nils-herrmann/clean_scirepeval huggingface dataset:
Tetra-amelia with lung hypoplasia and facial clefts, Roberts-SC syndrome: report of two cases

Although limb anomalies are a common clinical problem, they are rarely studied. The spectrum of limb anomalies ranges from very mild disorders such as syndactyly to very severe forms such as absent limb (amelia). Tetra-amelia is a rare anomaly with complete or partial deficiency of all four limbs. It may be isolated or associated with other anomalies. Roberts-SC phocomelia syndrome comprises four limb deficiencies, lung hypoplasia, facial clefts and other anomalies. We describe two cases that presented to us.
Effects of pectoral fins' spanwise flexibility on forward thrust generation

Pectoral fins of manta rays have been a great source of inspiration for propulsive mechanism of underwater vehicles in the last decade. In the existing literature, effects of the chordwise flexibility of these fins on the forward thrust generation has been widely studied, while the study on the spanwise flexibility is considerably lacking. In this paper, we aim to investigate the effects of the fins' spanwise flexibility (quantified by bending stiffness) on the forward thrust generation. An experiment was conducted in a water channel to measure the forward thrust generated by 16 fins under free stream condition (0.5 m/s). The fins' bending stiffness ranged from 0.018 N/mm to 0.499 N/mm. All the fins had the same span length and surface area in order to preserve the hydrodynamic integrity of the experiment. The experimental results showed that for the range of the spanwise flexibility considered in this study, the forward thrust generation increases by decreasing the fins' spanwise flexibility until a certain bending stiffness value. After that the rate of the change in the thrust generation becomes very minor, indicating a plateau region.
BCS: A Binary Cuckoo Search algorithm for feature selection

Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques.
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles

Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich information of RGB images. Deep Learning solutions applied to the data of other sensors typically mounted on autonomous cars (e.g. lidars or radars) are not explored much. In this paper we propose a novel solution to understand the dynamics of moving vehicles of the scene from only lidar information. The main challenge of this problem stems from the fact that we need to disambiguate the proprio-motion of the “observer” vehicle from that of the external “observed” vehicles. For this purpose, we devise a CNN architecture which at testing time is fed with pairs of consecutive lidar scans. However, in order to properly learn the parameters of this network, during training we introduce a series of so-called pretext tasks which also leverage on image data. These tasks include semantic information about vehicleness and a novel lidar-flow feature which combines standard image-based optical flow with lidar scans. We obtain very promising results and show that including distilled image information only during training, allows improving the inference results of the network at test time, even when image data is no longer used.
A random forest based machine learning approach for mild steel defect diagnosis

Industries today need to stay ahead in competition by servicing and satisfying customer's needs. Quality of the produced products to match as per customer demands is the key goal for a product manufacturing company. A product produced with variation in characteristics, than the anticipated are called as defect. In the mild steel coil manufacturing plants, large amount of data is generated with the help of many sensors deployed to measure different parameters which can be used for defect diagnosis of the coils produced. In case of mild steel coil, deviation of the final cooling temperature of the coil from desired temperature produces defective coils. The paper presents machine learning approach and the methodology for cooling temperature deviation defect diagnosis that consists of four phases namely data structuring, association identification, statistical derivation and classification. We also provide comparative results obtained with various data mining algorithms like decision trees, neural networks, SVM, ensemble techniques (boosting and random forest) in terms of performance parameters and prove that random forest outperforms rest of the techniques by achieving an accuracy of 95%.
Deception detection expertise.

A lively debate between Bond and Uysal (2007, Law and Human Behavior, 31, 109-115) and O'Sullivan (2007, Law and Human Behavior, 31, 117-123) concerns whether there are experts in deception detection. Two experiments sought to (a) identify expert(s) in detection and assess them twice with four tests, and (b) study their detection behavior using eye tracking. Paroled felons produced videotaped statements that were presented to students and law enforcement personnel. Two experts were identified, both female Native American BIA correctional officers. Experts were over 80% accurate in the first assessment, and scored at 90% accuracy in the second assessment. In Signal Detection analyses, experts showed high discrimination, and did not evidence biased responding. They exploited nonverbal cues to make fast, accurate decisions. These highly-accurate individuals can be characterized as experts in deception detection.
Measurement of preoperative and postoperative nasal tip projection and rotation.

To measure the effect of columellar struts and cephalic trim on tip projection and tip rotation using digitized photographs.



