nils-herrmann/clean_scirepeval huggingface dataset:
Bhlhe40 controls cytokine production by T cells and is essential for pathogenicity in autoimmune neuroinflammation

TH1 and TH17 cells mediate neuroinflammation in experimental autoimmune encephalomyelitis (EAE), a mouse model of multiple sclerosis. Pathogenic TH cells in EAE must produce the pro-inflammatory cytokine granulocyte-macrophage colony stimulating factor (GM-CSF). TH cell pathogenicity in EAE is also regulated by cell-intrinsic production of the immunosuppressive cytokine interleukin 10 (IL-10). Here we demonstrate that mice deficient for the basic helix-loop-helix (bHLH) transcription factor Bhlhe40 (Bhlhe40(-/-)) are resistant to the induction of EAE. Bhlhe40 is required in vivo in a T cell-intrinsic manner, where it positively regulates the production of GM-CSF and negatively regulates the production of IL-10. In vitro, GM-CSF secretion is selectively abrogated in polarized Bhlhe40(-/-) TH1 and TH17 cells, and these cells show increased production of IL-10. Blockade of IL-10 receptor in Bhlhe40(-/-) mice renders them susceptible to EAE. These findings identify Bhlhe40 as a critical regulator of autoreactive T-cell pathogenicity.
Design of a multi-band antenna for 4G wireless systems

In this paper, a multi-band antenna for 4G wireless systems is proposed. The proposed antenna consists of a modified planar inverted-F antenna with additional branch line for wide bandwidth and a folded monopole antenna. The antenna provides wide bandwidth for covering the hepta-band LTE/GSM/UMTS operation. The measured 6-dB return loss bandwidth was 169 MHz (793 MHz-962 MHz) at the low frequency band and 1030 MHz (1700 MHz-2730 MHz) at the high frequency band. The overall dimension of the proposed antenna is 55 mm × 110 mm × 5 mm.
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős–Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible. Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.
Empty container management for intermodal transportation networks

We present a computational analysis of the effect of planning horizon length on empty container management for intermodal transportation networks. The analysis is based on an integer program that seeks to minimize total costs related to moving empty containers, subject to meeting requirements for moving loaded containers. A case study of potential container-on-barge operations within the Mississippi River basin illustrates the effects of planning horizon length on mode selection. A longer planning horizon can encourage the use of inexpensive, slow transportation modes, such as barge. The impact depends on the number and location of container storage pools.
Chaperone-mediated autophagy: a unique way to enter the lysosome world.

All cellular proteins undergo continuous synthesis and degradation. This permanent renewal is necessary to maintain a functional proteome and to allow rapid changes in levels of specific proteins with regulatory purposes. Although for a long time lysosomes were considered unable to contribute to the selective degradation of individual proteins, the discovery of chaperone-mediated autophagy (CMA) changed this notion. Here, we review the characteristics that set CMA apart from other types of lysosomal degradation and the subset of molecules that confer cells the capability to identify individual cytosolic proteins and direct them across the lysosomal membrane for degradation.
Chemolithotrophic nitrate-dependent Fe(II)-oxidizing nature of actinobacterial subdivision lineage TM3

Anaerobic nitrate-dependent Fe(II) oxidation is widespread in various environments and is known to be performed by both heterotrophic and autotrophic microorganisms. Although Fe(II) oxidation is predominantly biological under acidic conditions, to date most of the studies on nitrate-dependent Fe(II) oxidation were from environments of circumneutral pH. The present study was conducted in Lake Grosse Fuchskuhle, a moderately acidic ecosystem receiving humic acids from an adjacent bog, with the objective of identifying, characterizing and enumerating the microorganisms responsible for this process. The incubations of sediment under chemolithotrophic nitrate-dependent Fe(II)-oxidizing conditions have shown the enrichment of TM3 group of uncultured Actinobacteria. A time-course experiment done on these Actinobacteria showed a consumption of Fe(II) and nitrate in accordance with the expected stoichiometry (1:0.2) required for nitrate-dependent Fe(II) oxidation. Quantifications done by most probable number showed the presence of 1 × 104 autotrophic and 1 × 107 heterotrophic nitrate-dependent Fe(II) oxidizers per gram fresh weight of sediment. The analysis of microbial community by 16S rRNA gene amplicon pyrosequencing showed that these actinobacterial sequences correspond to ∼0.6% of bacterial 16S rRNA gene sequences. Stable isotope probing using 13CO2 was performed with the lake sediment and showed labeling of these Actinobacteria. This indicated that they might be important autotrophs in this environment. Although these Actinobacteria are not dominant members of the sediment microbial community, they could be of functional significance due to their contribution to the regeneration of Fe(III), which has a critical role as an electron acceptor for anaerobic microorganisms mineralizing sediment organic matter. To the best of our knowledge this is the first study to show the autotrophic nitrate-dependent Fe(II)-oxidizing nature of TM3 group of uncultured Actinobacteria.
Sketch-based manga retrieval using manga109 dataset

