparison simplifies finding potential research directions in this field and future performance evaluation of the dataset.
Optimal Adaptive Waveform Design for Cognitive MIMO Radar

This paper addresses the problem of adaptive waveform design for estimation of parameters of linear systems. This problem arises in several applications such as radar, sonar, or tomography. In the proposed technique, the transmit/input signal waveform is optimally determined at each step based on the observations in the previous steps. The waveform is determined to minimize the Bayesian Cramér-Rao bound (BCRB) or the Reuven-Messer bound (RMB) for estimation of the unknown system parameters at each step. The algorithms are tested for spatial transmit waveform design in multiple-input multiple-output radar target angle estimation at very low signal-to-noise ratio. The proposed techniques allow to automatically focusing the transmit beam toward the target direction. The simulations show that the proposed adaptive waveform design methods achieve significantly higher rate of performance improvement as a function of the pulse index, compared to other signal transmission methods, in terms of estimation accuracy.
An 8b 5-GS/s CMOS SAR ADC with speed optimized SAR logic

An 8b 5-GS/s Successive Approximation Register (SAR) analog-to-digital converter (ADC) is implemented in 65-nm CMOS using 16-channel time-interleaving, achieving a SNR of 43.5 dB and Figure-of-Merit (FoM) of 245 fJ/conv-step. High speed operation is achieved by optimizing the critical path in the SAR ADC loop. A sampling network with a split-array with unit bridge capacitor topology is used to reduced the area of the sampling network and switch drivers. The ADC consumes 149 mW of power.
The Traveling Salesman Problem: A Survey

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Cascaded hand pose regression

We extends the previous 2D cascaded object pose regression work  in two aspects so that it works better for 3D articulated objects. Our first contribution is 3D pose-indexed features that generalize the previous 2D parameterized features and achieve better invariance to 3D transformations. Our second contribution is a principled hierarchical regression that is adapted to the articulated object structure. It is therefore more accurate and faster. Comprehensive experiments verify the state-of-the-art accuracy and efficiency of the proposed approach on the challenging 3D hand pose estimation problem, on a public dataset and our new dataset.
Single-Stage Single-Switch PFC Flyback Converter Using a Synchronous Rectifier

A single-stage single-switch power factor correction (PFC) flyback converter with a synchronous rectifier (SR) is proposed for improving power factor and efficiency. Using a variable switching-frequency controller, this converter is continuously operated with a reduced turn-on switching loss at the boundary of the continuous conduction mode and discontinuous conduction mode (DCM). The proposed PFC circuit provides relatively low dc-link voltage in the universal line voltage, and also complies with Standard IEC 61000-3-2 Class D limits. In addition, a new driving circuit as the voltage driven-synchronous rectifier is proposed to achieve high efficiency. In particular, since a driving signal is generated according to the voltage polarity, the SR driving circuit can easily be used in DCM applications. The proposed PFC circuit and SR driving circuit in the flyback converter with the reduced switching loss are analyzed in detail and optimized for high performance. Experimental results for a 19 V/90 W adapter at the variable switching-frequency of 30~70 kHz were obtained to show the performance of the proposed converter.
A survey of sparse matrix-vector multiplication performance on large matrices

One of the main sources of sparse matrices is the discretization of partial differential equations that govern continuumphysics phenomena such as fluid flow and transport, phase separation, mechanical deformation, electromagnetic wave propagation, and others. Recent advances in high-performance computing area have been enabling researchers to tackle increasingly larger problems leading to sparse linear systems with hundreds of millions to a few tens of billions of unknowns, e.g., [5, 6]. Iterative linear solvers are popular in large-scale computing as they consume less memory than direct solvers. Contrary to direct linear solvers, iterative solvers approach the solution gradually requiring the computation of sparse matrixvector (SpMV) products. The evaluation of SpMV products can emerge as a bottleneck for computational performance within the context of the simulation of large problems. In this work, we focus on a linear system arising from the discretization of the Cahn–Hilliard equation, which is a fourth order nonlinear parabolic partial differential equation that governs the separation of a two-component mixture into phases . The underlying spatial discretization is performed using the discontinuous Galerkin method (e.g. [10]) and Newton’s method. A number of parallel algorithms and strategies have been evaluated in this work to accelerate the evaluation of SpMV products.
Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.
Tractable Queries for Lightweight Description Logics

