nils-herrmann/clean_scirepeval huggingface dataset:
Design of a Compact High-Efficiency Circularly Polarized Monopulse Cavity-Backed Substrate Integrated Waveguide Antenna

A novel compact monopulse cavity-backed substrate integrated waveguide (SIW) antenna is proposed. The antenna consists of an array of four circularly polarized (CP) cavity-backed SIW antennas, three dual-mode hybrid coupler, and three input ports. TE10 and TE20 modes are excited in the dual-mode hybrid to produce sum and difference patterns, respectively. The antenna is modeled with a fast full-wave hybrid numerical method and also simulated using full-wave Ansoft HFSS. The whole antenna is integrated on a two-layer dielectric with the size of 42 mm × 36 mm. A prototype of the proposed monopulse antenna at the center frequency of 9.9 GHz is manufactured. Measured results show -10-dB impedance bandwidth of 2.4%, 3-dB axial ratio (AR) bandwidth of 1.75%, 12.3-dBi gain, and -28-dB null depth. The proposed antenna has good monopulse radiation characteristics, high efficiency, and can be easily integrated with planar circuits.
DAP3D-Net: Where, what and how actions occur in videos?

Action parsing in videos with complex scenes is an interesting but challenging task in computer vision. In this paper, we propose a novel deep model based on 3D CNN (convolutional neural network) and LSTM (long short-term memory) module with a multi-task learning manner for effective Deep Action Parsing (DAP3D-Net) in videos. Particularly in the training phase, each action clip, sliced to several short consecutive segments, is fed into 3D CNN followed by LSTM to model the whole action dynamic information, so that action localization, classification and attributes learning can be jointly optimized via our deep model. Once the DAP3D-Net is trained, for an upcoming test video, we can describe each individual action in the video simultaneously as: Where the action occurs; What the action is and How the action is performed. To well demonstrate the effectiveness of the proposed DAP3D-Net, we also contribute a new Numerous-category Aligned Synthetic Action dataset, i.e., NASA, which consists of 200,000 action clips of 300 categories and with 33 pre-defined action attributes in two hierarchical levels (i.e., low-level attributes of basic body part movements and high-level attributes related to action motion). We learn DAP3D-Net using the NASA dataset and then evaluate it on our collected Human Action Understanding (HAU) dataset and the public THUMOS dataset. Experimental results show that our approach can accurately localize, categorize and describe multiple actions in realistic videos.
Model-driven GUI generation and navigation for Android BIS apps

This paper presents our approach for producing graphical user interfaces (GUIs) for functionally rich business information system (BIS) prototypes, upon a mobile platform. Those prototypes are specified with annotated UML class diagrams. Navigation in the generated GUIs is allowed through the semantic links that match the associations and cardinalities among the conceptual domain entities, as expressed in the model. We start by reviewing the Android scaffolding for producing flexible GUIs for mobile devices. The latter can present rather different displays, in terms of size, orientation and resolution. Then we show how our model-based generative technique allows producing prototypes that match both the Android GUIs requirements, while implementing our model-driven approach for user navigation.
A convex-analysis perspective on disjunctive cuts

We treat with tools from convex analysis the general problem of cutting planes, separating a point from a (closed convex) set P . Crucial for this is the computation of extreme points in the so-called reverse polar set, introduced by E. Balas in 1979. In the polyhedral case, this enables the computation of cuts that define facets of P . We exhibit three (equivalent) optimization problems to compute such extreme points; one of them corresponds to selecting a specific normalization to generate cuts. We apply the above development to the case where P is (the closed convex hull of) a union, and more particularly a union of polyhedra (case of disjunctive cuts). We conclude with some considerations on the design of efficient cut generators. The paper also contains an appendix, reviewing some fundamental concepts of convex analysis.
Learning to Detect Vehicles by Clustering Appearance Patterns

This paper studies efficient means in dealing with intracategory diversity in object detection. Strategies for occlusion and orientation handling are explored by learning an ensemble of detection models from visual and geometrical clusters of object instances. An AdaBoost detection scheme is employed with pixel lookup features for fast detection. The analysis provides insight into the design of a robust vehicle detection system, showing promise in terms of detection performance and orientation estimation accuracy.
Improved Accounting for Differentially Private Learning

We consider the problem of differential privacy accounting, i.e. estimation of privacy loss bounds, in machine learning in a broad sense. We propose two versions of a generic privacy accountant suitable for a wide range of learning algorithms. Both versions are derived in a simple and principled way using well-known tools from probability theory, such as concentration inequalities. We demonstrate that our privacy accountant is able to achieve state-of-the-art estimates of DP guarantees and can be applied to new areas like variational inference. Moreover, we show that the latter enjoys differential privacy at minor cost.
Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review

The rapid growth in the use of mobile phone applications (apps) provides the opportunity to increase access to evidence-based mental health care.



