nils-herrmann/clean_scirepeval huggingface dataset:
Dose translation from animal to human studies revisited.

As new drugs are developed, it is essential to appropriately translate the drug dosage from one animal species to another. A misunderstanding appears to exist regarding the appropriate method for allometric dose translations, especially when starting new animal or clinical studies. The need for education regarding appropriate translation is evident from the media response regarding some recent studies where authors have shown that resveratrol, a compound found in grapes and red wine, improves the health and life span of mice. Immediately after the online publication of these papers, the scientific community and popular press voiced concerns regarding the relevance of the dose of resveratrol used by the authors. The animal dose should not be extrapolated to a human equivalent dose (HED) by a simple conversion based on body weight, as was reported. For the more appropriate conversion of drug doses from animal studies to human studies, we suggest using the body surface area (BSA) normalization method. BSA correlates well across several mammalian species with several parameters of biology, including oxygen utilization, caloric expenditure, basal metabolism, blood volume, circulating plasma proteins, and renal function. We advocate the use of BSA as a factor when converting a dose for translation from animals to humans, especially for phase I and phase II clinical trials.
Side gate HiGT with low dv/dt noise and low loss

This paper presents a novel side gate HiGT (High-conductivity IGBT) that incorporates historical changes of gate structures for planar and trench gate IGBTs. Side gate HiGT has a side-wall gate, and the opposite side of channel region for side-wall gate is covered by a thick oxide layer to reduce Miller capacitance (Cres). In addition, side gate HiGT has no floating p-layer, which causes the excess Vge overshoot. The proposed side gate HiGT has 75% smaller Cres than the conventional trench gate IGBT. The excess Vge overshoot during turn-on is effectively suppressed, and Eon + Err can be reduced by 34% at the same diode's recovery dv/dt. Furthermore, side gate HiGT has sufficiently rugged RBSOA and SCSOA.
Decision-Making Framework for Automated Driving in Highway Environments

This paper presents a decision-making framework for automated driving in highway environments. The framework is capable of reliably, robustly assessing a given highway situation (with respect to the possibility of collision) and of automatically determining an appropriate maneuver for the situation. It consists of two main components: situation assessment and strategy decision. The situation assessment component utilizes multiple complementary “threat measures” and Bayesian networks in its calculations of “threat levels” at the car and lane level to evaluate the possibility of collisions for a given highway traffic situation. The strategy decision component, designed to generate goal-directed and collision-free behaviors, automatically determines an appropriate maneuver in a given highway situation via a hierarchical state machine—such a machine both reduces the complexity of and extends a strategy model. The types of maneuver determined by the component include both simple maneuvers, such as slowing down to avoid collision with a vehicle in front, and complex maneuvers, such as lane changes and overtaking. The presented decision-making framework is tested and evaluated—both on a closed high-speed test track in simulated traffic with various driving scenarios and on public highways in real traffic through in-vehicle testing—to verify that it can provide sufficiently reliable performance for automated driving in highway environments in terms of safety, reliability, and robustness.
A Jaccard base similarity measure to improve performance of CF based recommender systems

Revolution in social computing has resulted in the wonderful evolution of recommender systems. Recommender systems maintain a repository of user profiles, created by a community of users, for generating personalized recommendations aimed at individual users. One of the approaches used in recommender systems is collaborative filtering (CF) which has become one of the most famous approaches for providing personalized recommendations to users. Nearest neighbors based methods used in CF are being widely used by many online stores to enhance users shopping experience. Nearest neighbors-based CF methods use some similarity measure techniques to find similar users/items for an active user/item. Almost all similarity measurement methods use ratings of commonly rated items while calculating similarity between a pair of users/items. Our approach works in the same manner as Jaccard similarity works. But Jaccard similarity does not consider the absolute value of rating and only considers the ratio of co-rated items. We take into account the ratio of absolute rating values which are equal in value, to the total no of co-rated items. An additional argument we take into account is the average rating value of users. We compared performance of our proposed method with many state-of-the-art similarity measures. Recommendation results from a set of real data sets show that our proposed measure has some performance improvement in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
A Dual Prediction Network for Image Captioning

