nils-herrmann/clean_scirepeval huggingface dataset:
Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: The Benefit of Target Expectation Maximization

In this paper, we make two contributions to unsupervised domain adaptation (UDA) using the convolutional neural network (CNN). First, our approach transfers knowledge in all the convolutional layers through attention alignment. Most previous methods align high-level representations, e.g., activations of the fully connected (FC) layers. In these methods, however, the convolutional layers which underpin critical lowlevel domain knowledge cannot be updated directly towards reducing domain discrepancy. Specifically, we assume that the discriminative regions in an image are relatively invariant to image style changes. Based on this assumption, we propose an attention alignment scheme on all the target convolutional layers to uncover the knowledge shared by the source domain. Second, we estimate the posterior label distribution of the unlabeled data for target network training. Previous methods, which iteratively update the pseudo labels by the target network and refine the target network by the updated pseudo labels, are vulnerable to label estimation errors. Instead, our approach uses category distribution to calculate the cross-entropy loss for training, thereby ameliorating the error accumulation of the estimated labels. The two contributions allow our approach to outperform the state-of-the-art methods by +2.6% on the Office-31 dataset.
Pilot-Assisted Time-Varying Channel Estimation for OFDM Systems

In this paper, we deal with channel estimation for orthogonal frequency-division multiplexing (OFDM) systems. The channels are assumed to be time-varying (TV) and approximated by a basis expansion model (BEM). Due to the time-variation, the resulting channel matrix in the frequency domain is no longer diagonal, but approximately banded. Based on this observation, we propose novel channel estimators to combat both the noise and the out-of-band interference. In addition, the effect of a receiver window on channel estimation is also studied. Our claims are supported by simulation results, which are obtained considering Jakes' channels with fairly high Doppler spreads
Cat-inspired mechanical design of self-adaptive toes for a legged robot

Cats have protractible claws to fold their tips to keep them sharp. They protract claws while hunting and pawing on slippery surfaces. Protracted claws by tendons and muscles of toes can help cats anchoring themselves steady while their locomotion trends to slip and releasing the hold while they retract claws intentionally. This research proposes a kind of modularized self-adaptive toe mechanism inspired by cat claws to improve the extremities' contact performance for legged robot. The mechanism is constructed with four-bar linkage actuated by contact reaction force and retracted by applied spring tension. A feasible mechanical design based on several essential parameters is introduced and an integrated Sole-Toe prototype is built for experimental evaluation. Mechanical self-adaption and actual contact performance on specific surface have been evaluated respectively on a biped walking platform and a bench-top mechanical testing.
Optimization of prosthetic foot stiffness to reduce metabolic cost and intact knee loading during below-knee amputee walking: a theoretical study.

Unilateral below-knee amputees develop abnormal gait characteristics that include bilateral asymmetries and an elevated metabolic cost relative to non-amputees. In addition, long-term prosthesis use has been linked to an increased prevalence of joint pain and osteoarthritis in the intact leg knee. To improve amputee mobility, prosthetic feet that utilize elastic energy storage and return (ESAR) have been designed, which perform important biomechanical functions such as providing body support and forward propulsion. However, the prescription of appropriate design characteristics (e.g., stiffness) is not well-defined since its influence on foot function and important in vivo biomechanical quantities such as metabolic cost and joint loading remain unclear. The design of feet that improve these quantities could provide considerable advancements in amputee care. Therefore, the purpose of this study was to couple design optimization with dynamic simulations of amputee walking to identify the optimal foot stiffness that minimizes metabolic cost and intact knee joint loading. A musculoskeletal model and distributed stiffness ESAR prosthetic foot model were developed to generate muscle-actuated forward dynamics simulations of amputee walking. Dynamic optimization was used to solve for the optimal muscle excitation patterns and foot stiffness profile that produced simulations that tracked experimental amputee walking data while minimizing metabolic cost and intact leg internal knee contact forces. Muscle and foot function were evaluated by calculating their contributions to the important walking subtasks of body support, forward propulsion and leg swing. The analyses showed that altering a nominal prosthetic foot stiffness distribution by stiffening the toe and mid-foot while making the ankle and heel less stiff improved ESAR foot performance by offloading the intact knee during early to mid-stance of the intact leg and reducing metabolic cost. The optimal design also provided moderate braking and body support during the first half of residual leg stance, while increasing the prosthesis contributions to forward propulsion and body support during the second half of residual leg stance. Future work will be directed at experimentally validating these results, which have important implications for future designs of prosthetic feet that could significantly improve amputee care.
Systematic Generalization: What Is Required and Can It Be Learned?

Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task with little adaptation and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare both types of models in how much they lend themselves to a particular form of systematic generalization. Using a synthetic VQA test, we evaluate which models are capable of reasoning about all possible object pairs after training on only a small subset of them. Our findings show that the generalization of modular models is much more systematic and that it is highly sensitive to the module layout, i.e. to how exactly the modules are connected. We furthermore investigate if modular models that generalize well could be made more end-to-end by learning their layout and parametrization. We find that end-to-end methods from prior work often learn a wrong layout and a spurious parametrization that do not facilitate systematic generalization. Our results suggest that, in addition to modularity, systematic generalization in language understanding may require explicit regularizers or priors.
Feature selection techniques for intrusion detection using non-bio-inspired and bio-inspired optimization algorithms

As Internet access widens, IDS (Intrusion Detection System) is becoming a very important component of network security to prevent unauthorized use and misuse of data. An IDS routinely handles massive amounts of data traffc that contain redundant and irrelevant features, which impact the performance of the IDS negatively. Feature selection methods play an important role in eliminating unrelated and redundant features in IDS. Statistical analysis, neural networks, machine learning, data mining techniques, and support vector machine models are employed in some such methods. Good feature selection leads to better classification accuracy. Recently, bio-inspired optimization algorithms have been used for feature selection. This work provides a survey of feature selection techniques for IDS, including bio-inspired algorithms.
Thermal Stresses Associated with Part Overhang Geometry in Electron Beam Additive Manufacturing : Process Parameter Effects

For powder-bed electron beam additive manufacturing (EBAM), support structures are required when fabricating an overhang to prevent defects such as curling, which is due to the complex thermomechanical process in EBAM. In this study, finite element modeling is developed to simulate the thermomechanical process in EBAM in building overhang part. Thermomechanical characteristics such as thermal gradients and thermal stresses around the overhang build are evaluated and analyzed. The model is applied to evaluate process parameter effects on the severity of thermal stresses. The major results are summarized as follows. For a uniform set of process parameters, the overhang areas have a higher maximum temperature, a higher tensile stress, and a larger distortion than the areas above a solid substrate. A higher energy density input, e.g., a lower beam speed or a higher beam current may cause more severe curling at the overhang area.
Polite Dialogue Generation Without Parallel Data

Stylistic dialogue response generation, with valuable applications in personality-based conversational agents, is a challenging task because the response needs to be fluent, contextually-relevant, as well as paralinguistically accurate. Moreover, parallel datasets for regular-to-stylistic pairs are usually unavailable. We present three weakly-supervised models that can generate diverse, polite (or rude) dialogue responses without parallel data. Our late fusion model (Fusion) merges the decoder of an encoder-attention-decoder dialogue model with a language model trained on stand-alone polite utterances. Our label-finetuning (LFT) model prepends to each source sequence a politeness-score scaled label (predicted by our state-of-the-art politeness classifier) during training, and at test time is able to generate polite, neutral, and rude responses by simply scaling the label embedding by the corresponding score. Our reinforcement learning model (Polite-RL) encourages politeness generation by assigning rewards proportional to the politeness classifier score of the sampled response. We also present two retrievalbased, polite dialogue model baselines. Human evaluation validates that while the Fusion and the retrieval-based models achieve politeness with poorer context-relevance, the LFT and Polite-RL models can produce significantly more polite responses without sacrificing dialogue quality.
A review of data complexity measures and their applicability to pattern classification problems

It is widely accepted that the empirical behavior of classifiers strongly depends on available data. For a given problem, it is rather difficult to guess which classifier will provide the best performance. Traditional experimental studies consist of presenting accuracy of a set of classifiers on a small number of problems, without analyzing why a classifier outperforms other classification algorithms. Recently, some researchers have tried to characterize data complexity and relate it to classifier performance. In this paper, we present a review of data complexity measures in the framework of pattern classification and discuss possible applications to a number of practical problems.
A Third Method of Generation and Detection of Single-Sideband Signals

This paper presents a third method of generation and detection of a single-sideband signal. The method is basically different from either the conventional filter or phasing method in that no sharp cutoff filters or wide-band 90° phase-difference networks are needed. This system is especially suited to keeping the signal energy confined to the desired bandwidth. Any unwanted sideband occupies the same band as the desired sideband, and the unwanted sideband in the usual sense is not present.
Analyzing Payment Based Question and Answering Service

Community based question answering (CQA) services receive a large volume of questions today. It is increasingly challenging to motivate domain experts to give timely answers. Recently, payment-based CQA services explore new incentive models to engage real-world experts and celebrities by allowing them to set a price on their answers. In this paper, we perform a data-driven analysis on Fenda, a paymentbased CQA service that has gained initial success with this incentive model. Using a large dataset of 220K paid questions (worth 1 million USD) over two months, we examine how monetary incentives affect different players in the system and their over-time engagement. Our study reveals several key findings: while monetary incentive enables quick answers from experts, it also drives certain users to aggressively game the systems for profits. In addition, this incentive model turns CQA service into a supplier-driven marketplace where users need to proactively adjust their price as needed. We find famous people are unwilling to lower their price, which in turn hurts their income and engagement-level over time. Based on our results, we discuss the implications to future paymentbased CQA design.
Innovations in Parallel Corpus Search Tools

Recent years have seen an increased interest in and availability of parallel corpora. Large corpora from international organizations (e.g. European Union, United Nations, European Patent Office), or from multilingual Internet sites (e.g. OpenSubtitles) are now easily available and are used for statistical machine translation but also for online search by different user groups. This paper gives an overview of different usages and different types of search systems. In the past, parallel corpus search systems were based on sentence-aligned corpora. We argue that automatic word alignment allows for major innovations in searching parallel corpora. Some online query systems already employ word alignment for sorting translation variants, but none supports the full query functionality that has been developed for parallel treebanks. We propose to develop such a system for efficiently searching large parallel corpora with a powerful query language.
Anti-influenza viral effects of honey in vitro: potent high activity of manuka honey.

Influenza viruses are a serious threat to human health and cause thousands of deaths annually. Thus, there is an urgent requirement for the development of novel anti-influenza virus drugs. Therefore, the aim of this study was to evaluate the anti-influenza viral activity of honey from various sources.



Antiviral activities of honey samples were evaluated using MDCK cells. To elucidate the possible mechanism of action of honey, plaque inhibition assays were used. Synergistic effects of honey with known anti-influenza virus drugs such as zanamivir or oseltamivir were tested.



Manuka honey efficiently inhibited influenza virus replication (IC50 = 3.6 ± 1.2 mg/mL; CC50 = 82.3 ± 2.2 mg/mL; selective index = 22.9), which is related to its virucidal effects. In the presence of 3.13 mg/mL manuka honey, the IC50 of zanamivir or oseltamivir was reduced to nearly 1/1000th of their single use.



Our results showed that honey, in general, and particularly manuka honey, has potent inhibitory activity against the influenza virus, demonstrating a potential medicinal value.
Evaluation of Remote Sensing Aerial Systems In Existing Transportation Practices

The application of small Remotely-Controlled (R/C) aircraft for aerial photography presents many unique advantages over manned aircraft due to their lower acquisition cost, lower maintenance issue, and superior flexibility. The extraction of reliable information from these images could benefit DOT engineers in a variety of research topics including, but not limited to work zone management, traffic congestion, safety, and environmental. During this effort, one of the West Virginia University (WVU) R/C aircraft, named ‘Foamy’, has been instrumented for a proof-of-concept demonstration of aerial data acquisition. Specifically, the aircraft has been outfitted with a GPS receiver, a flight data recorder, a downlink telemetry hardware, a digital still camera, and a shutter-triggering device. During the flight a ground pilot uses one of the R/C channels to remotely trigger the camera. Several hundred high-resolution geo-tagged aerial photographs were collected during 10 flight experiments at two different flight fields. A Matlab based geo-reference software was developed for measuring distances from an aerial image and estimating the geo-location of each ground asset of interest. A comprehensive study of potential Sources of Errors (SOE) has also been performed with the goal of identifying and addressing various factors that might affect the position estimation accuracy. The result of the SOE study concludes that a significant amount of position estimation error was introduced by either mismatching of different measurements or by the quality of the measurements themselves. The first issue is partially addressed through the design of a customized Time-Synchronization Board (TSB) based on a MOD 5213 embedded microprocessor. The TSB actively controls the timing of the image acquisition process, ensuring an accurate matching of the GPS measurement and the image acquisition time. The second issue is solved through the development of a novel GPS/INS (Inertial Navigation System) based on a 9-state Extended Kalman Filter (EKF). The developed sensor fusion algorithm provides a good estimation of aircraft attitude angle without the need for using expensive sensors. Through the help of INS integration, it also provides a very smooth position estimation that eliminates large jumps typically seen in the raw GPS measurements.
NOX2-dependent regulation of inflammation.