Manga (Japanese comics) are popular worldwide. However, current e-manga archives offer very limited search support, i.e., keyword-based search by title or author. To make the manga search experience more intuitive, efficient, and enjoyable, we propose a manga-specific image retrieval system. The proposed system consists of efficient margin labeling, edge orientation histogram feature description with screen tone removal, and approximate nearest-neighbor search using product quantization. For querying, the system provides a sketch-based interface. Based on the interface, two interactive reranking schemes are presented: relevance feedback and query retouch. For evaluation, we built a novel dataset of manga images, Manga109, which consists of 109 comic books of 21,142 pages drawn by professional manga artists. To the best of our knowledge, Manga109 is currently the biggest dataset of manga images available for research. Experimental results showed that the proposed framework is efficient and scalable (70 ms from 21,142 pages using a single computer with 204 MB RAM).
Group Activity Recognition with Differential Recurrent Convolutional Neural Networks

Human group activity recognition has drawn the attention of researchers worldwide because of the significant role it plays in many applications, including video surveillance and public security. Existing solutions for group activity recognition rely on human detection and tracking. To ensure high detection accuracy, current state-of-the-art tracking techniques require human supervision to identify objects of interest before automatic tracking can take place. This limitation has prevented existing approaches from being used in real-world applications. In scenarios when human supervision is unavailable, tracking algorithms could generate inaccurate trajectories and cause a decrease in performance for the existing group analysis methods. To address the aforementioned drawbacks, we investigate in this paper an end-to-end deep model, Differential Recurrent Convolutional Neural Networks (DRCNN). Our model consists of convolutional neural networks (CNN) and stacked differential long short-term memory (DLSTM) networks. It takes sequential raw video data as input and does not consider each group member as an individual object. Different from traditional non-end-to-end solutions which separate the steps of feature extraction and parameter learning, DRCNN utilizes a unified deep model to optimize the parameters of CNN and LSTM hand in hand. It thus has the potential of generating a more harmonious model. In addition, taking advantage of the semantic representation of CNN and the memory states of DLSTM, DRCNN has strong capabilities in understanding complex scene semantics and group dynamics. Extensive experimental studies indicate that the proposed technique can accomplish the task of fully automatic group activity recognition without sacrificing performance, and even outperforms the human-aided state-ofthe- art methods on two benchmark group activity datasets. To the best of our knowledge, this is the first end-to-end group activity recognition technique ever proposed.
Novel Closed-Form Expressions for Effective Electromagnetic Parameters of Honeycomb Radar-Absorbing Structure

Novel closed-form expressions for effective material properties of honeycomb radar-absorbing structure (RAS) are proposed. These expressions, which are derived from strong fluctuation theory with anisotropic correlation function, consist of two parts: 1) the initial value part and 2) the dispersion characteristic part. Compared with the classical closed-form formulas, the novel expressions provide for a better formulation of the effective electromagnetic parameters of honeycomb RAS, which are characterized by well-behaved increase in wide frequency band. The good agreement between the theoretical results and the existing experimental data confirms the validity of the proposed expressions. Furthermore, a linear monomial dispersion characteristic function, which argues not for the absolute frequency value, but the relative frequency displacement of a frequency point relative to the frequency of initial value, is introduced to replace the polynomial expansion of the unknown correlation part in strong fluctuation theory. Such replacement reveals the near-linear relationship between the undetermined coefficients of monomial function and the coating thickness of honeycomb RAS. Compared with polynomial fitting method, which is based on polynomial expansion, this technique can further support the prediction of undetermined coefficients, when simulation results or measurement data are not available.
Forensic analysis of the ChatSecure instant messaging application on android smartphones

