nils-herrmann/clean_scirepeval huggingface dataset:
Low-Profile Broadband Dual-Polarized Integrated Patch Subarray for X-Band Synthetic Aperture Radar Payload on Small Satellite

A low-profile broadband dual-polarized patch subarray is designed in this letter for a highly integrated X-band synthetic aperture radar payload on a small satellite. The proposed subarray is lightweight and has a low profile due to its tile structure realized by a multilayer printed circuit board process. The measured results confirm that the subarray yields 14-dB bandwidths from 9.15 to 10.3 GHz for H-pol and from 9.35 to 10.2 GHz for V-pol. The isolation remains better than 40 dB. The average realized gains are approximately 13 dBi for both polarizations. The sidelobe levels are 25 dB for H-pol and 20 dB for V-pol. The relative cross-polarization levels are  30 dB within the half-power beamwidth range.
TriDAR : A HYBRID SENSOR FOR EXPLOITING THE COMPLEMENTARY NATURE OF TRIANGULATION AND LIDAR TECHNOLOGIES

3D ranging technologies generally fall into either position-based (triangulation) or time-based (LIDAR) approaches. Triangulation can provide much higher precision at close to mid-range but degrades quickly with the square of distance. LIDARs can provide reasonable precision over much longer distances but, unlike triangulation, they do not improve in capabilities at short range where precision and speed are critical for many applications such as autonomous rendezvous and docking, guidance and navigation, obstacle avoidance, and inspection. This paper discusses the complementary nature of the two technologies and presents an overview of a hybrid Triangulation-LIDAR sensor (TriDAR) currently under development at Neptec. This design has been chosen as an autonomous rendezvous & docking sensor for the Hubble Robotic Vehicle (HRV) but other space and terrestrial applications are also discussed. Performance estimates of the hybrid sensor are also presented.
Exploring Trajectories of Pornography Use Through Adolescence and Emerging Adulthood.

While the scholarly literature on pornography use is growing, much of this literature has examined pornography use as a static feature that does not change. Despite this trend, pornography use, like most sexual behaviors, is likely best viewed as a dynamic feature that may shift across the developmental life span. Using a sample of 908 adults from the United States, retrospective data on pornography use through adolescence and emerging adulthood were gathered to explore trajectories of pornography use across these developmental periods. Latent mixture models suggested the presence of common patterns of use across both developmental periods. Adolescence patterns appeared to largely be distinguished by those who either engaged or did not engage with pornography, while emerging adulthood data revealed the presence of a group of experimenters who engaged in pornography through adolescence but then decreased use through their 20s. Men were found to be more likely to have consistent profiles of pornography use, while single adults were likely to have delayed entry into pornography use. Associations with adult mental health and pornography use were found, suggesting that early exposure to pornography was related to elevated current pornography use patterns and, to a lesser extent, dysfunctional pornography use. Trajectories also had a weak association with life satisfaction, with individuals reporting trajectories involving consistent pornography use reporting lower life satisfaction after controls.
Cluster head selection using hesitant fuzzy in Wireless Sensor Networks

Since Wireless Sensor Networks (WSNs) are composed of a set of sensor nodes that limit resource constraints such as energy constraints, energy consumption in WSNs is one of the challenges of these networks. One of the solutions to reduce energy consumption in WSNs is to use clustering. In clustering, cluster members send their data to their Cluster Head (CH), and the CH after collecting the data, sends them to the Base Station (BS). In clustering, choosing CHs is very important; so many methods have proposed to choose the CH. In this study, a hesitant fuzzy method with three input parameters namely, remaining energy, distance to the BS, distance to the center of cluster is proposed for efficient cluster head selection in WSNs. We define different scenarios and simulate them, then investigate the results of simulation.
Reducing Offline BCI Calibration Effort Using Weighted Adaptation Regularization with Source Domain Selection

Single-trial classification of Event-Related Potentials (ERPs) is needed in many real-world brain-computer interface (BCI) applications. However, because of individual differences, the classifier needs to be calibrated by using some labeled subject specific training samples, which may be inconvenient to obtain. In this paper we propose a weighted adaptation regularization (wAR) approach for offline BCI calibration, which uses data from other subjects to reduce the amount of labeled data required in offline single-trial classification of ERPs. Our proposed model explicitly handles class-imbalance problems which are common in many real-world BCI applications. War can improve the classification performance, given the same number of labeled subject-specific training samples, or, equivalently, it can reduce the number of labeled subject-specific training samples, given a desired classification accuracy. To reduce the computational cost of wAR, we also propose a source domain selection (SDS) approach. Our experiments show that wARSDS can achieve comparable performance with wAR but is much less computationally intensive. We expect wARSDS to find broad applications in offline BCI calibration.
Neural Relational Inference for Interacting Systems

Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics. The interplay of components can give rise to complex behavior, which can often be explained using a simple model of the system’s constituent parts. In this work, we introduce the neural relational inference (NRI) model: an unsupervised model that learns to infer interactions while simultaneously learning the dynamics purely from observational data. Our model takes the form of a variational auto-encoder, in which the latent code represents the underlying interaction graph and the reconstruction is based on graph neural networks. In experiments on simulated physical systems, we show that our NRI model can accurately recover ground-truth interactions in an unsupervised manner. We further demonstrate that we can find an interpretable structure and predict complex dynamics in real motion capture and sports tracking data.
Economic scarcity alters the perception of race.

When the economy declines, racial minorities are hit the hardest. Although existing explanations for this effect focus on institutional causes, recent psychological findings suggest that scarcity may also alter perceptions of race in ways that exacerbate discrimination. We tested the hypothesis that economic resource scarcity causes decision makers to perceive African Americans as "Blacker" and that this visual distortion elicits disparities in the allocation of resources. Studies 1 and 2 demonstrated that scarcity altered perceptions of race, lowering subjects' psychophysical threshold for seeing a mixed-race face as "Black" as opposed to "White." In studies 3 and 4, scarcity led subjects to visualize African American faces as darker and more "stereotypically Black," compared with a control condition. When presented to naïve subjects, face representations produced under scarcity elicited smaller allocations than control-condition representations. Together, these findings introduce a novel perceptual account for the proliferation of racial disparities under economic scarcity.
A survey of channel models for underwater optical wireless communication

This paper describes and assesses underwater channel models for optical wireless communication. Models considered are: inherent optical properties; vector radiative transfer theory with the small-angle analytical solution and numerical solutions of the vector radiative transfer equation (Monte Carlo, discrete ordinates and invariant imbedding). Variable composition and refractive index, in addition to background light, are highlighted as aspects of the channel which advanced models must represent effectively. Models are assessed against these aspects in terms of their ability to predict transmitted power and spatial and temporal distributions of light a specified distance from a transmitter. Monte Carlo numerical methods are found to be the most versatile but are compromised by long computational time and greater errors than other methods.
SOLILOQUY : A Cautionary Tale

The Soliloquy primitive, first proposed by the third author in 2007, is based on cyclic lattices. It has very good efficiency properties, both in terms of public key size and the speed of encryption and decryption. There are straightforward techniques for turning Soliloquy into a key exchange or other public-key protocols. Despite these properties, we abandoned research on Soliloquy after developing (2010 to 2013) a reasonably efficient quantum attack on the primitive. A similar quantum algorithm has been recently published in some highly insightful independent work by Eisenträger, Hallgren, Kitaev, and Song . However, their paper concentrates on computing unit groups of arbitrary degree number fields whereas we will show how to apply the approach to the special case of Soliloquy.
Changes and challenges of technical debt and its management during ongoing digital transformation

In this paper, we look how changes and challenges in technical debt and its management that could emerge from the ongoing digital transformation. The digital transformation ensues from companies' opportunities to join various digital ecosystems and use external digital infrastructures to extend business operations and capabilities. Those external infrastructures cannot be controlled by their users, which is interesting especially from the service, systems and software development viewpoint. This will have also impact on technical debt and its management. We outline these changes and challenges with possible scenarios that could occur with technical debt during this change.
Cognitive insight and delusion proneness: An investigation using the Beck Cognitive Insight Scale

The present study investigated the relationship between delusion proneness, as assessed using the Peters et al. Delusions Inventory [Peters, E.R., Joseph, S.A., Garety, P.A., 1999. The measurement of delusional ideation in the normal population: Introducing the PDI (Peters et al. Delusions Inventory). ], and cognitive insight, as assessed using the Beck Cognitive Insight Scale (BCIS;  A new instrument for measuring insight: The Beck Cognitive Insight Scale. ]. Two hundred undergraduate students with no history of psychotic disorder participated. Results indicated that, consistent with hypotheses, those higher in delusion proneness endorsed more certainty in their beliefs and judgment than those who were lower in delusion proneness (Self-Certainty subscale of the BCIS; p = .007). Contrary to hypotheses, however, those who were higher in delusion proneness were more open to external feedback and were more willing to acknowledge fallibility than those who were lower in delusion proneness (Self-Reflectiveness subscale of the BCIS; p = .002). The results are discussed in relation to theories of delusion formation.
Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study