Using photographs of 62 patients who underwent external rhinoplasty, we retrospectively analyzed nasal tip projection (the Goode method) and rotation (nasolabial angle) before and after surgery. A cartilaginous strut was used in 36 patients, whereas 26 patients did not receive a strut. Patients were categorized into 4 subgroups, depending on the placement of a strut (placement, strut+ vs nonplacement, strut-) and the removal of the cephalic margin (removal, cephalic+ vs nonremoval, cephalic-) of the lateral crus: strut-/cephalic-, n = 17; strut+/cephalic-, n = 23; strut-/cephalic+, n = 9; strut+/cephalic+, n = 12.



Nasal tip projection, measured with the Goode method, increased from 0.58 to 0.60 (P = .02) in the strut+ group; in the strut- group, nasal tip projection did not change significantly. Nasolabial angle increased from 93.96 degrees to 100.92 degrees in the strut+/cephalic- group and from 88.30 degrees to 95.06 degrees in the strut+/cephalic+ group. Removal of the cephalic margin alone (strut-/cephalic+) hardly affected tip rotation (P = .05).



The external rhinoplasty approach did not lead to a decrease in nasal tip projection. A cartilaginous strut slightly increased nasal tip projection and also increased nasal tip rotation. This effect was accentuated by the removal of the cephalic margin of the lateral crus.
A Low-Profile Broadband 32-Slot Continuous Transverse Stub Array for Backhaul Applications in $E$ -Band

A high-gain, broadband, and low-profile continuous transverse stub antenna array is presented in E-band. This array comprises 32 long slots excited in parallel by a uniform corporate parallel-plate-waveguide beamforming network combined to a pillbox coupler. The radiating slots and the corporate feed network are built in aluminum whereas the pillbox coupler and its focal source are fabricated in printed circuit board technology. Specific transitions have been designed to combine both fabrication technologies. The design, fabrication, and measurement results are detailed, and a simple design methodology is proposed. The antenna is well matched ( $S_{11} < -13.6$  dB) between 71 and 86 GHz, and an excellent agreement is found between simulations and measurements, thus validating the proposed design. The antenna gain is higher than 29.3 dBi over the entire bandwidth, with a peak gain of 30.8 dBi at 82.25 GHz, and a beam having roughly the same half-power beamwidth in E- and H-planes. This antenna architecture is considered as an innovative solution for long-distance millimeter-waves telecommunication applications such as fifth-generation backhauling in E-band.
News Article Teaser Tweets and How to Generate Them

We define the task of teaser generation and provide an evaluation benchmark and baseline systems for it. A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read the news item. Teasers are one of the main vehicles for transmitting news to social media users. We compile a novel dataset of teasers by systematically accumulating tweets and selecting ones that conform to the teaser definition. We compare a number of neural abstractive architectures on the task of teaser generation and the overall best performing system is See et al. (2017)’s seq2seq with pointer network.
Depth map enhancement based on color and depth consistency

Current low-cost depth sensing techniques, such as Microsoft Kinect, still can achieve only limited precision. The resultant depth maps are often found to be noisy, misaligned with the color images, and even contain many large holes. These limitations make it difficult to be adopted by many graphics applications. In this paper, we propose a computational approach to address the problem. By fusing raw depth values with image color, edges and smooth priors in a Markov random field optimization framework, both misalignment and large holes can be eliminated effectively, our method thus can produce high-quality depth maps that are consistent with the color image. To achieve this, a confidence map is estimated for adaptive weighting of different cues, an image inpainting technique is introduced to handle large holes, and contrasts in the color image are also considered for an accurate alignment. Experimental results demonstrate the effectiveness of our method.
Factors Influencing the Usage of Websites: The Case of a Generic Portal in the Netherlands