NADPH oxidase (NOX) isoforms together have multiple functions that are important for normal physiology and have been implicated in the pathogenesis of a broad range of diseases, including atherosclerosis, cancer and neurodegenerative diseases. The phagocyte NADPH oxidase (NOX2) is critical for antimicrobial host defence. Chronic granulomatous disease (CGD) is an inherited disorder of NOX2 characterized by severe life-threatening bacterial and fungal infections and by excessive inflammation, including Crohn's-like inflammatory bowel disease (IBD). NOX2 defends against microbes through the direct antimicrobial activity of reactive oxidants and through activation of granular proteases and generation of neutrophil extracellular traps (NETs). NETosis involves the breakdown of cell membranes and extracellular release of chromatin and neutrophil granular constituents that target extracellular pathogens. Although the immediate effects of oxidant generation and NETosis are predicted to be injurious, NOX2, in several contexts, limits inflammation and injury by modulation of key signalling pathways that affect neutrophil accumulation and clearance. NOX2 also plays a role in antigen presentation and regulation of adaptive immunity. Specific NOX2-activated pathways such as nuclear factor erythroid 2-related factor 2 (Nrf2), a transcriptional factor that induces antioxidative and cytoprotective responses, may be important therapeutic targets for CGD and, more broadly, diseases associated with excessive inflammation and injury.
Cephalopagus conjoined twins in a leopard cat (Prionailurus bengalensis).

Case reports of conjoined twins ("Siamese twins") in wild mammals are scarce. Most published reports of conjoined twins in mammals concern cases in man and domestic mammals. This article describes a case of cephalopagus conjoined twins in a leopard cat (Prionailurus bengalensis) collected on the island of Sumatra, Indonesia, in the period 1873-76. A review of known cases of conjoined twinning in wild mammals is given.
Outdoor navigation with a spherical amphibious robot

Traditionally, mobile robot design is based on wheels, tracks or legs with their respective advantages and disadvantages. Very few groups have explored designs with spherical morphology. During the past ten years, the number of robots with spherical shape and related studies has substantially increased, and a lot of work is done in this area of mobile robotics. Interest in robots with spherical morphology has also increased, in part due to NASA's search for an alternative design for a Mars rover since the wheel-based rover Spirit is now stuck for good in soft soil. This paper presents the spherical amphibious robot Groundbot, developed by Rotundus AB in Stockholm, Sweden, and describes in detail the navigation algorithm employed in this system.
Sampling Informative Training Data for RNN Language Models

We propose an unsupervised importance sampling approach to selecting training data for recurrent neural network (RNN) language models. To increase the information content of the training set, our approach preferentially samples high perplexity sentences, as determined by an easily queryable n-gram language model. We experimentally evaluate the heldout perplexity of models trained with our various importance sampling distributions. We show that language models trained on data sampled using our proposed approach outperform models trained over randomly sampled subsets of both the Billion Word (Chelba et al., 2014) and Wikitext-103 benchmark corpora (Merity et al., 2016).
Multi-task matrix factorization for collaborative filtering

Collaborative Filtering (CF) is widely applied to personalized recommendation systems. Traditional collaborative filtering techniques make predictions through a user-item matrix of ratings which explicitly presents user preference. With the increasingly growing number of users and items, insufficient rating data still leads to the decreasing predictive accuracy with traditional collaborative filtering approaches. In the real world, however, many different types of user feedback, e.g. review, like or not, votes etc., co-exist in many online content providers. In this paper we integrate rating data with some other new types of user feedback and propose a multi-task matrix factorization model in order for flexibly using multiple data. We use a common user feature space shared across sub-models in this model and thus the model can simultaneously train the corresponding sub-models with every training sample. Our experiments indicate that new types of user feedback really work and show improvements on predictive accuracy compared to state-of-the-art algorithms.
Review of infectious diseases in refugees and asylum seekers—current status and going forward

An unprecedented rise in the number of asylum seekers and refugees was seen in Europe in 2015, and it seems that numbers are not going to be reduced considerably in 2016. Several studies have tried to estimate risk of infectious diseases associated with migration but only very rarely these studies make a distinction on reason for migration. In these studies, workers, students, and refugees who have moved to a foreign country are all taken to have the same disease epidemiology. A common disease epidemiology across very different migrant groups is unlikely, so in this review of infectious diseases in asylum seekers and refugees, we describe infectious disease prevalence in various types of migrants. We identified 51 studies eligible for inclusion. The highest infectious disease prevalence in refugee and asylum seeker populations have been reported for latent tuberculosis (9-45%), active tuberculosis (up to 11%), and hepatitis B (up to 12%). The same population had low prevalence of malaria (7%) and hepatitis C (up to 5%). There have been recent case reports from European countries of cutaneous diphtheria, louse-born relapsing fever, and shigella in the asylum-seeking and refugee population. The increased risk that refugees and asylum seekers have for infection with specific diseases can largely be attributed to poor living conditions during and after migration. Even though we see high transmission in the refugee populations, there is very little risk of spread to the autochthonous population. These findings support the efforts towards creating a common European standard for the health reception and reporting of asylum seekers and refugees.