Our goal was to systematically review the research evidence supporting the efficacy of mental health apps for mobile devices (such as smartphones and tablets) for all ages.



A comprehensive literature search (2008-2013) in MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, PsycINFO, PsycTESTS, Compendex, and Inspec was conducted. We included trials that examined the effects of mental health apps (for depression, anxiety, substance use, sleep disturbances, suicidal behavior, self-harm, psychotic disorders, eating disorders, stress, and gambling) delivered on mobile devices with a pre- to posttest design or compared with a control group. The control group could consist of wait list, treatment-as-usual, or another recognized treatment.



In total, 5464 abstracts were identified. Of those, 8 papers describing 5 apps targeting depression, anxiety, and substance abuse met the inclusion criteria. Four apps provided support from a mental health professional. Results showed significant reductions in depression, stress, and substance use. Within-group and between-group intention-to-treat effect sizes ranged from 0.29-2.28 and 0.01-0.48 at posttest and follow-up, respectively.



Mental health apps have the potential to be effective and may significantly improve treatment accessibility. However, the majority of apps that are currently available lack scientific evidence about their efficacy. The public needs to be educated on how to identify the few evidence-based mental health apps available in the public domain to date. Further rigorous research is required to develop and test evidence-based programs. Given the small number of studies and participants included in this review, the high risk of bias, and unknown efficacy of long-term follow-up, current findings should be interpreted with caution, pending replication. Two of the 5 evidence-based mental health apps are currently commercially available in app stores.
Improved Transition-Based Parsing and Tagging with Neural Networks

We extend and improve upon recent work in structured training for neural network transition-based dependency parsing. We do this by experimenting with novel features, additional transition systems and by testing on a wider array of languages. In particular, we introduce set-valued features to encode the predicted morphological properties and part-ofspeech confusion sets of the words being parsed. We also investigate the use of joint parsing and partof-speech tagging in the neural paradigm. Finally, we conduct a multi-lingual evaluation that demonstrates the robustness of the overall structured neural approach, as well as the benefits of the extensions proposed in this work. Our research further demonstrates the breadth of the applicability of neural network methods to dependency parsing, as well as the ease with which new features can be added to neural parsing models.
How Many Samples are Needed to Estimate a Convolutional Neural Network?

A widespread folklore for explaining the success of Convolutional Neural Networks (CNNs) is that CNNs use a more compact representation than the Fullyconnected Neural Network (FNN) and thus require fewer training samples to accurately estimate their parameters. We initiate the study of rigorously characterizing the sample complexity of estimating CNNs. We show that for an m-dimensional convolutional filter with linear activation acting on a d-dimensional input, the sample complexity of achieving population prediction error of is r Opm{ q 2, whereas the sample-complexity for its FNN counterpart is lower bounded by Ωpd{ q samples. Since, in typical settings m ! d, this result demonstrates the advantage of using a CNN. We further consider the sample complexity of estimating a onehidden-layer CNN with linear activation where both the m-dimensional convolutional filter and the r-dimensional output weights are unknown. For this model, we show that the sample complexity is r O ` pm` rq{ 2 ̆ when the ratio between the stride size and the filter size is a constant. For both models, we also present lower bounds showing our sample complexities are tight up to logarithmic factors. Our main tools for deriving these results are a localized empirical process analysis and a new lemma characterizing the convolutional structure. We believe that these tools may inspire further developments in understanding CNNs.
A Quantitative Study of Small Disjuncts