In this paper, we empirically investigate an extension of the Technology Acceptance Model (TAM, Davis, 1989) to explain the individual acceptance and usage of websites. Conceptually, we examine perceived ease-of-use, usefulness, enjoyment, and their impact on attitude towards using, intention to use and actual use. The paper also introduces a new construct, “perceived visual attractiveness” of the website and suggest that it influences usefulness, enjoyment, and ease-of-use. For our empirical research we partnered with a Dutch generic portal site with over 300 000 subscribers at the time the research was conducted. The websurvey resulted in sample size of 825 respondents. The results confirmed all of the 12 hypotheses formulated. Three findings are worth mentioning in particular: (1) intention is most dominantly influenced by attitude (β = 0.51), (2) ease-of-use, enjoyment, and usefulness contribute equally to attitude towards using (β = 0.23, 0.23, and 0.17 respectively) and (3) visual attractiveness contributes remarkably well to both ease-of-use, enjoyment, and usefulness (β = 0.41, 0.35, and 0.21). Although this is not the first research to apply TAM to an internet context, we claim three major contributions: (1) a single website as the unit of analysis, (2) the introduction of visual attractiveness, and (3) the use of “real” website visitors rather than student samples. Promising future research lies in the conceptual connection between actual website features and website use, a connection for which the TAM framework provides a meaningful bridge. Factors Influencing the Usage of Websites: The Case of a Generic Portal in the Netherlands
Metamaterial-Substrate Antenna Array for MIMO Communication System

We demonstrate how a magnetic permeability enhanced metamaterial can enhance the antenna array of a multiple-input multiple-output(MIMO) communication system. The performance of a rectangular patch antenna array on a metamaterial substrate was studied relative to a similar array constructed on a conventional FR4 substrate. Differently spaced arrays were analytically compared using array correlation coefficients and mean effective gain as performance metrics. Achievable channel capacity were obtained through channel measurements made on a MIMO testbed. While results show that arrays on conventional FR4 substrates have higher capacity due to gain and efficiency factors, arrays can be made smaller, and have less mutual coupling and correlation coefficients, when using a metamaterial substrate, but the antenna built on the metamaterial substrate can be made more efficient through the use of better host materials. This was reflected in the analysis of both antenna arrays normalized to remove efficiency and gain differences where they showed similar performances. Hence, metamaterial substrates are a cost-effective solution when antenna miniaturization is a key design criteria compared to conventional substrates that achieve the same miniaturization factor without significantly sacrificing performance.
Deep People Counting with Faster R-CNN and Correlation Tracking

Crowd counting is a key problem for many computer vision tasks while most existing methods try to count people based on regression with hand-crafted features. Recently, the fast development of deep learning has resulted in many promising detectors of generic object classes. In this paper, to effective leverage the discriminability of convolutional neural networks, we propose a method to people counting based on Faster R-CNN[9] head-shoulder detection and correlation tracking. Firstly, we train a Faster R-CNN head-shoulder detector with Zeiler model to detect people with multiple poses and views. Next, we employ kernelized correlation filter(KCF)[7] to track the people and obtain the trajectory. Considering the results of the detection and tracking, we fuse the two bounding box to obtain a continuous and stable trajectory. Extensive experiments and comparison show the promise of the proposed approach.
HILOG: A Foundation for Higher-Order Logic Programming

D We describe a novel logic, called HiLog, and show that it provides a more suitable basis for logic programming than does traditional predicate logic. HiLog has a higher-order syntax and allows arbitrary terms to appear in places where predicates, functions, and atomic formulas occur in predicate calculus. But its semantics is first-order and admits a sound and complete proof procedure. Applications of HiLog are discussed, including DCG grammars, higher-order and modular logic programming, and deductive databases.
Criminal multilation of the human body in Sweden--a thirty-year medico-legal and forensic psychiatric study.

During the 30-year period 1961-1990, a total of 22 deaths with criminal multilation/dismemberment of the human body were registered in Sweden. The multilations occurred in time clusters, mostly during the summer and winter periods, and increased during the three decades, with incidence rates of 0.05, 0.1, and 0.125 per million inhabitants and year, respectively. Multilation was noted 6.6 times more often in large urban areas than in the rest of Sweden. Defensive mutilation, in order to get rid of the corpse or make its identity more difficult, was noted in ten instances, aggressive mutilation following outrageous overkilling in four, offensive mutilation (lust murder) in seven, and necromanic multilation in one instance. In the last-mentioned case the cause of death was natural, while all deaths in the first three groups were homicidal, or homicide was strongly suspected. All perpetrators were males, in six instances assisted by other persons. In more than half of the cases the perpetrator's occupation was associated with application of anatomical knowledge, e.g., butcher, physician, veterinary assistant, or hunter. The perpetrators of the defensive and aggressive mutilations were mostly disorganized, i.e., alcoholics or drug users with previous psychiatric contacts and criminal histories, while the lust murderers were mostly organized, with a history of violent crimes (including the "serial killing" type), drug abuse and mental disorders with anxiety and schizophrenia, in that order to a diminishing degree. There were differences in mode of mutilation, depending on whether the mutilation was carried out by a layman, a butcher, or a physician. In only one case was the perpetrator convicted for the mutilation act itself; in the remaining instances the manslaughter, as a more serious crime, assimilated the mutilation. When the mutilation made it impossible to establish the cause of death, the perpetrators, despite strong circumstantial evidence indicating murder, were acquitted.
MLMD: Multi-Layered Visualization for Multi-Dimensional Data