General captioning practice involves a single forward prediction, with the aim of predicting the word in the next timestep given the word in the current timestep. In this paper, we present a novel captioning framework, namely Dual Prediction Network (DPN), which is end-to-end trainable and addresses the captioning problem with dual predictions. Specifically, the dual predictions consist of a forward prediction to generate the next word from the current input word, as well as a backward prediction to reconstruct the input word using the predicted word. DPN has two appealing properties: 1) By introducing an extra supervision signal on the prediction, DPN can better capture the interplay between the input and the target; 2) Utilizing the reconstructed input, DPN can make another new prediction. During the test phase, we average both predictions to formulate the final target sentence. Experimental results on the MS COCO dataset demonstrate that, benefiting from the reconstruction step, both generated predictions in DPN outperform the predictions of methods based on the general captioning practice (single forward prediction), and averaging them can bring a further accuracy boost. Overall, DPN achieves competitive results with state-of-the-art approaches, across multiple evaluation metrics.
Flat array antenna for Ku-band mobile satellite terminals

This work presents the advances in the development of an innovative flat array antenna for Ku-band mobile satellite terminals. The antenna is conceived to allow a bi-directional high data rate satellite link. The available circular surface (diameter 110 cm) integrates both a transmitting and a receiving section, operating in orthogonal linear polarizations. The TX frequency range is fully covered while the RX bandwidth is around 300 MHz arbitrarily allocated on the DVB range depending on requirements. The beam is steered in elevation through a phased array architecture not employing costly phase shifters, while the steering in azimuth is mechanical. Active BFNs in printed circuit technology allow excellent performance in terms of EIRP (42 dBW/1MHz, ITU-R S.728.1) and G/T (13 dB/°K at 40° in elevation), maintaining extremely low profile (&#60; 5 cm, radome included). Gain as high as 32 dBi is achieved thanks to the use of slotted waveguide technology, while low fabrication costs are ensured by the employment of innovative SIW (substrate integrated waveguide) structures. In the design fabrication cost is considered as a driving factor, yet providing high performance with a flat profile and thus resulting in a great commercial potentiality.
Should I use TensorFlow

Google’s Machine Learning framework TensorFlow was opensourced in November 2015  and has since built a growing community around it. TensorFlow is supposed to be flexible for research purposes while also allowing its models to be deployed productively . This work is aimed towards people with experience in Machine Learning considering whether they should use TensorFlow in their environment. Several aspects of the framework important for such a decision are examined, such as the heterogenity, extensibility and its computation graph. A pure Python implementation of linear classification is compared with an implementation utilizing TensorFlow. I also contrast TensorFlow to other popular frameworks with respect to modeling capability, deployment and performance and give a brief description of the current adaption of the framework.
Bayesian Learning via Stochastic Dynamics

The attempt to find a single "optimal" weight vector in conventional network training can lead to overfitting and poor generalization. Bayesian methods avoid this, without the need for a validation set, by averaging the outputs of many networks with weights sampled from the posterior distribution given the training data. This sample can be obtained by simulating a stochastic dynamical system that has the posterior as its stationary distribution. 1 CONVENTIONAL AND BAYESIAN LEARNING I view neural networks as probabilistic models, and learning as statistical inference. Conventional network learning finds a single "optimal" set of network parameter values, corresponding to maximum likelihood or maximum penalized likelihood inference. Bayesian inference instead integrates the predictions of the network over all possible values of the network parameters, weighting each parameter set by its posterior probability in light of the training data. 1.1 NEURAL NETWORKS AS PROBABILISTIC MODELS Consider a network taking a vector of real-valued inputs, x, and producing a vector of real-valued outputs, y, perhaps computed using hidden units. Such a network architecture corresponds to a function, I, with y = I(x, w), where w is a vector of connection weights. If we assume the observed outputs, y, are equal to y plus Gaussian noise of standard deviation (j, the network defines the conditional probability
Path planning using Matlab-ROS integration applied to mobile robots

In this paper the possibilities that Matlab provides to design, implementation and monitoring programs of autonomous navigation for mobile robots, on both simulated and real platforms, through its new toolbox for robotics will be explored. Robotics System Toolbox has established itself as a solid tool of integration between Matlab and robot operating under ROS environment. It will be studied the tools available in this toolbox that allow achieving the connection between the two platforms, in addition to the generation of algorithms of location, path planning, mapping and autonomous navigation.
The Cerebellum and Neurodevelopmental Disorders

