bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the efficacy and safety of sarilumab combination therapy in patients with rheumatoid arthritis with inadequate response to conventional disease-modifying antirheumatic drugs or tumour necrosis factor α inhibitors: systematic literature review and network meta-analyses
Authors: Choy Ernest , Freemantle Nick , Proudfoot Clare , Chen Chieh-I , Pollissard Laurence , Kuznik Andreas , Van Hoogstraten Hubert , Mangan Erin , Carita Paulo , Huynh Thi-Minh-Thao
Published: February 2019

Objective

To compare efficacy and safety of subcutaneous sarilumab 200 mg and 150 mg every 2 weeks plus conventional synthetic disease-modifying antirheumatic drugs (+csDMARDs) versus other targeted DMARDs+csDMARDs and placebo+csDMARDs, in inadequate responders to csDMARDs (csDMARD-IR) or tumour necrosis factor α inhibitors (TNFi-IR).

Methods

Systematic literature review and network meta-analyses (NMA) conducted on 24 week efficacy and safety outcomes: Health Assessment Questionnaire Disability Index, modified total sharp score (mTSS, including 52 weeks), American College of Rheumatology (ACR) 20/50/70, European League Against Rheumatism Disease Activity Score 28-joint count erythrocyte sedimentation rate (DAS28)<2.6; serious infections/serious adverse events (including 52 weeks).

Results

53 trials were selected for NMA.csDMARD-IR: Sarilumab 200 mg+csDMARDs and 150 mg+csDMARDs were superior versus placebo+csDMARDs on all outcomes. Against most targeted DMARDs, sarilumab 200 mg showed no statistically significant differences, except superiority to baricitinib 2 mg, tofacitinib and certolizumab on 24 week mTSS. Sarilumab 150 mg was similar to all targeted DMARDs.TNFi-IR:Sarilumab 200 mg was similar to abatacept, golimumab, tocilizumab 4 mg and 8 mg/kg intravenously and rituximab on ACR20/50/70, superior to baricitinib 2 mg on ACR50 and DAS28<2.6 and to abatacept, golimumab, tocilizumab 4 mg/kg intravenously and rituximab on DAS28<2.6. Sarilumab 150 mg was similar to targeted DMARDs but superior to baricitinib 2 mg and rituximab on DAS28<2.6 and inferior to tocilizumab 8 mg on ACR20 and DAS28<2.6. Serious adverse events, including serious infections, appeared similar for sarilumab versus comparators.

Conclusions

Results suggest that in csDMARD-IR and TNFi-IR (a smaller network), sarilumab+csDMARD had superior efficacy and similar safety versus placebo+csDMARDs and at least similar efficacy and safety versus other targeted DMARDs+csDMARDs.
Retrospective analysis of iatrogenic diseases in cattle requiring admission to a veterinary hospital
Authors: Sala Giulia , Boccardo Antonio , Fantinato Eleonora , Coppoletta Eleonora , Bronzo Valerio , Riccaboni Pietro , Belloli Angelo Giovanni , Pravettoni Davide
Published: February 2019

Iatrogenic diseases in veterinary medicine are often related to malpractice or lack of skill. For this retrospective study, 4262 clinical records of cattle admitted to the veterinary teaching hospital of the University of Milan between 2005 and 2017 were analysed, and 121 cases (2.8 per cent), referred for an iatrogenic‐related disease, were selected. The findings showed that iatrogenic diseases were more often caused by farmers (92.6per cent) than by bovine practitioners (7.4 per cent). Iatrogenic diseases were caused mainly by the improper administration of drugs (43.0 per cent), forced extraction during calving (19.8 per cent), forced milk or colostrum feeding, which was often performed by awkward administration using a nipple bottle (14.9 per cent) or by oral oesophageal tubing (15.7 per cent). Moreover, farmers often performed medical, nursing and zootechnical procedures without adequate training. The role of the practitioner is fundamental in farmer education. Clinicians, especially in some professional branches such as neonatology, should not delegate medical procedures to untrained farmers. Effective tutoring and good communication with farmers play a key role in dairy herd health and consequently in public health. This then can lead to a decrease in improper drug administration, the prevention of antibiotic resistance and the reduction of treatment costs.
Does incorporating gear selectivity during macroscale investigations of fish growth reduce size-selective sampling bias in parameter estimates?
Authors: Li Yan , Wagner Tyler
Published: February 2019

Understanding of fish growth, the spatial variability in individual growth, and the potential drivers of such variability is a fundamental component of many ecological investigations. However, sampling gears are always size-selective, and this selectivity can result in biased parameter estimates that can lead to, for example, biased stock assessments that use growth estimates. Using seven flathead catfish (Pylodictis olivaris) populations from across the USA as an example, we investigated to what degree the incorporation of gear selectivity in growth models reduces size-selective bias in the estimation of growth parameters during macroscale investigations of fish growth. We developed a series of simulation scenarios by combining different sampling methods to obtain fish samples and different gear selectivity assumptions to estimate parameters. Results showed that the efficacy of incorporating gear selectivity in growth models to reduce size-selective sampling bias during macroscale investigations depends on multiple factors, including (i) the size distribution of small and large fish in the sample (which is a function of sampling methods), and (ii) consistency of sampling methods across populations. Incorporation of gear selectivity may reduce bias when data are lacking for large fish and when sampling methods are inconsistent across populations. Demographics of the sampled populations and the growth parameter of interest can also affect the utility of directly incorporating gear selectivity into growth models. Because multiple factors can influence the efficacy of incorporating gear selectivity into growth models, the decision to do so likely needs to be made on a case-by-case basis. This study extends the existing gear selectivity research by focusing on macroscale fish growth investigations across multiple populations and provides guidance on how to handle gear selectivity assumptions during such investigations.
Effect of heterogeneous weights on the average trapping time and two types of random walks in weighted directed networks
Authors: Zhang Huai , Zhang Hongjuan
Published: February 2019

In this paper, we define a class of weighted directed networks with the weight of its edge dominated by a weight parameter w. According to the construction of the networks, we study two types of random walks in the weighted directed networks with a trap fixed on the central node, i.e., standard random walks and mixed random walks. For the standard random walks, the trapping process is controlled by a weight parameter w which changes the transition probability of random walks. For the mixed random walks including nonnearest-neighbor hopping, the trapping process is steered by a stochastic parameter [Formula: see text], where [Formula: see text] changes the walking rule. For the above two techniques, we derive both analytically the average trapping time (ATT) as the measure of trapping efficiency, and the obtained analytical expressions are in good agreement with the corresponding numerical solutions for different values of w and [Formula: see text]. The obtained results indicate that ATT scales superlinearly with network size Nn and the weight parameter w changes simultaneously the prefactor and the leading scalings of ATT, while the stochastic parameter [Formula: see text] can only alter the prefactor of ATT and leave the leading scalings of ATT unchanged. This work may help in paving the way for understanding the effects of the link weight and nonnearest-neighbor hopping in real complex systems.
Finger-Vein Quality Assessment Based on Deep Features From Grayscale and Binary Images
Authors: Qin Huafeng , El-Yacoubi Mounim A.
Published: February 2019

Finger-vein verification is a highly secure biometric authentication that has been widely investigated over the last years. One of its challenges, however, is the possible degradation of image quality, that results in spurious and missing vein patterns, which increases the verification error. Despite recent advances in finger-vein quality assessment, the proposed solutions are limited as they depend on human expertise and domain knowledge to extract handcrafted features for assessing quality. We have proposed, recently, the first deep neural network (DNN) framework for assessing finger-vein quality, that does not require manual labeling of high and low quality images, as is the case for state of the art methods, but infers such annotations automatically based on an objective indicator, the biometric verification decision. This framework has significantly outperformed the existing methods, whether the input image is in grayscale or is binary. Motivated by these performances, we propose, in this work, a representation learning of finger vein image quality, where a DNN takes as input conjointly the grayscale and binary versions of the input image to predict vein quality. Our model allows to learn the joint representation from grayscale and binary images, for quality assessment. The experimental results, obtained on a large public dataset, demonstrates that our proposed method accurately identifies high and low quality images, and outperforms other techniques in terms of equal error rate (EER) minimization, including our previous DNN models, based either on grayscale or binary input.
INFLUENCE OF LASER BORIDING ON THE FRICTION AND WEAR OF SLIDING COUPLES WITH BEARING ALLOYS
Authors: DUDEK KAZIMIERA , SZCZYPINSKI-SALA WOJCIECH , LUBAS JANUSZ
Published: February 2019

The present study discusses the influence of laser borided surface layer on the parameters of sliding couples lubricated by engine oil. The borided layer was created by laser remelting of a surface layer of steel samples coated with amorphous boron. The study of friction, wear, and lubrication was conducted on the pairs made of steel AISI 5045 and SAE-783 and SAE-48 bearing alloys which were lubricated with 15W/40 engine oil. The research showed important differences in the friction coefficient and temperature in the tested couple with laser borided surface layer. In the couples containing the counterparts with SAE-783 bearing alloy, the tested parameter of friction was higher than in the couple composed of counterparts with SAE-48 bearing alloy. The counterparts with SAE-48 showed more intensive wear in contact with the laser borided surface layer than the counterpart with SAE 783. The process of friction at the contact area of the sliding couple with laser borided surface layer leads to the destruction of the lubricating properties of 15W/40 engine oil and reduces its resistance to scuffing and seizure.
A Gradient Weighted Finite Element Method (GW-FEM) for Static and Quasi-Static Electromagnetic Field Computation
Authors: Tang Bingxian , Li She , Cui Xiangyang
Published: February 2019

This paper presented a Gradient Weighted Finite Element Method (GW-FEM) for solving electromagnetic problems. First, the analysis domain is discretized into a set of triangular or tetrahedral elements which are easy to automatically generate. Then, Gradient Weighted influence domains are further constructed by the center element with all the adjacent elements. The Galerkin Weak form is evaluated based on these influence domains. The GW-FEM is employed here for the solution of static and quasi-static electromagnetic problems by using linear triangular or tetrahedral elements. All the properties of GW-FEM are proved theoretically and analyzed in detail. Consistency between four benchmark results is obtained by GW-FEM and analytical results verify the accuracy, stability, and potential of this method. It turns out that GW-FEM possesses potentials in the applications of electromagnetic problems.
Fundamentals of Fascinating Graphene Nanosheets: A Comprehensive Study
Authors: Sahoo Sumanta Kumar , Mallik Archana
Published: February 2019

Graphene nanosheets have attracted immense research interest among the materials science community from electronics to the biomedical field. Being the first member of two-dimensional nanomaterials family, discovered in 2004 followed by the Nobel Prize winning in 2010, it is now readily witnessing global industrial revolutions. The nanomaterial is bestowed with such unprecedented features that can be tangible to a wide spectrum of applications ranging from energy storage devices to sensor application. Enormous flattened surfaces, superior mechanical strength and flexibility, ballistic intrinsic carrier mobility, nearly transparent nature, high thermal conductivity and room-temperature ferromagnetic behavior are few of the extraordinary attributes of the monolayer of carbon nanosheets. In this comprehensive review, an attempt has been put forward to precisely revisit and represent the literature available on the fundamental properties of graphene nanomaterials. Also, the usage of its characteristic features in various applications as well as synthesis process has been briefly discussed.
Structuring Al3+-doped LiNi1∕3Co1∕3Mn1∕3O2by 3D-birdnest-shaped MnO2
Authors: Zhu Jiping , Yan Jiawei , Chen Jun , Guo Xin , Zhao Shanguang
Published: February 2019

The successfully prepared 3D-birdnest-shaped MnO2had a hollow structure and a large amount of free space among nanotubes, which was more favorable for other substances to attach on it. Due to its special structure, we prepared the LiNi[Formula: see text]Co[Formula: see text]Mn[Formula: see text]AlxO2([Formula: see text], 0.01, 0.03, 0.05, 0.08) in a simple way by employing 3D-birdnest-shaped MnO2, and revealed the influence of Al[Formula: see text]-doped in LiNi[Formula: see text]Co[Formula: see text]Mn[Formula: see text]O2. And the sample of LiNi[Formula: see text]Co[Formula: see text]Mn[Formula: see text]O2had outstanding electrochemical properties; when a small amount of Al[Formula: see text] ([Formula: see text]) was doped into the material, the cyclic performance of the material was improved significantly (90.9% capacity retention after 100 cycle), and the capacity of the LiNi[Formula: see text]Co[Formula: see text]Mn[Formula: see text]AlxO2reached 186.2[Formula: see text]mAh[Formula: see text]g[Formula: see text]. Our results provided the underlying insights needed to guide the design of cathode materials.
Electron injection model for graphene-induced efficiency enhancement in dye-sensitized solar cells
Authors: Merazga Amar , Al-Zahrani Jamilah , Al-Baradi Ateyyah , Omer Bushra , Badawi Ali , Al-Omairy Sultan
Published: February 2019

A model, correlating the reduced graphene oxide (rGO)-induced shrinking of the bandgap in rGO/TiO2 composite films to the efficiency enhancement in associated dye-sensitized solar cells, is proposed. The intercalated rGO nanosheets into TiO2 cause redistribution of the density of states (DOS) and modification of the energetics. The DOS increases with rGO proportion, [rGO], and the conduction band (CB) edge moves into the gap to regain the initial critical DOS for a mobility edge. The rate of electron injection from the dye increases proportionally to the DOS, thus enhancing the DSSC efficiency.
A lamination model for shape memory polymer/woven fabric composites
Authors: Su Xiaobin , Peng Xiongqi
Published: February 2019

Based on continuum mechanics and finite element method, a lamination model was developed for shape memory polymer composites (SMPCs) reinforced by woven fabrics. SMPCs were modeled as a laminated structure with woven fabric reinforcements embedded in shape memory polymers (SMPs). Thermo-responsive SMPs were defined by a 3D phenomenological model based on the phase transition approach, while woven fabric reinforcements were characterized by an anisotropic hyperelastic model. The proposed model was validated by comparing numerical results of a SMPC in the shape memory cycle of bending deformation with experimental data. Applications of the lamination model were demonstrated on numerical simulations of a tube made of SMPC in three shape memory cycles with different deformation modes. The proposed model is simple and applicable in the simulations of various shape memory cycles related to SMPCs. It also provides a theoretical foundation for the analysis and design optimization of woven fabric reinforced SMPC structures.
Development Cooperation in Africa: Creating New Momentum for China-U.S. Relations
Authors: Wei Song
Published: February 2019

As China projects itself as an emerging donor of development aid, its development cooperation with Africa has garnered unprecedented attention from the world. While China is faced with many challenges in aid practices in Africa despite its remarkable achievements over the past decades, both developed countries and African countries set high expectations for China’s potential contribution. Against this backdrop, it is crucial for China to enhance trilateral cooperation with developed donors and share experience with them on how to manage aid programs. Based on successful cooperation in the past, China-U.S.-Africa trilateral cooperation will not only strengthen China-U.S. bilateral ties, but also improve China’s overall aid effectiveness to Africa. In the future, China should initiate more development cooperation programs and work to create a coordinating mechanism with the United States in areas of their common understanding and interests; it should also go beyond traditional means of assistance and try to get involved in the U.S.-led public-private partnership (PPP) projects.
Miscommunication Detection and Recovery in Situated Human–Robot Dialogue
Authors: Marge Matthew , Rudnicky Alexander I.
Published: February 2019

Even without speech recognition errors, robots may face difficulties interpreting natural-language instructions. We present a method for robustly handling miscommunication between people and robots in task-oriented spoken dialogue. This capability is implemented in TeamTalk, a conversational interface to robots that supports detection and recovery from the
situated grounding problems
of referential ambiguity and impossible actions. We introduce a representation that detects these problems and a nearest-neighbor learning algorithm that selects recovery strategies for a virtual robot. When the robot encounters a grounding problem, it looks back on its interaction history to consider how it resolved similar situations. The learning method is trained initially on crowdsourced data but is then supplemented by interactions from a longitudinal user study in which six participants performed navigation tasks with the robot. We compare results collected using a general model to user-specific models and find that user-specific models perform best on measures of dialogue efficiency, while the general model yields the highest agreement with human judges. Our overall contribution is a novel approach to detecting and recovering from miscommunication in dialogue by including situated context, namely, information from a robot’s path planner and surroundings.
Applying Deep Learning for Epilepsy Seizure Detection and Brain Mapping Visualization
Authors: Hossain M. Shamim , Amin Syed Umar , Alsulaiman Mansour , Muhammad Ghulam
Published: February 2019

Deep Convolutional Neural Network (CNN) has achieved remarkable results in computer vision tasks for end-to-end learning. We evaluate here the power of a deep CNN to learn robust features from raw Electroencephalogram (EEG) data to detect seizures. Seizures are hard to detect, as they vary both inter- and intra-patient. In this article, we use a deep CNN model for seizure detection task on an open-access EEG epilepsy dataset collected at the Boston Children's Hospital. Our deep learning model is able to extract spectral, temporal features from EEG epilepsy data and use them to learn the general structure of a seizure that is less sensitive to variations. For cross-patient EEG data, our method produced an overall sensitivity of 90.00%, specificity of 91.65%, and overall accuracy of 98.05% for the whole dataset of 23 patients. The system can detect seizures with an accuracy of 99.46%. Thus, it can be used as an excellent cross-patient seizure classifier. The results show that our model performs better than the previous state-of-the-art models for patient-specific and cross-patient seizure detection task. The method gave an overall accuracy of 99.65% for patient-specific data. The system can also visualize the special orientation of band power features. We use correlation maps to relate spectral amplitude features to the output in the form of images. By using the results from our deep learning model, this visualization method can be used as an effective multimedia tool for producing quick and relevant brain mapping images that can be used by medical experts for further investigation.
Liquid Cloud Storage
Authors: Luby Michael , Padovani Roberto , Richardson Thomas J. , Minder Lorenz , Aggarwal Pooja
Published: February 2019

A
liquid system
provides durable object storage based on spreading redundantly generated data across a network of hundreds to thousands of potentially unreliable storage nodes. A liquid system uses a combination of a
large code
,
lazy repair
, and
flow storage organization
. We show that a liquid system can be operated to enable flexible and essentially optimal combinations of storage durability, storage overhead, repair bandwidth usage, and access performance.
Interview with Dene Grigar
Authors: Atzenbeck Claus
Published: February 2019

Dene Grigar is Professor and Director of The Creative Media & Digital Culture Program at Washington State University Vancouver whose research focuses on the creation, curation, preservation, and criticism of Electronic Literature, specifically building multimedial environments and experiences for live performance, installations, and curated spaces; desktop computers; and mobile media devices. She has authored 14 media works such as "Curlew" (2014), "A Villager's Tale" (2011), the "24-Hour Micro E-Lit Project" (2009), "When Ghosts Will Die" (2008), and "Fallow Field: A Story in Two Parts" (2005), as well as 56 scholarly articles and three books. She also curates exhibits of electronic literature and media art, mounting shows at the British Computer Society and the Library of Congress and for the Symposium on Electronic Art (ISEA) and the Modern Language Association (MLA), among other venues. With Stuart Moulthrop (U of Wisconsin Milwaukee) she developed the methodology for documenting born digital media, a project that culminated in an open-source, multimedia book, entitled "Pathfinders" (2015), and book of media art criticism, entitled "Traversals" (2017), for The MIT Press. She is President of the Electronic Literature Organization, Associate Editor of "Leonardo Reviews," and "Literary Studies in the Digital Age (LSDA)," and a series editor for "Electronic Literature," with Bloomsbury Press. In 2017 she was awarded the Lewis E. and Stella G. Buchanan Distinguished Professorship by her university. She also directs the Electronic Literature Lab at WSUV. In her spare time she runs, collects wine, does yoga, and misses the Gulf Coast.
A computational investigation of the propaganda model
Authors: Elejalde Erick
Published: February 2019

Erick Elejalde obtained his Ph.D. in Computer Science from the University of Concepcion, Chile in 2018. His thesis focuses on analyzing the behavior of the mass media on-line and testing socioeconomic theories using computational methods. His research interests include computational social science, online media-behavior modeling, and social networks. He works now as a research assistant at the Data Science Institute, Universidad del Desarrollo, Santiago, Chile.
"Identifying novel privacy issues of online users on social media platforms" by Ghazaleh Beigi and Huan Liu with Martin Vesely as coordinator
Authors: Beigi Ghazaleh , Liu Huan
Published: February 2019

Online users generate tremendous amounts of data. To better serve users, it is required to share the user-related data with advertisers and application developers. Socia media user-related data might make users susceptible to unintended user privacy breach. To encourage data sharing and mitigate user privacy concerns, a number of anonymization and de-anonymization algorithms have been developed to help protect privacy of users. This article introduces our recent research on online users privacy in social media. In particular, we review an approach to identifying novel privacy issues via an adversarial attack specialized for social media data. Our work sheds light on the study of new privacy risks in social media data arising from the innate heterogeneity of user-generated data.
In-Depth Analysis on Microarchitectures of Modern Heterogeneous CPU-FPGA Platforms
Authors: Choi Young-Kyu , Cong Jason , Fang Zhenman , Hao Yuchen , Reinman Glenn , Wei Peng
Published: February 2019

Conventional homogeneous multicore processors are not able to provide the continued performance and energy improvement that we have expected from past endeavors. Heterogeneous architectures that feature specialized hardware accelerators are widely considered a promising paradigm for resolving this issue. Among different heterogeneous devices, FPGAs that can be reconfigured to accelerate a broad class of applications with orders-of-magnitude performance/watt gains, are attracting increased attention from both academia and industry. As a consequence, a variety of CPU-FPGA acceleration platforms with diversified microarchitectural features have been supplied by industry vendors. Such diversity, however, poses a serious challenge to application developers in selecting the appropriate platform for a specific application or application domain.

This article aims to address this challenge by determining which microarchitectural characteristics affect performance, and in what ways. Specifically, we conduct a quantitative comparison and an in-depth analysis on five state-of-the-art CPU-FPGA acceleration platforms: (1) the Alpha Data board and (2) the Amazon F1 instance that represent the traditional PCIe-based platform with private device memory; (3) the IBM CAPI that represents the PCIe-based system with coherent shared memory; (4) the first generation of the Intel Xeon+FPGA Accelerator Platform that represents the QPI-based system with coherent shared memory; and (5) the second generation of the Intel Xeon+FPGA Accelerator Platform that represents a hybrid PCIe-based (non-coherent) and QPI-based (coherent) system with shared memory. Based on the analysis of their CPU-FPGA communication latency and bandwidth characteristics, we provide a series of insights for both application developers and platform designers. Furthermore, we conduct two case studies to demonstrate how these insights can be leveraged to optimize accelerator designs. The microbenchmarks used for evaluation have been released for public use.
Understanding the Dynamics of Social Interactions
Authors: Trabelsi Rim , Varadarajan Jagannadan , Zhang Le , Jabri Issam , Pei Yong , Smach Fethi , Bouallegue Ammar , Moulin Pierre
Published: February 2019

In this article, we deal with the problem of understanding human-to-human interactions as a fundamental component of social events analysis. Inspired by the recent success of multi-modal visual data in many recognition tasks, we propose a novel approach to model dyadic interaction by means of features extracted from synchronized 3D skeleton coordinates, depth, and Red Green Blue (RGB) sequences. From skeleton data, we extract new view-invariant proxemic features, named Unified Proxemic Descriptor (UProD), which is able to incorporate intrinsic and extrinsic distances between two interacting subjects. A novel key frame selection method is introduced to identify salient instants of the interaction sequence based on the joints’ energy. From Red Green Blue Depth (RGBD) videos, more holistic CNN features are extracted by applying an adaptive pre-trained Convolutional Neural Networks (CNNs) on optical flow frames. For better understanding the dynamics of interactions, we expand the boundaries of dyadic interactions analysis by proposing a fundamentally new modeling for non-treated problem aiming to discern the active from the passive interactor. Extensive experiments have been carried out on four multi-modal and multi-view interactions datasets. The experimental results demonstrate the superiority of our proposed techniques against the state-of-the-art approaches.
Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics
Authors: Deng Cheng , Li Zhao , Gao Xinbo , Tao Dacheng
Published: February 2019

As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, suggesting that extensive labor-consuming research and derivation is required. In this article, we propose a novel image tampering detection method based on deep multi-scale discriminative networks (MSD-Nets). The multi-scale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality (
QF
1) is higher than the second one (
QF
2). A special network in this module is designed to distinguish the small statistical difference between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area is located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with state-of-the-art approaches.
Solving Graph Problems via Potential Maximal Cliques
Authors: Korhonen Tuukka , Berg Jeremias , Järvisalo Matti
Published: February 2019

The BT algorithm of Bouchitté and Todinca based on enumerating potential maximal cliques, originally proposed for the treewidth and minimum fill-in problems, yields improved exact exponential-time algorithms for various graph optimization problems related to optimal triangulations. While the BT algorithm has received significant attention in terms of theoretical analysis, less attention has been paid on engineering efficient implementations of the algorithm for different problems and thereby on empirical studies on its effectiveness in practice. In this work, we provide an experimental evaluation of an implementation of the BT algorithm, based on our second-place winning entry in the 2nd Parameterized Algorithms and Computational Experiments Challenge (PACE 2017), extended to several related graph problems: treewidth, minimum fill-in, generalized and fractional hypertreewidth, and the total table size problem. Instead of focusing on problem-specific optimization of BT for a particular problem, our focus in this work is on studying the applicability of BT more generally to a range of problems. Based on the results, we conclude that an efficient implementation of the BT algorithm yields an empirically competitive approach to each of the considered problems when compared to available implementations of alternative problem-specific algorithmic approaches.
The Bionode
Authors: Pederson Daniel J. , Quinkert Christopher J. , Arafat Muhammad A. , Somann Jesse P. , Williams Jack D. , Bercich Rebecca A. , Wang Zhi , Albors Gabriel O. , Jefferys John G. R. , Irazoqui Pedro P.
Published: February 2019

Implantable closed-loop neuromodulation devices for use in long-term chronic studies in a lab or clinical trial are expensive to acquire and difficult to modify for specific use cases. This article documents the design and fabrication of a wireless implantable device using only commercially available off-the-shelf (COTS) components. This device, called the Bionode, can record and transmit up to four channels of biopotential data while simultaneously providing biphasic constant-current stimulation. The Bionode is a viable, low-cost, reusable, and easily modifiable research tool with clinical implications that has gained widespread use in various research projects at Purdue University.
The Complexity of Minimal Inference Problem for Conservative Constraint Languages
Authors: Wrona Michał
Published: February 2019

We study the complexity of the inference problem for propositional circumscription (the minimal inference problem) over arbitrary finite domains. The problem is of fundamental importance in nonmonotonic logics and commonsense reasoning. The complexity of the problem for the two-element domain has been completely classified. In this article, we classify the complexity of the problem over all conservative languages. We consider a version of the problem parameterized by a set of relations (a constraint language), from which we are allowed to build a knowledge base, and where a linear order used to compare models is a part of an input. We show that in this setting the problem is either Π

P

2
-complete, coNP-complete, or in P. The classification is based on a coNP-hardness proof for a new class of languages, an analysis of languages that do not express any member of the class, and a new general polynomial-time algorithm solving the minimal inference problem for a large class of languages.
Gadgets and Anti-Gadgets Leading to a Complexity Dichotomy
Authors: Cai Jin-Yi , Kowalczyk Michael , Williams Tyson
Published: February 2019

We introduce an idea called
anti-gadgets
for reductions in complexity theory. These anti-gadgets are presented as graph fragments, but their effect is equivalent to erasing the presence of other graph fragments, as if we had managed to include a negative copy of a certain graph gadget. We use this idea to prove a complexity dichotomy theorem for the partition function
Z
(
G
) of spin systems over 3-regular directed graphs
G
,
Z
(
G
) = ∑

σ :
V
(
G
) → { 0,1}

∏

(
u
,
v
) ∈
E
(
G
)

f
(σ (
u
), σ (
v
)), where each edge is given a (not necessarily symmetric) complex-valued binary function
f
: { 0,1}
2
→ C. We show that
Z
(
G
) is either computable in polynomial time or #P-hard, depending explicitly on
f
. When the input graph
G
is planar, there is an additional class of polynomial time computable partition functions
Z
(
G
), while everything else remains #P-hard. Furthermore, this additional class is precisely those that can be transformed by a holographic reduction to matchgates, followed by the Fisher-Kasteleyn-Temperley algorithm via Pfaffians.
Commuting planar polynomial vector fields for conservative newton systems
Authors: Nagloo Joel , Ovchinnikov Alexey , Thompson Peter
Published: February 2019

We study the problem of characterizing polynomial vector fields that commute with a given polynomial vector field on a plane. It is a classical result that one can write down solution formulas for an ODE that corresponds to a planar vector field that possesses a linearly independent (transversal) commuting vector field (see Theorem 2.1). In what follows, we will use the standard correspondence between (polynomial) vector fields and derivations on (polynomial) rings. Let [MATH HERE] be a derivation, where
f
is a polynomial with coefficients in a field
K
of zero characteristic. This derivation corresponds to the differential equation ẍ =
f
(
x
), which is called a conservative Newton system as it is the expression of Newton's second law for a particle confined to a line under the influence of a conservative force. Let
H
be the Hamiltonian polynomial for d with zero constant term, that is [MATH HERE]. Then the set of all polynomial derivations that commute with d forms a
K
[
H
]-module
M
d
[6, Corollary 7.1.5]. We show that, for every such
d,
the module
M
d
is of rank 1 if and only if deg
f
⩾ 2. For example, the classical elliptic equation

ẍ = 6x
2
+ a,

where
a
∈ C, falls into this category. For proofs of the results stated in this abstract, see [5].
Hashing to G2 on BLS pairing-friendly curves
Authors: Budroni Alessandro , Pintore Federico
Published: February 2019

When a pairing
e
: G
1
x G
2
→ G
T
, on an elliptic curve
E
defined over F
q
, is exploited in a cryptographic protocol, there is often the need to hash binary strings into G
1
and G
2
. Traditionally, if
E
admits a twist Ẽ of order
d,
then G
1
=
E
(F

q

)⋂
E
[
r
], where
r
is a prime integer, and G
2
= Ẽ(F

q
k/d

)⋂
Ẽ
[
r
], where
k
is the embedding degree of
E
w.r.t. r. The standard approach for hashing a binary string into G
1
and G
2
is to map it to general points
P∈E
(

F
q

) and
P′ ∈ Ẽ
(F

q
k/d

), and then multiply them by the cofactors
c
=
#E
(F

q

)/
r
and
c
′ =
#Ẽ
(F

q
k/d

)/
r
respectively. Usually, the multiplication by c′ is computationally expensive. In order to speed up such a computation, two different methods (by Scott
et al.
and by Fuentes
et al.
) have been proposed. In this poster we consider these two methods for BLS pairing-friendly curves having
k
∈ {12, 24, 30, 42,48}, providing efficiency comparisons. When
k
= 42,48, the Fuentes
et al.
method requires an expensive one-off pre-computation which was infeasible for the computational power at our disposal. In these cases, we theoretically obtain hashing maps that follow Fuentes
et al.
idea.
Truncated normal forms for solving polynomial systems
Authors: Telen Simon , Mourrain Bernard , Van Barel Marc
Published: February 2019

In this poster we present the results of [10]. We consider the problem of finding the common roots of a set of polynomial functions defining a zero-dimensional ideal
I
in a ring
R
of polynomials over C. We propose a general algebraic framework to find the solutions and to compute the structure of the quotient ring
R/I
from the cokernel of a resultant map. This leads to what we call Truncated Normal Forms (TNFs). Algorithms for generic dense and sparse systems follow from the classical resultant constructions. In the presented framework, the concept of a border basis is generalized by relaxing the conditions on the set of basis elements. This allows for algorithms to adapt the choice of basis in order to enhance the numerical stability. We present such an algorithm. The numerical experiments show that the methods allow to compute all zeros of challenging systems (high degree, with a large number of solutions) in small dimensions with high accuracy.
Techniques for natural-style proofs in elementary analysis
Authors: Jebelean Tudor
Published: February 2019

Combining methods from satisfiability checking with methods from symbolic computation promises to solve challenging problems in various areas of theory and application. We look at the basically equivalent problem of proving statements directly in a non-clausal setting, when additional information on the underlying domain is available in form of specific properties and algorithms. We demonstrate on a concrete example several heuristic techniques for the automation of natural-style proving of statements from elementary analysis. The purpose of this work in progress is to generate proofs similar to those produced by humans, by combining automated reasoning methods with techniques from computer algebra. Our techniques include: the S-decomposition method for formulae with alternating quantifiers, quantifier elimination by cylindrical algebraic decomposition, analysis of terms behaviour in zero, bounding the ∈-bounds, rewriting of expressions involving absolute value, algebraic manipulations, and identification of equal terms under unknown functions. These techniques are being implemented in the
Theorema
system and are able to construct automatically natural-style proofs for numerous examples including: convergence of sequences, limits and continuity of functions, uniform continuity, and other.
Systemic bile acids induce insulin resistance in a TGR5-independent manner
Authors: Syring Kristen E. , Cyphert Travis J. , Beck Thomas C. , Flynn Charles R. , Mignemi Nicholas A. , McGuinness Owen P.
Published: February 2019

Bile acids are involved in the emulsification and absorption of dietary fats, as well as acting as signaling molecules. Recently, bile acid signaling through farnesoid X receptor and G protein-coupled bile acid receptor (TGR5) has been reported to elicit changes in not only bile acid synthesis but also metabolic processes, including the alteration of gluconeogenic gene expression and energy expenditure. A role for bile acids in glucose metabolism is also supported by a correlation between changes in the metabolic state of patients (i.e., obesity or postbariatric surgery) and altered serum bile acid levels. However, despite evidence for a role for bile acids during metabolically challenging settings, the direct effect of elevated bile acids on insulin action in the absence of metabolic disease has yet to be investigated. The present study examines the impact of acutely elevated plasma bile acid levels on insulin sensitivity using hyperinsulinemic-euglycemic clamps. In wild-type mice, elevated bile acids impair hepatic insulin sensitivity by blunting the insulin suppression of hepatic glucose production. The impaired hepatic insulin sensitivity could not be attributed to TGR5 signaling, as TGR5 knockout mice exhibited a similar inhibition of insulin suppression of hepatic glucose production. Canonical insulin signaling pathways, such as hepatic PKB (or Akt) activation, were not perturbed in these animals. Interestingly, bile acid infusion directly into the portal vein did not result in an impairment in hepatic insulin sensitivity. Overall, the data indicate that acute increases in circulating bile acids in lean mice impair hepatic insulin sensitivity via an indirect mechanism.
Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks
Authors: Kanoosh Huthaifa M. , Houssein Essam Halim , Selim Mazen M.
Published: February 2019

Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.
Epigenetic Memory of Early-Life Parental Perturbation: Dopamine Decrease and DNA Methylation Changes in Offspring
Authors: Bordoni Laura , Nasuti Cinzia , Di Stefano Antonio , Marinelli Lisa , Gabbianelli Rosita
Published: February 2019

Early-life exposure (from postnatal day 6 to postnatal day 21) to permethrin has been associated with long-term development of dopaminergic neurodegeneration in rats. Here, we first investigated if the dopamine decrease observed following early postnatal exposure to permethrin, an oxidative stressor, can impair the dopamine level in the brain of their untreated offspring. Secondly, we evaluated whether this adverse event affects the epigenome of both directly exposed rats (F0) and their untreated offspring (F1). The results show that early-life exposure to the stressor is associated with changes in global DNA methylation and hydroxymethylation in adult age. Furthermore, parental exposure leads to a significant reduction in dopamine level in the offspring (F1) born from parents or just mothers early-life treated (72.72% and 47.35%, respectively). About 2/3 of pups from exposed mothers showed a significant reduction in dopamine level compared to controls. Global DNA methylation and hydroxymethylation impairment was associated with the F1 pups that showed reduced dopamine. This study provides pivotal evidences on intergenerational effects of postnatal exposure to permethrin emphasizing that this xenobiotic can influence the epigenetic memory of early-life parental perturbations disturbing offspring health.
Successful Management of Cryptococcal Meningitis and Bone Marrow Infiltration in a Young HIV/AIDS Patient: A Case Report and Review of the Literature
Authors: Patel Sonika , Jalbut Marla , Esham Kimberly S. , Stone David
Published: February 2019

Bone marrow cryptococcosis has been rarely reported in the literature, and there are no established treatment guidelines specific to this AIDS-related complication. The recommended treatment for AIDS-related invasive fungal treatments include amphotericin B and flucytosine which are associated with an array of complications making optimal treatment recommendations difficult. This case presentation represents an example of a patient with newly diagnosed AIDS and bone marrow cryptococcosis, which was successfully managed with an antifungal regimen adjusted to her comorbidities.
A Combination of UTMD-Mediated HIF-1αshRNA Transfection and TAE in the Treatment of Hepatic Cancer
Authors: Liao Yangying , Luo Haibo , He Zhizhong , Kuang Yongpei , Chen Peifen , Zhang Xiuzhen , Chen Junjun , Wen Qirong , Xie Yuhuan , Ding Shangwei
Published: February 2019

To explore the antitumor effect of hypoxia-inducible factor-1αshort hairpin RNA (HIF-1αshRNA) delivered by ultrasound targeted microbubble destruction (UTMD) and transcatheter arterial embolization (TAE) on rats with hepatic cancer. After the models of transplantation hepatoma were established, Wistar rats were randomly divided into 4 groups: Control group, UTMD group, TAE group, and UTMD+TAE group. Contrast-enhanced ultrasound (CEUS) was used to monitor tumor size on day 14 after four different treatments. Western blotting and immunohistochemistry were applied to measure the protein level of HIF-1αand VEGF in the hepatic cancer tissue. In comparison with UTMD+TAE group (21.25±10.68 days), the mean survival time was noticeably shorter in the Control group and TAE group (13.02±4.30 days and 15.03±7.32 days) (p<0.05, respectively). There was no statistical difference between UTMD+TAE group and UTMD group of the mean survival time (p>0.05). In addition, our results proved that the tumor sizes in UTMD+TAE group were obviously smaller than those in other groups (p<0.05, respectively). By CEUS, we clearly found that the tumor size was the smallest on day 14 in the UTMD+TAE group. The western blotting and immunohistochemistry results proved that the protein levels of HIF-1αand VEGF in UTMD+TAE group were obviously lower than those in TAE group and Control group on days 7 and 14 (p<0.05, respectively). However, there was no statistical difference between UTMD+TAE group and UTMD group (p>0.05). In this study we tried to explore the antitumor effect through a combination of UTMD-mediated HIF-1αshRNA transfection and TAE on rats with hepatic cancer. Our results showed that UTMD-mediated HIF-1αshRNA transfection and TAE can obviously silence HIF-1αand VEGF expression, thereby successfully inhibiting the growth of the tumor.