We present the forensic analysis of the artifacts generated on Android smartphones by ChatSecure, a secure Instant Messaging application that provides strong encryption for transmitted and locally-stored data to ensure the privacy of its users. We show that ChatSecure stores local copies of both exchanged messages and files into two distinct, AES-256 encrypted databases, and we devise a technique able to decrypt them when the secret passphrase, chosen by the user as the initial step of the encryption process, is known. Furthermore, we show how this passphrase can be identified and extracted from the volatile memory of the device, where it persists for the entire execution of ChatSecure after having been entered by the user, thus allowing one Please, cite as: Cosimo Anglano, Massimo Canonico, Marco Guazzone, “Forensic Analysis of the ChatSecure Instant Messaging Application on Android Smartphones,” Digital Investigation, Volume 19, December 2016, Pages 44–59, DOI: 10.1016/j.diin.2016.10.001  http://dx.doi.org/10.1016/j.diin.2016.10.001 ∗Corresponding author. Address: viale T. Michel 11, 15121 Alessandria (Italy).  Email addresses: cosimo.anglano@uniupo.it (Cosimo Anglano), massimo.canonico@uniupo.it (Massimo Canonico), marco.guazzone@uniupo.it (Marco Guazzone)  2016 to carry out decryption even if the passphrase is not revealed by the user. Finally, we discuss how to analyze and correlate the data stored in the databases used by ChatSecure to identify the IM accounts used by the user and his/her buddies to communicate, as well as to reconstruct the chronology and contents of the messages and files that have been exchanged among them. For our study we devise and use an experimental methodology, based on the use of emulated devices, that provides a very high degree of reproducibility of the results, and we validate the results it yields against those obtained from real smartphones.
A bibliometric analysis of micro/nano-bubble related research: current trends, present application, and future prospects

With great versatile characteristics, micro/nano-bubble related research have attracted much attention due to their extensive applications in the last half century. Researchers not merely focus on their physi-chemical properties, but also aim at their well-controlled generation methods and potential adhibition field. It can be expected that the future prospects of micro/nano-bubble related research will be tremendous and that there will be even more to be explored. In this case study, a bibliometric analysis was conducted to evaluate micro/nano-bubble related research from 1991 to 2014, based on the Science Citation Index EXPANDED database. The Ultrasound in Medicine and Biology with the highest h-index of 56 is the leading journal in this field, publishing 6.9 % of articles over this period, followed by Langmuir and Journal of the Acoustical Society of America. USA and the Univ Toronto, Canada were the most productive country and institution, respectively, while the USA, was the most internationally collaborative and had the highest h-index (111) of all countries. A new method named “word cluster analysis” was successfully applied to trace the research hotspots. Innovation in detection means and novel pathways for medical applications via micro/nano-bubble is considered to relate to the increasingly new types and varieties of diseases or cancers, as well as the well-controlled generation of micro/nano-bubbles.
Spatial Uncertainty Sampling for End-to-End Control

End-to-end trained neural networks (NNs) are a compelling approach to autonomous vehicle control because of their ability to learn complex tasks without manual engineering of rule-based decisions. However, challenging road conditions, ambiguous navigation situations, and safety considerations require reliable uncertainty estimation for the eventual adoption of full-scale autonomous vehicles. Bayesian deep learning approaches provide a way to estimate uncertainty by approximating the posterior distribution of weights given a set of training data. Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty. In this paper, we propose a Bayesian NN for end-to-end control that estimates uncertainty by exploiting feature map correlation during training. This approach achieves improved model fits, as well as tighter uncertainty estimates, than traditional element-wise dropout. We evaluate our algorithms on a challenging dataset collected over many different road types, times of day, and weather conditions, and demonstrate how uncertainties can be used in conjunction with a human controller in a parallel autonomous setting.
Endocannabinoid signalling in reward and addiction

Brain endocannabinoid (eCB) signalling influences the motivation for natural rewards (such as palatable food, sexual activity and social interaction) and modulates the rewarding effects of addictive drugs. Pathological forms of natural and drug-induced reward are associated with dysregulated eCB signalling that may derive from pre-existing genetic factors or from prolonged drug exposure. Impaired eCB signalling contributes to dysregulated synaptic plasticity, increased stress responsivity, negative emotional states and cravings that propel addiction. Understanding the contributions of eCB disruptions to behavioural and physiological traits provides insight into the eCB influence on addiction vulnerability.
Unilateral cleft lip repair: an anatomical subunit approximation technique.

A technique of unilateral cleft lip repair is described. The repair draws from a variety of previously described repairs and adheres to a concept of anatomical subunits of the lip. Cases from within the spectrum of the deformity have been chosen from a series of 144 consecutive cases to demonstrate the applicability of the technique in all forms of unilateral cleft lip.



Incisions cross the lip perpendicular to the cutaneous roll at the cleft side peak of Cupid's bow of the medial lip and at the base of the philtral column of the lateral lip. Above this level, incisions ascend the lip to allow for approximation along a line symmetrical with the non-cleft-side philtral column. Incisions then ascend superolaterally bordering the lip columellar crease to the point of closure in the nostril sill. A Rose-Thompson lengthening effect occurs just above the level of the cutaneous roll. If necessary, a small triangle positioned just above the cutaneous roll is often used. Any central vermilion deficiency is augmented by a laterally based triangular vermilion flap from the lateral lip element.



Since January of 2000, this technique has been used in 144 consecutive unilateral cleft lip repairs. The inferior triangle is small (average, 1.24 mm; range, 0 to 2 mm). The technique can be applied to all degrees of unilateral cleft lip.