With the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data. To reveal the performance of different programs for transcriptome assembly, this work analyzed some important factors, including k-mer values, genome complexity, coverage depth, directional reads, etc. Seven program conditions, four single k-mer assemblers (SK: SOAPdenovo, ABySS, Oases and Trinity) and three multiple k-mer methods (MK: SOAPdenovo-MK, trans-ABySS and Oases-MK) were tested. While small and large k-mer values performed better for reconstructing lowly and highly expressed transcripts, respectively, MK strategy worked well for almost all ranges of expression quintiles. Among SK tools, Trinity performed well across various conditions but took the longest running time. Oases consumed the most memory whereas SOAPdenovo required the shortest runtime but worked poorly to reconstruct full-length CDS. ABySS showed some good balance between resource usage and quality of assemblies. Our work compared the performance of publicly available transcriptome assemblers, and analyzed important factors affecting de novo assembly. Some practical guidelines for transcript reconstruction from short-read RNA-Seq data were proposed. De novo assembly of C. sinensis transcriptome was greatly improved using some optimized methods.
Cerebrospinal fluid and plasma biomarkers in Alzheimer disease

Intense multidisciplinary research has provided detailed knowledge of the molecular pathogenesis of Alzheimer disease (AD). This knowledge has been translated into new therapeutic strategies with putative disease-modifying effects. Several of the most promising approaches, such as amyloid-β immunotherapy and secretase inhibition, are now being tested in clinical trials. Disease-modifying treatments might be at their most effective when initiated very early in the course of AD, before amyloid plaques and neurodegeneration become too widespread. Thus, biomarkers are needed that can detect AD in the predementia phase or, ideally, in presymptomatic individuals. In this Review, we present the rationales behind and the diagnostic performances of the core cerebrospinal fluid (CSF) biomarkers for AD, namely total tau, phosphorylated tau and the 42 amino acid form of amyloid-β. These biomarkers reflect AD pathology, and are candidate markers for predicting future cognitive decline in healthy individuals and the progression to dementia in patients who are cognitively impaired. We also discuss emerging plasma and CSF biomarkers, and explore new proteomics-based strategies for identifying additional CSF markers. Furthermore, we outline the roles of CSF biomarkers in drug discovery and clinical trials, and provide perspectives on AD biomarker discovery and the validation of such markers for use in the clinic.
The postauricular fascial flap as an adjunct to Mustardé and Furnas type otoplasty.

Anterior riberation methods of otoplasty have been criticized because of the risk of anterior hematoma that can cause anterior skin necrosis, scarring, and even cartilage destruction caused by infection. As a result, cartilage-sparing otoplasty such as the Mustardé and Furnas types has been increasingly popular. However, postauricular suture extrusion may result, and recurrence rates of up to 25 percent have been recorded. In this study, cartilage-sparing otoplasty is refined by the addition of a postauricular fascial flap to reduce suture extrusion and recurrence rates. Fifty-one patients underwent otoplasty (45 bilateral, six unilateral). This technique involves the elevation of a fascial flap from the postauricular region. A new antihelical fold is then created by Mustardé sutures, and the conchal bowl is rotated by Furnas-type concha-mastoid sutures. The fascial flap is then advanced to cover the sutures with a supplementary vascularized layer to prevent suture extrusion. In addition, the advancement of the flap acts as a postauricular support to prevent recurrence. A natural-looking antihelical fold and helical rim is created by this technique. There were no hematomas. There was recurrence in eight ears (8 percent) in six patients. Two patients requested further surgery. No patients developed suture extrusion or granuloma. This is a simple and intrinsically safe procedure and does not cause irreparable complications such as anterior scarring or skin necrosis. The postauricular fascial flap seems to prevent suture extrusion. It may also help to reduce recurrence rates to acceptable levels.
Evaluating Embedded FPGA Accelerators for Deep Learning Applications