Systems that learn from examples often express the learned concept in the form of a disjunctive description. Disjuncts that correctly classify few training examples are known as small disjuncts and are interesting to machine learning researchers because they have a much higher error rate than large disjuncts. Previous research has investigated this phenomenon by performing ad hoc analyses of a small number of datasets. In this paper we present a quantitative measure for evaluating the effect of small disjuncts on learning and use it to analyze 30 benchmark datasets. We investigate the relationship between small disjuncts and pruning, training set size and noise, and come up with several interesting results.
Anatomical study of the palatine aponeurosis: application to posterior palatal seal of the complete maxillary denture

The palatine aponeurosis is a thin, fibrous lamella comprising the extended tendons of the tensor veli palatini muscles, attached to the posterior border and inferior surface of the palatine bone. In dentistry, the relationship between the “vibrating line” and the border of the hard and soft palate has long been discussed. However, to our knowledge, there has been no discussion of the relationship between the palatine aponeurosis and the vibrating line(s). Twenty sides from ten fresh frozen White cadaveric heads (seven males and three females) whose mean age at death was 79 years) were used in this study. The thickness of the mucosa including the submucosal tissue was measured. The maximum length of the palatine aponeurosis on each side and the distance from the posterior nasal spine to the posterior border of the palatine aponeurosis in the midline were also measured. The relationship between the marked borderlines and the posterior border of the palatine bone was observed. The thickness of the mucosa and submucosal tissue on the posterior nasal spine and the maximum length of the palatine aponeurosis were 3.4 mm, and 12.2 mm on right side and 12.8 mm on left, respectively. The length of the palatine aponeurosis in the midline was 4.9 mm. In all specimens, the borderline between the compressible and incompressible parts corresponded to the posterior border of the palatine bone.
Bayesian decision theory in sensorimotor control

Action selection is a fundamental decision process for us, and depends on the state of both our body and the environment. Because signals in our sensory and motor systems are corrupted by variability or noise, the nervous system needs to estimate these states. To select an optimal action these state estimates need to be combined with knowledge of the potential costs or rewards of different action outcomes. We review recent studies that have investigated the mechanisms used by the nervous system to solve such estimation and decision problems, which show that human behaviour is close to that predicted by Bayesian Decision Theory. This theory defines optimal behaviour in a world characterized by uncertainty, and provides a coherent way of describing sensorimotor processes.
Recent advances in malaria genomics and epigenomics

Malaria continues to impose a significant disease burden on low- and middle-income countries in the tropics. However, revolutionary progress over the last 3 years in nucleic acid sequencing, reverse genetics, and post-genome analyses has generated step changes in our understanding of malaria parasite (Plasmodium spp.) biology and its interactions with its host and vector. Driven by the availability of vast amounts of genome sequence data from Plasmodium species strains, relevant human populations of different ethnicities, and mosquito vectors, researchers can consider any biological component of the malarial process in isolation or in the interactive setting that is infection. In particular, considerable progress has been made in the area of population genomics, with Plasmodium falciparum serving as a highly relevant model. Such studies have demonstrated that genome evolution under strong selective pressure can be detected. These data, combined with reverse genetics, have enabled the identification of the region of the P. falciparum genome that is under selective pressure and the confirmation of the functionality of the mutations in the kelch13 gene that accompany resistance to the major frontline antimalarial, artemisinin. Furthermore, the central role of epigenetic regulation of gene expression and antigenic variation and developmental fate in P. falciparum is becoming ever clearer. This review summarizes recent exciting discoveries that genome technologies have enabled in malaria research and highlights some of their applications to healthcare. The knowledge gained will help to develop surveillance approaches for the emergence or spread of drug resistance and to identify new targets for the development of antimalarial drugs and perhaps vaccines.
The pupil as a measure of emotional arousal and autonomic activation.