A technique of unilateral cleft lip repair is described. The repair allows for a repair line that ascends the lip at the seams of anatomical subunits.
High gain wideband 2×2 microstrip array antenna using RIS and Fabry Perot Cavity resonator

This paper presents a high gain wide band 2×2 microstrip array antenna. The microstrip array antenna (MSA) is fabricated on inexpensive FR4 substrate and placed 1mm above ground plane to improve the bandwidth and efficiency of the antenna. A reactive impedance surface (RIS) consisting of 13×13 array of 4 mm square patches with inter-element spacing of 1 mm is fabricated on the bottom side of FR4 substrate. RIS reduces the coupling between the ground plane and MSA array and therefore increases the efficiency of antenna. It enhances the bandwidth and gain of the antenna. RIS also helps in reduction of SLL and cross polarization. This MSA array with RIS is place in a Fabry Perot cavity (FPC) resonator to enhance the gain of the antenna. 2×2 and 4×4 array of square parasitic patches are fed by MSA array fabricated on a FR4 superstrate which forms the partially reflecting surface of FPC. The FR4 superstrate layer is supported with help of dielectric rods at the edges with air at about λ0/2 from ground plane. A microstrip feed line network is designed and the printed MSA array is fed by a 50 Ω coaxial probe. The VSWR is <; 2 is obtained over 5.725-6.4 GHz, which covers 5.725-5.875 GHz ISM WLAN frequency band and 5.9-6.4 GHz satellite uplink C band. The antenna gain increases from 12 dB to 15.8 dB as 4×4 square parasitic patches are fabricated on superstrate layer. The gain variation is less than 2 dB over the entire band. The antenna structure provides SLL and cross polarization less than -2ο dB, front to back lobe ratio higher than 20 dB and more than 70 % antenna efficiency. A prototype structure is realized and tested. The measured results satisfy with the simulation results. The antenna can be a suitable candidate for access point, satellite communication, mobile base station antenna and terrestrial communication system.
Fat Injection: A Systematic Review of Injection Volumes by Facial Subunit

Fat grafting to the aging face has become an integral component of esthetic surgery. However, the amount of fat to inject to each area of the face is not standardized and has been based mainly on the surgeon’s experience. The purpose of this study was to perform a systematic review of injected fat volume to different facial zones. A systematic review of the literature was performed through a MEDLINE search using keywords “facial,” “fat grafting,” “lipofilling,” “Coleman technique,” “autologous fat transfer,” and “structural fat grafting.” Articles were then sorted by facial subunit and analyzed for: author(s), year of publication, study design, sample size, donor site, fat preparation technique, average and range of volume injected, time to follow-up, percentage of volume retention, and complications. Descriptive statistics were performed. Nineteen articles involving a total of 510 patients were included. Rhytidectomy was the most common procedure performed concurrently with fat injection. The mean volume of fat injected to the forehead is 6.5 mL (range 4.0–10.0 mL); to the glabellar region 1.4 mL (range 1.0–4.0 mL); to the temple 5.9 mL per side (range 2.0–10.0 mL); to the eyebrow 5.5 mL per side; to the upper eyelid 1.7 mL per side (range 1.5–2.5 mL); to the tear trough 0.65 mL per side (range 0.3–1.0 mL); to the infraorbital area (infraorbital rim to lower lid/cheek junction) 1.4 mL per side (range 0.9–3.0 mL); to the midface 1.4 mL per side (range 1.0–4.0 mL); to the nasolabial fold 2.8 mL per side (range 1.0–7.5 mL); to the mandibular area 11.5 mL per side (range 4.0–27.0 mL); and to the chin 6.7 mL (range 1.0–20.0 mL). Data on exactly how much fat to inject to each area of the face in facial fat grafting are currently limited and vary widely based on different methods and anatomical terms used. This review offers the ranges and the averages for the injected volume in each zone. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
Prioritizing test cases for regression testing

Test case prioritization techniques schedule test cases in an order that increases their effectiveness in meeting some performance goal. One performance goal, rate of fault detection, is a measure of how quickly faults are detected within the testing process; an improved rate of fault detection can provide faster feedback on the system under test, and let software engineers begin locating and correcting faults earlier than might otherwise be possible. In previous work, we reported the results of studies that showed that prioritization techniques can significantly improve rate of fault detection. Those studies, however, raised several additional questions: (1) can prioritization techniques be effective when aimed at specific modified versions; (2) what tradeoffs exist between fine granularity and coarse granularity prioritization techniques; (3) can the incorporation of measures of fault proneness into prioritization techniques improve their effectiveness? This paper reports the results of new experiments addressing these questions.