Cerebellar dysfunction is evident in several developmental disorders, including autism, attention deficit-hyperactivity disorder (ADHD), and developmental dyslexia, and damage to the cerebellum early in development can have long-term effects on movement, cognition, and affective regulation. Early cerebellar damage is often associated with poorer outcomes than cerebellar damage in adulthood, suggesting that the cerebellum is particularly important during development. Differences in cerebellar development and/or early cerebellar damage could impact a wide range of behaviors via the closed-loop circuits connecting the cerebellum with multiple cerebral cortical regions. Based on these anatomical circuits, behavioral outcomes should depend on which cerebro-cerebellar circuits are affected. Here, we briefly review cerebellar structural and functional differences in autism, ADHD, and developmental dyslexia, and discuss clinical outcomes following pediatric cerebellar damage. These data confirm the prediction that abnormalities in different cerebellar subregions produce behavioral symptoms related to the functional disruption of specific cerebro-cerebellar circuits. These circuits might also be crucial to structural brain development, as peri-natal cerebellar lesions have been associated with impaired growth of the contralateral cerebral cortex. The specific contribution of the cerebellum to typical development may therefore involve the optimization of both the structure and function of cerebro-cerebellar circuits underlying skill acquisition in multiple domains; when this process is disrupted, particularly in early development, there could be long-term alterations of these neural circuits, with significant impacts on behavior.
A Modified Vortex Search Algorithm for Numerical Function Optimization

The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS algorithm, candidate solutions are generated around the current best solution by using a Gaussian distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some problems along. Especially, for the functions those have a number of local minimum points, to select a single point to generate candidate solutions leads the algorithm to being trapped into a local minimum point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions are generated around a number of points at each iteration pass. Computational results showed that with the help of this modification the global search ability of the existing VS algorithm is improved and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark numerical function set.
Positive and negative emotional verbal stimuli elicit activity in the left amygdala.

The human amygdala's involvement in negative emotion is well established, but relatively little is known regarding its role in positive emotion. Here we examined the neural response to emotionally positive, negative, and neutral words using fMRI. Relative to neutral words, positive and negative emotional words elicited greater activity in the left amygdala. Positive but not negative words elicited activity in dorsal and ventral striatal regions which have been linked in previous neuroimaging studies to reward and positive affect, including caudate, putamen, globus pallidus, and accumbens. These findings provide the first direct evidence that the amygdala is involved in emotional reactions elicited by both positive and negative emotional words, and further indicate that positive words additionally activate brain regions related to reward.
Multiobjective Optimal Design of an Axial Flux Permanent Magnet Generator for Directly Coupled Wind Turbines

Factors such as high efficiency, high power density, the possibility of realizing a compact multistage machine and the feasibility of applying a large number of poles make the Axial Flux Permanent Magnet (AFPM) generators highly appealing for use as low speed wind power generators. This study puts forth a multi-objective optimization of the efficiency and power density of a low speed AFPM synchronous generator. The optimization problem was formulated by means of general sizing equations and then Genetic Algorithm (GA) was utilized. This study uses a weighted fitness function which offers a tool for ascertaining the priority of objective functions. This fitness function includes two variables whereby an increase in either of them leads to more improvement in one of the objective functions than in the other. The merits of this method are especially palpable in situations where it is necessary to prioritize the objective functions as is indeed the case with generators used in wind turbines which should have not only a high efficiency but also a reduced weight and volume. Finally, the results are verified through the three dimensional Finite Element Method (3D-FEM).
Modified GDI Technique-A Power Efficient Method For Digital Circuit Design

This paper presents logic style compariso ns based on different logic functions and claimed m odified Gate Diffusion Input logic (Mod-GDI) to be much more power-efficient than Gate Diffusion Input logic (GDI) and complementary CMOS logic design. However, DC and Tr ansient analysis performed on more efficient modifi ed Gate Diffusion Input logic (Mod-GDI) circuit realization s and a wider range of different logic cells, as we ll as the use of practical circuit arrangements reveal Mod-GDI to be superior to GDI and CMOS in the majority cases wit h respect to speed, area, power dissipation, and power-delay pro ducts. This manuscript shows that Mod-GDI is the logic style of preference for the realization of arbitrary combinational circuits, if low voltage, low power, and small power-delay products are of concern. All Simulations are perfor med through PSPICE based on 0.18 μm CMOS technology, and results show power characteristics of Mod-GDI technique of low power digital circuit design. Simulation results shows up to 45% reduction in power-delay product in Mod-GDI. Mod-GDI approach allows realization of a broad variety of multifaceted logic functions by means of only two transistors. T his technique is appropriate for designing of fast, low power circuits, using reduced number of transistor (as compared to CMOS techniques), while improving power characteris tics.
Approaches to Measuring the Difficulty of Games in Dynamic Difficulty Adjustment Systems