In this paper, we propose and implement a multi-layered visualization technique in 3D space called Multi-Layered Visualization for Multi-Dimensional Data (MLMD) with its corresponding interaction techniques, for visualizing multi-dimensional data. Layers of point based plots are stacked and connected in a virtual visualization cube for comparison between different dimension settings. Viewed from the side, the plot layers intrinsically form parallel coordinates plots. MLMD integrates point based plots and parallel coordinates compactly so as to present more information at a time to help data investigation. Pertinent user interactions for MLMD method to enable convenient manipulation are designed. By using MLMD and its matching interaction techniques, proper dimension settings and in-depth data perception can be achieved.
Hardware-software integrated approaches to defend against software cache-based side channel attacks

Software cache-based side channel attacks present serious threats to modern computer systems. Using caches as a side channel, these attacks are able to derive secret keys used in cryptographic operations through legitimate activities. Among existing countermeasures, software solutions are typically application specific and incur substantial performance overhead. Recent hardware proposals including the Partition-Locked cache (PLcache) and Random-Permutation cache (RPcache) , although very effective in reducing performance overhead while enhancing the security level, may still be vulnerable to advanced cache attacks. In this paper, we propose three hardware-software approaches to defend against software cache-based attacks - they present different tradeoffs between hardware complexity and performance overhead. First, we propose to use preloading to secure the PLcache. Second, we leverage informing loads, which is a lightweight architectural support originally proposed to improve memory performance, to protect the RPcache. Third, we propose novel software permutation to replace the random permutation hardware in the RPcache. This way, regular caches can be protected with hardware support for informing loads. In our experiments, we analyze various processor models for their vulnerability to cache attacks and demonstrate that even to the processor model that is most vulnerable to cache attacks, our proposed software-hardware integrated schemes provide strong security protection.
A scalable, commodity data center network architecture

Today's data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. The network architecture typically consists of a tree of routing and switching elements with progressively more specialized and expensive equipment moving up the network hierarchy. Unfortunately, even when deploying the highest-end IP switches/routers, resulting topologies may only support 50% of the aggregate bandwidth available at the edge of the network, while still incurring tremendous cost. Non-uniform bandwidth among data center nodes complicates application design and limits overall system performance.
 In this paper, we show how to leverage largely commodity Ethernet switches to support the full aggregate bandwidth of clusters consisting of tens of thousands of elements. Similar to how clusters of commodity computers have largely replaced more specialized SMPs and MPPs, we argue that appropriately architected and interconnected commodity switches may deliver more performance at less cost than available from today's higher-end solutions. Our approach requires no modifications to the end host network interface, operating system, or applications; critically, it is fully backward compatible with Ethernet, IP, and TCP.
Learning Graphical Model Structure Using L1-Regularization Paths

Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the structure of directed graphical models. Specifically, we make three contributions. First, we show how the decomposability of the MDL score, plus the ability to quickly compute entire regularization paths, allows us to efficiently pick the optimal regularization parameter on a per-node basis. Second, we show how to use L1 variable selection to select the Markov blanket, before a DAG search stage. Finally, we show how L1 variable selection can be used inside of an order search algorithm. The effectiveness of these L1-based approaches are compared to current state of the art methods on 10 datasets.
1 Positive Feedback as a Search Strategy

A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given.
Learning Continuous Control Policies by Stochastic Value Gradients

We present a unified framework for learning continuous control policies using backpropagation. It supports stochastic control by treating stochasticity in the Bellman equation as a deterministic function of exogenous noise. The product is a spectrum of general policy gradient algorithms that range from model-free methods with value functions to model-based methods without value functions. We use learned models but only require observations from the environment instead of observations from model-predicted trajectories, minimizing the impact of compounded model errors. We apply these algorithms first to a toy stochastic control problem and then to several physics-based control problems in simulation. One of these variants, SVG(1), shows the effectiveness of learning models, value functions, and policies simultaneously in continuous domains.