FPGA-based embedded soft vector processors can exceed the performance and energy-efficiency of embedded GPUs and DSPs for lightweight deep learning applications. For low complexity deep neural networks targeting resource constrained platforms, we develop optimized Caffe-compatible deep learning library routines that target a range of embedded accelerator-based systems between 4 -- 8 W power budgets such as the Xilinx Zedboard (with MXP soft vector processor), NVIDIA Jetson TK1 (GPU), InForce 6410 (DSP), TI EVM5432 (DSP) as well as the Adapteva Parallella board (custom multi-core with NoC). For MNIST (28×28 images) and CIFAR10 (32×32 images), the deep layer structure is amenable to MXP-enhanced FPGA mappings to deliver 1.4 -- 5× higher energy efficiency than all other platforms. Not surprisingly, embedded GPU works better for complex networks with large image resolutions.
Differential equations with applications in cancer diseases.

Mathematical modeling is a process by which a real world problem is described by a mathematical formulation. The cancer modeling is a highly challenging problem at the frontier of applied mathematics. A variety of modeling strategies have been developed, each focusing on one or more aspects of cancer.



The vast majority of mathematical models in cancer diseases biology are formulated in terms of differential equations. We propose an original mathematical model with small parameter for the interactions between these two cancer cell sub-populations and the mathematical model of a vascular tumor. We work on the assumption that, the quiescent cells' nutrient consumption is long. One the equations system includes small parameter epsilon. The smallness of epsilon is relative to the size of the solution domain.



MATLAB simulations obtained for transition rate from the quiescent cells' nutrient consumption is long, we show a similar asymptotic behavior for two solutions of the perturbed problem. In this system, the small parameter is an asymptotic variable, different from the independent variable. The graphical output for a mathematical model of a vascular tumor shows the differences in the evolution of the tumor populations of proliferating, quiescent and necrotic cells. The nutrient concentration decreases sharply through the viable rim and tends to a constant level in the core due to the nearly complete necrosis in this region.



Many mathematical models can be quantitatively characterized by ordinary differential equations or partial differential equations. The use of MATLAB in this article illustrates the important role of informatics in research in mathematical modeling. The study of avascular tumor growth cells is an exciting and important topic in cancer research and will profit considerably from theoretical input. Interpret these results to be a permanent collaboration between math's and medical oncologists.
PSWare: A publish / subscribe middleware supporting composite event in wireless sensor network

Middleware for wireless sensor network (WSN) has been proposed as an effective solution to ease the application development by providing high-level abstractions. One of the important tasks of middleware in WSN is to support event service. As an important paradigm for event service, publish / subscribe (pub/sub) can support the asynchronous data exchange for applications and has received a lot of attention in traditional distributed systems. In WSNs, however, the design of pub/sub, especially on composite events, has not been adequately addressed. In this paper, we present the design and implementation of PSWare, a pub / sub middleware for WSN which can support both primitive and composite events. Our contribution mainly includes three parts. First, we propose an event definition language (EDL), which is specifically tailored to WSNs and can achieve high expressiveness and availability in the definition of primitive and composite events. The application programmers of PSWare can use the proposed EDL to define events in a simple manner. We implemented a compiler to compile the program written in EDL into byte codes. Second, we develop a runtime environment on sensor nodes, which provide a platform to run the compiled byte codes. Finally, we propose a composite event detection protocol to detect the events in an energy-efficient fashion.
A simple graphical calculator based on a new synthesis formulas to design a bandpass filters for wireless applications

On the one hand this paper presents a theoretical method to predict the responses for the parallel coupled microstrip bandpass filters, and on the other hand proposes a new MATLAB simulation interface including all parameters design procedure to predict the filter responses. The main advantage of this developed interface calculator is to enable researchers and engineers to design and determine easily all parameters of the PCMBPF responses with high accuracy and very small CPU time. To validate the numerical method and the corresponding new interface calculator, two PCMBP filters for wireless communications are designed and compared with the commercial electromagnetic CST simulator and the fabricated prototype respectively. Measured results show good agreement with those obtained by numerical method and simulations.
Perplexity Minimization for Translation Model Domain Adaptation in Statistical Machine Translation

We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation (SMT). While techniques for domain adaptation of monolingual data can be borrowed for parallel data, we explore conceptual differences between translation model and language model domain adaptation and their effect on performance, such as the fact that translation models typically consist of several features that have different characteristics and can be optimized separately. We also explore adapting multiple (4–10) data sets with no a priori distinction between in-domain and out-of-domain data except for an in-domain development set.