In this article, three approaches are proposed for measuring difficulty that can be useful in developing Dynamic Difficulty Adjustment (DDA) systems in different game genres. Our analysis of the existing DDA systems shows that there are three ways to measure the difficulty of the game: using the formal model of gameplay, using the features of the game, and direct examination of the player. These approaches are described in this article and supplemented by appropriate examples of DDA implementations. In addition, the article describes the distinction between task complexity and task difficulty in DDA systems. Separating task complexity (especially the structural one) is suggested, which is an objective characteristic of the task, and task difficulty, which is related to the interaction between the task and the task performer.
Efficient algorithms for supersingular isogeny Diffie-Hellman

We propose a new suite of algorithms that significantly improve the performance of supersingular isogeny Diffie-Hellman (SIDH) key exchange. Subsequently, we present a full-fledged implementation of SIDH that is geared towards the 128-bit quantum and 192-bit classical security levels. Our library is the first constant-time SIDH implementation and is more than 2.5 times faster than the previous best (non-constant-time) SIDH software. The high speeds in this paper are driven by compact, inversion-free point and isogeny arithmetic and fast SIDH-tailored field arithmetic: on an Intel Haswell processor, generating ephemeral public keys takes 51 million cycles for Alice and 59 million cycles for Bob while computing the shared secret takes 47 million and 57 million cycles, respectively. The size of public keys is only 751 bytes, which is significantly smaller than most of the popular post-quantum key exchange alternatives. Ultimately, the size and speed of our software illustrates the strong potential of SIDH as a post-quantum key exchange candidate and we hope that these results encourage a wider cryptanalytic effort.
SDNetkit: A testbed for experimenting SDN in multi-domain networks

Mininet is the de-facto standard simulation environment for experimenting with SDN enabled networks based on the OpenFlow protocol. Although Mininet is powerful and not resource hungry, it has a strong limitation: it is not possible to use it for networks in which both OpenFlow and standard distributed routing protocols (e.g. Open Short Path First, OSPF) simultaneously run. In this paper we present SDNetkit, an enhanced release of the widely used Netkit network emulator that overcomes the limitation imposed by Mininet. We improved Netkit by adding all needed software to run OpenFlow based networks (e.g. OpenVSwitch and the Ryu framework). We show two use cases in which OpenFlow and standard protocols coexist. In particular, we address interoperability problems by presenting one use case in which OpenFlow nodes interact with standard ones (e.g. OSPF routers) in multi-domain networks, as well as one use case in which the OpenFlow protocol and OSPF run on the same machine, discussing some problems related to specific configurations. We believe that having the possibility to experiment SDN also in presence of interoperability scenarios results in opening to new research perspectives.
Image Processing on DSP Environment Using OpenCV

This paper presents image conversion, including C++ implementation, memory constraints, and floatingpoint support to integrate in the accelerators or co-processors environment. A new function of converting an image file to data file (.dat) using OpenCV to optimize image processing systems on the TMS320C6713 DSK digital signal processor (DSP) has been developed. The image of all formats and all sizes can be converted efficiently to suite the end user requirements. The function so created supports both 8-bit (uint8) and 16-bit (uint16) raw pixel depth formats for uploading the image data in the DSP environment. The converted image is then loaded to the DSP and a contrast stretching filter is applied to enhance the contrast of the image. The results obtained are studied for possible implementation in real-time applications.  DSP, OpenCV, Contrast Stretching, Image
Ring oscillators for CMOS process tuning and variability control

Test structures utilizing ring oscillators to monitor MOSFET ac characteristics for digital CMOS circuit applications are described. The measurements provide information on the average behavior of sets of a few hundred MOSFETs under high speed switching conditions. The design of the ring oscillators is specifically tailored for process centering and monitoring of variability in circuit performance in the manufacturing line as well as in the product. The delay sensitivity to key MOSFET parameter variations in a variety of ring oscillator designs is studied using a compact model for partially depleted silicon on insulator(PD-SOI) technology, but the analysis is equally valid for conventional bulk Si technology. Examples of hardware data illustrating the use of this methodology are taken primarily from experimental hardware in the 90-nm CMOS technology node in PD-SOI. The design and data analysis techniques described here allow very rapid investigation of the sources of variations in circuit delays.