It is a classic result in database theory that conjunctive query (CQ) answering, which is NP-complete in general, is feasible in polynomial time when restricted to acyclic queries. Subsequent results identified more general structural properties of CQs (like bounded treewidth) which ensure tractable query evaluation. In this paper, we lift these tractability results to knowledge bases formulated in the lightweight description logics DL-Lite and ELH. The proof exploits known properties of query matches in these logics and involves a querydependent modification of the data. To obtain a more practical approach, we propose a concrete polynomial-time algorithm for answering acyclic CQs based on rewriting queries into datalog programs. A preliminary evaluation suggests the interest of our approach for handling large acyclic CQs.
Text-Independent Writer Identification via CNN Features and Joint Bayesian

This paper proposes a novel method for offline text-independent writer identification by using convolutional neural network (CNN) and joint Bayesian, which consists of two stages, i.e. feature extraction and writer identification. In the stage of feature extraction, since a large number of data is essential to train an effective CNN model with high generalizability and the amount of handwriting is limited in writer identification, a data augmentation technique is first developed to generate thousands of handwriting images for each writer. Then a deep CNN network is designed to extract discriminative features to represent the properties of different writing styles, which is trained by using the generated handwriting images. In the stage of writer identification, the training dataset is used to train the CNN model for feature extraction and the joint Bayesian technique is employed to accomplish the task of writer identification based on the extracted CNN features. The proposed method is tested on two standard benchmark datasets, i.e. ICDAR2013 and CVL dataset. Experimental results demonstrate that the proposed method gets the best performance compared to the state-of-the-art approaches.
Alobar holoprosencephaly, mobile proboscis and trisomy 13 in a fetus with maternal gestational diabetes mellitus: a 2D ultrasound diagnosis and review of the literature

Alobar holoprosencephaly is a rare and severe brain malformation due to early arrest in brain cleavage and rotation. We report a congenital anomalous fetus with alobar holoprosencephaly, prenatally diagnosed by two-dimensional (2D) sonography at the 40 weeks of gestation. The mother was affected by gestational diabetes mellitus and was obese (BMI > 30 kg/m2). 2D Ultrasound depicted the cerebral malformation, cyclopy, proboscis, cardiac defects (atrial septal defect, hypoplastic left heart, anomalous communication between right ventricle and aorta) and extremities defects. The newborn died just after delivery. External examination confirmed a mobile proboscis-like nose on the normal nose position. The fetus had both claw hands. The right and left foots showed to be equine. Autopsy confirmed the ultrasound diagnosis and chromosome analysis revealed trisomy 13 (47,XY,+13). Fetopathologic examination showed cyclopy, proboscis and alobar holoprosencephaly of the fetus, which was consistent with Patau syndrome. The teratogenic effect of diabetes on fetus has been described, but no previous clinical case of a congenital anomalous fetus with trisomy 13 and maternal gestational diabetes has been reported. This case report is the first to describe 2D ultrasound diagnosis of alobar holoprosencephaly and trisomy 13 with maternal gestational diabetes mellitus.
Gesture Recognition with a Convolutional Long Short-Term Memory Recurrent Neural Network

Inspired by the adequacy of convolutional neural networks in implicit extraction of visual features and the efficiency of Long Short-Term Memory Recurrent Neural Networks in dealing with long-range temporal dependencies, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (CNNLSTM) for the problem of dynamic gesture recognition. The model is able to successfully learn gestures varying in duration and complexity and proves to be a significant base for further development. Finally, the new gesture command TsironiGR-dataset for human-robot interaction is presented for the evaluation of CNNLSTM.
Bit Error Tolerance of a CIFAR-10 Binarized Convolutional Neural Network Processor

Deployment of convolutional neural networks (ConvNets) in always-on Internet of Everything (IoE) edge devices is severely constrained by the high memory energy consumption of hardware ConvNet implementations. Leveraging the error resilience of ConvNets by accepting bit errors at reduced voltages presents a viable option for energy savings, but few implementations utilize this due to the limited quantitative understanding of how bit errors affect performance. This paper demonstrates the efficacy of SRAM voltage scaling in a 9-layer CIFAR-10 binarized ConvNet processor, achieving memory energy savings of 3.12× with minimal accuracy degradation (∼99% of nominal). Additionally, we quantify the effect of bit error accumulation in a multi-layer network and show that further energy savings are possible by splitting weight and activation voltages. Finally, we compare the measured error rates for the CIFAR-10 binarized ConvNet against MNIST networks to demonstrate the difference in bit error requirements across varying complexity in network topologies and classification tasks.
Parallel Strategies Selection

We consider the problem of selecting the best variable-value strategy for solving a given problem in constraint programming. We show that the recent Embarrassingly Parallel Search method (EPS) can be used for this purpose. EPS proposes to solve a problem by decomposing it in many subproblems and to give them on-demand to workers which run in parallel. Our method uses a sample of these subproblems for comparing strategies in order to select the most promising one to be used for solving the remaining subproblems. Each subproblem of the sample is solved with all the candidate strategies in parallel using a timeout that is twice the time of the best one. The selection of the strategy is then based on the Wilcoxon signed rank test. This test is able to deal with censored data caused by timeouts and makes no assumption on the solving time distribution. The experiments we performed on a set of classical benchmarks for satisfaction and optimization problems show that our method selects most of the time the best strategy. Our method also outperforms the portfolio approach consisting of running some strategies in parallel and is competitive with the multi armed bandit framework.
Short segment fixation of thoracolumbar burst fractures without fusion

There continues to be controversy surrounding the management of thoracolumbar burst fractures. Numerous methods of fixation have been described for this injury, but to our knowledge, spinal fusion has always been part of the stabilising procedure, whether this involves an anterior or a posterior approach. Apart from an earlier publication from this centre, there have been no reports on the use of internal fixation without fusion for this type of fracture. The aim of the study was to determine the outcome of patients with thoracolumbar burst fractures who were treated with short segment pedicle screw fixation without fusion. This is a retrospective review of 28 consecutive patients who had short segment pedicle screw fixation of thoracolumbar burst fractures without fusion performed between 1990 and 1993. All patients underwent a clinical and radiological assessment by an independent observer. Outcome was measured using the Low Back Outcome Score. The minimum follow-up period was 2 years (mean 3.1 years). Fifty percent of patients achieved an excellent result with the Low Back Outcome Score, while 12% were assessed as good, 20% fair and 16% obtained a poor result. The only significant factor affecting outcome was the influence of a compensation claim (P < 0.05). The implant failure rate (14% of patients) and the clinical outcome was similar to that from series where fusion had been performed in addition to pedicle screw fixation. The results of this study support the view that posterolateral bone grafting is not necessary when managing patients with thoracolumbar burst fractures by short segment pedicle screw fixation.
Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions

In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular bandwidth and all communications take place through the base stations. In this article, we envision a two-tier cellular network that involves a macrocell tier (i.e., BS-to-device communications) and a device tier (i.e., device-to-device communications). Device terminal relaying makes it possible for devices in a network to function as transmission relays for each other and realize a massive ad hoc mesh network. This is obviously a dramatic departure from the conventional cellular architecture and brings unique technical challenges. In such a two-tier cellular system, since the user data is routed through other users' devices, security must be maintained for privacy. To ensure minimal impact on the performance of existing macrocell BSs, the two-tier network needs to be designed with smart interference management strategies and appropriate resource allocation schemes. Furthermore, novel pricing models should be designed to tempt devices to participate in this type of communication. Our article provides an overview of these major challenges in two-tier networks and proposes some pricing schemes for different types of device relaying.