Pupil diameter was monitored during picture viewing to assess effects of hedonic valence and emotional arousal on pupillary responses. Autonomic activity (heart rate and skin conductance) was concurrently measured to determine whether pupillary changes are mediated by parasympathetic or sympathetic activation. Following an initial light reflex, pupillary changes were larger when viewing emotionally arousing pictures, regardless of whether these were pleasant or unpleasant. Pupillary changes during picture viewing covaried with skin conductance change, supporting the interpretation that sympathetic nervous system activity modulates these changes in the context of affective picture viewing. Taken together, the data provide strong support for the hypothesis that the pupil's response during affective picture viewing reflects emotional arousal associated with increased sympathetic activity.
Trace norm regularization and faster inference for embedded speech recognition RNNs

We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR). For compression, we introduce and study a trace norm regularization technique for training low rank factored versions of matrix multiplications. Compared to standard low rank training, we show that our method leads to good accuracy versus number of parameter trade-offs and can be used to speed up training of large models. For speedup, we enable faster inference on ARM processors through new open sourced kernels optimized for small batch sizes, resulting in 3x to 7x speed ups over the widely used gemmlowp library. Beyond LVCSR, we expect our techniques and kernels to be more generally applicable to embedded neural networks with large fully connected or recurrent layers.
Poultry markets: on the underground economy of twitter followers

Since Twitter has emerged as one of the easiest ways of reaching people, companies started using it to advertise their products. However, creating a functional network of followers to whom to promote content is not a straightforward task. On the one side, collecting followers requires time. On the other side, companies need to establish a reputation to motivate users to follow them.
 A number of websites have emerged to help Twitter users create a large network of followers. These websites promise their subscribers to provide followers in exchange for a fee or limited services free of charge but in exchange for the user's Twitter account credentials. In addition, they offer to spread their clients' promotional messages in the network. In this paper, we study the phenomenon of these Twitter Account Markets, and we show how their services are often linked to abusive behavior and compromised Twitter profiles.
Fibromyalgia Syndrome - a multidisciplinary approach.

According to American College of Rheumatology fibromyalgia syndrome (FMS) is a common health problem characterized by widespread pain and tenderness. The pain and tenderness, although chronic, present a tendency to fluctuate both in intensity and location around the body. Patients with FMS experience fatigue and often have sleep disorders. It is estimated that FMS affects two to four percent of the general population. It is most common in women, though it can also occur in men. FMS most often first occur in the middle adulthood, but it can start as early as in the teen years or in the old age. The causes of FMS are unclear. Various infectious agents have recently been linked with the development of FMS. Some genes are potentially linked with an increased risk of developing FMS and some other health problems, which are common comorbidities to FMS. It is the genes that determine individual sensitivity and reaction to pain, quality of the antinociceptive system and complex biochemistry of pain sensation. Diagnosis and therapy may be complex and require cooperation of many specialists. Rheumatologists often make the diagnosis and differentiate FMS with other disorders from the rheumatoid group. FMS patients may also require help from the Psychiatric Clinic (Out-Patients Clinic) due to accompanying mental problems. As the pharmacological treatment options are limited and only complex therapy gives relatively good results, the treatment plan should include elements of physical therapy.
On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks

As in many neural network architectures, the use of Batch Normalization (BN) has become a common practice for Generative Adversarial Networks (GAN). In this paper, we propose using Euclidean reconstruction error on a test set for evaluating the quality of GANs. Under this measure, together with a careful visual analysis of generated samples, we found that while being able to speed training during early stages, BN may have negative effects on the quality of the trained model and the stability of the training process. Furthermore, Weight Normalization, a more recently proposed technique, is found to improve the reconstruction, training speed and especially the stability of GANs, and thus should be used in place of BN in GAN training.
Estimating and Optimizing the Impact of Inventory on Consumer Choices in a Fashion Retail Setting Pol Boada Collado

In fashion retailing, the display of product inventory at the store is important to capture consumers’ attention. Higher inventory levels might allow more attractive displays and thus increase sales, in addition to avoiding stock-outs. We develop a choice model where product demand is indeed affected by inventory, and controls for product and store heterogeneity, seasonality, promotions and potential unobservable shocks in each market. We empirically test the model with daily traffic, inventory and sales data from a large retailer, at the store-day-product level. We find that the impact of inventory level on sales is positive and highly significant, even in situations of extremely high service level. The magnitude of this effect is large: each 1% increase in product-level inventory at the store increases sales of 0.58% on average. This supports the idea that inventory has a strong role in helping customers choose a particular product within the assortment. We finally describe how a retailer should optimally decide its inventory levels within a category and describe the properties of the optimal solution. Applying such optimization to our data set yields consistent and significant revenue improvements, of more than 10% for any date and store compared to current practices.  April 6, 2016.  May 17, 2017
Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots.