Miracle-dz/UniFAR-FTUs huggingface dataset:
QUESTION 20 : What methods can the Food and Drug Administration ( FDA ) and other regulatory bodies use to evaluate the efficacy of novel anti-infective technologies ?

RECOMMENDATION: The FDA and other regulatory bodies can use in vitro cell culture methods to evaluate the antimicrobial efficacy against pathogens, followed by animal studies to evaluate osseointegration issues and a subsequent osteomyelitis/periprosthetic joint infection (PJI) animal model to evaluate the in vivo efficacy. However, clinical trials may be required for clearance or approval of some novel anti-infective technologies.
Soren: Adaptive MapReduce for Programmable GPUs

In recent years the MapReduce programming model has been widely used for developing parallel data-intensive applications. As a result of its popularity, there exist many implementations of the MapReduce model on different parallel architectures including on massively parallel programmable GPUs. A basic challenge in implementing a MapReduce runtime system is the wide diversity of applications developed based on the model. That means a fixed implementation of the MapReduce runtime system may become suboptimal for some classes of applications. In this paper, we propose an adaptive framework for MapReduce on GPUs which is capable of monitoring key characteristics of applications and dynamically executing them efficiently in one of the three variations of the MapReduce engine it implements. Our preliminary results show that our adaptive method can significantly improve performance for many MapReduce applications (including a 11x performance speedup in one case) compared to a state-of-the-art MapReduce implementation on GPUs.
AGENT COMMUNICATION FOR ROLE PLAYING IN THE IDEA ASSOCIATION PROCESS

This paper proposes a framework for the specification of communication mechanisms that regulates interactions among agents participating in role playing in the idea association process. Since agents process a sort of human-like behavior, our approach is made taking as reference human communication characteristics through using role playing as metaphor. Therefore, we first analyze the characteristics of human communication, deriving role playing for linking ideas in the human world. By integrating with ACL mechanisms, we propose a framework to express communication mechanisms for exchanging message in a multi-agent framework called DIM-2. Finally, the framework is evaluated through an experiment. Also its computational feasibility of a support system for the distributed interactions is discussed in this paper.
Morphologic changes of dendritic spines of striatal neurons in the levodopa‐induced dyskinesia model

Maladaptive plasticity at corticostriatal synapses plays an important role in the development of levodopa‐induced dyskinesia. Recently, it has been shown that synaptic plasticity is closely linked to morphologic changes of dendritic spines. To evaluate morphologic changes of dendritic spines of two types of striatal medium spiny neurons, which project to the internal segment of globus pallidus or the external segment of globus pallidus, in the levodopa‐induced dyskinesia model, we used 6‐hydroxydopamine‐lesioned rats chronically treated with levodopa. Dendritic spines were decreased and became enlarged in the direct pathway neurons of the model of levodopa‐induced dyskinesia. The same levodopa treatment to normal rats, in which no dyskinesia was observed, also induced enlargement of dendritic spines, but not a decrease in density of spines in the direct pathway neurons. These results suggest that a loss and enlargement of dendritic spines in the direct pathway neurons plays important roles in the development of levodopa‐induced dyskinesia. © 2014 International Parkinson and Movement Disorder Society
A RAG1 Mutation Found in Omenn Syndrome Causes Coding Flank Hypersensitivity: A Novel Mechanism for Antigen Receptor Repertoire Restriction1

Hypomorphic RAG mutants with severely reduced V(D)J recombination activity cause Omenn Syndrome (OS), an immunodeficiency with features of immune dysregulation and a restricted TCR repertoire. Precisely how RAG mutants produce autoimmune and allergic symptoms has been unclear. Current models posit that the severe recombination defect restricts the number of lymphocyte clones, a few of which are selected upon Ag exposure. We show that murine RAG1 R972Q, corresponding to an OS mutation, renders the recombinase hypersensitive to selected coding sequences at the hairpin formation step. Other RAG1 OS mutants tested do not manifest this sequence sensitivity. These new data support a novel mechanism for OS: by selectively impairing recombination at certain coding flanks, a RAG mutant can cause primary repertoire restriction, as opposed to a more random, limited repertoire that develops secondary to severely diminished recombination activity.
Panic disorder in university students: a review.

Abstract Panic disorder is characterized by recurrent, sudden, often unprovoked attacks of intense, diffuse autonomic discharge accompanied by severe anxiety as distinguished from ordinary generalized anxiety. Recent research indicates a biochemical abnormality that predisposes some individuals to the disorder, which generally begins in young adulthood and affects women twice as frequently as men. Untreated panic disorder follows a chronic, fluctuating course. Complications include hypochondriasis, anticipatory anxiety, phobic avoidance and agoraphobia, abuse of alcohol and sedative drugs, and depression. Diagnosis is complicated by patients' complaints of physical rather than emotional symptoms. Workups can be avoided if the physician considers the diagnosis in apparently healthy young adults who present with episodic cardiac, gastrointestinal, neurologic, and/or respiratory symptoms. The physician needs to be able to distinguish the symptoms of panic disorder from those of ordinary anxiety and also to r.
Stress Emotion Evaluation in Multiplayer Online Battle Arena (MOBA) Video Game Related to Gaming Rules Using Electroencephalogram (EEG)

One of the functions of playing video games is place for entertainment, but there are also people become stress while playing video games because of frustration (fail to achieve objective in video games). One of video games genre that nowadays is popular is Multi Player Online Battle Arena (MOBA). This MOBA games have high complexity and more challenging for player, so this genre is suitable for this research to see what in game events that makes player's stress. This research objective is to evaluate stress when playing video games using cognitive ergonomic approach to see what in-game characteristic that increase stress. Methods for this research are frequency analysis from average power, asymmetry, and alpha/beta ratio also theta/beta ratio. Frequency Analysis is conducted by using electroencephalogram data from respondent while playing DOTA 2. Based on result of this research, there are some conditions that affect and did not affect to player's stress to both experienced and first time player.
Integrated Access and Backhaul in 5G mmWave Networks: Potentials and Challenges

Integrated Access and Backhaul (IAB) is being investigated as a means to overcome deployment costs of ultra-dense 5G millimeter wave (mmWave) networks by realizing wireless backhaul links to relay the access traffic. For the development of these systems, however, it is fundamental to validate the performance of IAB in realistic scenarios through end-to-end system level simulations. In this paper, we shed light on the most recent standardization activities on IAB, and compare architectures with and without IAB in mmWave deployments. While it is well understood that IAB networks reduce deployment costs by obviating the need to provide wired backhaul to each cellular base-station, in this paper we demonstrate the cell-edge throughput advantage offered by IAB using end-to-end system level simulations. We further highlight some research challenges associated with this architecture that will require further investigations.
Automated detection and extraction of skull from MR head images: Preliminary results

Skull extraction from Magnetic Resonance (MR) head image datasets is the process of segmentation of brain tissues from other tissues (e.g., skin, bone, fat) and has an important role in computer-assisted operations. Because, the accuracy of skull extraction affects the next stages of digital image processing or analysis. Semi-/full automated detection and extraction of the skull from MR datasets is a difficult task due to inhomogeneous intensity values, overlapping of non-brain and brain tissues, noise, unclear edges, existence of lesions or tumors. Also, skull size, shape and thickness may vary in each person. Therefore, gray level based or shape based skull detection and extraction algorithms can not always yield in desired results. To overcome these difficulties, in this paper, a hybrid algorithm is proposed. In this approach, anatomy knowledge, binary morphological operations and Gaussian model based classification is used. Experimental results show efficiency of the proposed approach.
Benchmark of Polygon Quality Metrics for Polytopal Element Methods

Polytopal Element Methods (PEM) allow to solve differential equations on general polygonal and polyhedral grids, potentially offering great flexibility to mesh generation algorithms. Differently from classical finite element methods, where the relation between the geometric properties of the mesh and the performances of the solver are well known, the characterization of a good polytopal element is still subject to ongoing research. Current shape regularity criteria are quite restrictive, and greatly limit the set of valid meshes. Nevertheless, numerical experiments revealed that PEM solvers can perform well on meshes that are far outside the strict boundaries imposed by the current theory, suggesting that the real capabilities of these methods are much higher. In this work, we propose a benchmark to study the correlation between general 2D polygonal meshes and PEM solvers. The benchmark aims to explore the space of 2D polygonal meshes and polygonal quality metrics, in order to identify weaker shape-regularity criteria under which the considered methods can reliably work. The proposed tool is quite general, and can be potentially used to study any PEM solver. Besides discussing the basics of the benchmark, in the second part of the paper we demonstrate its application on a representative member of the PEM family, namely the Virtual Element Method, also discussing our findings.
XMM-Newton observations of PSR J0726−2612, a radio-loud XDINS

We present the results of an XMM-Newton observation of the slowly rotating (P = 3.4 s), highly magnetized (B ≈ 3 × 1013 G) radio pulsar PSR J0726–2612. A previous X-ray observation with the Chandra satellite showed that some of the properties of PSR J0726–2612 are similar to those of the X-ray-dim isolated neutron stars (XDINSs), a small class of nearby slow pulsars characterized by purely thermal X-ray spectra and undetected in the radio band. We confirm the thermal nature of the X-ray emission of PSR J0726–2612, which can be fitted by the sum of two blackbodies with temperatures kT1=0.074+0.006-0.011 keV and kT2=0.14+0.004-0.002 keV and emitting radii R1=10.4+10.8-2.8 km and R2=0.5+0.9-0.3 km, respectively (assuming a distance of 1 kpc). A broad absorption line modeled with a Gaussian profile centered at 0.39+0.02-0.03 keV is required in the fit. The pulse profile of PSR J0726–2612 is characterized by two peaks with similar intensity separated by two unequal minima, a shape and pulsed fraction that cannot be reproduced without invoking magnetic beaming of the X-ray emission. The presence of a single radio pulse suggests that in PSR J0726–2612 the angles that the dipole axis and the line of sight make with the rotation axis, ξ and χ, respectively, are similar. This geometry differs from that of the two radio-silent XDINSs with double-peaked pulse profiles similar to that of PSR J0726–2612, for which ξ ∼ 90° and χ ∼ 45° have recently been estimated. These results strengthen the similarity between PSR J0726–2612 and the XDINSs and support the possibility that the lack of radio emission from the latter might simply be due to an unfavorable viewing geometry.
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains

Neural language modeling (LM) has led to significant improvements in several applications, including Automatic Speech Recognition. However, they typically require large amounts of training data, which is not available for many domains and languages. In this study, we propose a multilingual neural language model architecture, trained jointly on the domain-specific data of several low-resource languages. The proposed multilingual LM consists of language specific word embeddings in the encoder and decoder, and one language specific LSTM layer, plus two LSTM layers with shared parameters across the languages. This multilingual LM model facilitates transfer learning across the languages, acting as an extra regularizer in very low-resource scenarios. We integrate our proposed multilingual approach with a state-of-the-art highly-regularized neural LM, and evaluate on the conversational data domain for four languages over a range of training data sizes. Compared to monolingual LMs, the results show significant improvements of our proposed multilingual LM when the amount of available training data is limited, indicating the advantages of cross-lingual parameter sharing in very low-resource language modeling.
Transverse vibration of fluid conveying carbon nanotubes embedded in two-parameter elastic medium

In this study, based on continuum mechanics, an Eul er–Bernoulli model has been applied to analyze the free transverse vibration of fluid conveyi ng Single-Walled Carbon Nanotubes (SWCNTs), embedded in two-parameter elastic mediums such as; the Pasternak and viscoelastic mediums. By exerting the transfer matrix me thod (TMM), the vibration analysis of a fluid conveying SWCNT is developed. The effects of fluid flow velocity, Winkler, visco and Pasternak-elastic mediums are studied on the resonance fr equencies. The results indicate that, the resonant frequencies at which the structural instabilit y of nanotubes emerge are significantly influenced by their surrounding elastic medium. Each ela stic medium is found to have special characteristics, resulting in different frequency behavio rs for each SWCNT. Finally, the effects of geometry are studied on the SWCNT's resonance frequ encies. Interesting results are obtained, which are helpful in the modeling or designing proc ess of a carbon nanotube.
Visual Diagnosis of Dermatological Disorders: Human and Machine Performance

Skin conditions are a global health concern, ranking the fourth highest cause of nonfatal disease burden when measured as years lost due to disability. As diagnosing, or classifying, skin diseases can help determine effective treatment, dermatologists have extensively researched how to diagnose conditions from a patient's history and the lesion's visual appearance. Computer vision researchers are attempting to encode this diagnostic ability into machines, and several recent studies report machine level performance comparable with dermatologists. This report reviews machine approaches to classify skin images and consider their performance when compared to human dermatologists. Following an overview of common image modalities, dermatologists' diagnostic approaches and common tasks, and publicly available datasets, we discuss approaches to machine skin lesion classification. We then review works that directly compare human and machine performance. Finally, this report addresses the limitations and sources of errors in image-based skin disease diagnosis, applicable to both machines and dermatologists in a teledermatology setting.
Using your ADC and Backend Capacitors to Authenticate for Free: (Virtually) Free from Database, Enrollment, and Excessive Area/Power Overhead

Detection of counterfeit chips has emerged as a crucial concern. Physically-unclonable-function (PUF)-based techniques are widely used for authentication, however, require dedicated hardware and large signature database. In this work, we show intrinsic & database-free authentication using back-end capacitors. The discussed technique simplifies authentication setup and reduces the test cost. We show that an analog-to-digital converter (ADC) can be modified for back-end capacitor-based authentication in addition to its regular functionality; hence, a dedicated authentication module is not necessary. Moreover, since back-end capacitors are quite insensitive to temperature and aging-induced variations than transistors, the discussed technique result in a more reliable authentication than transistor PUF-based authentication. The modifications to conventional ADC incur 3.2% power overhead and 75% active-area overhead; however, arguably, the advantages of the discussed intrinsic & database-free authentication outweigh the overheads. Full version of this article is published at IEEE TVLSI.
RL4health: Crowdsourcing Reinforcement Learning for Knee Replacement Pathway Optimization

Joint replacement is the most common inpatient surgical treatment in the US. We investigate the clinical pathway optimization for knee replacement, which is a sequential decision process from onset to recovery. Based on episodic claims from previous cases, we view the pathway optimization as an intelligence crowdsourcing problem and learn the optimal decision policy from data by imitating the best expert at every intermediate state. We develop a reinforcement learning-based pipeline that uses value iteration, state compression and aggregation learning, kernel representation and cross validation to predict the best treatment policy. It also provides forecast of the clinical pathway under the optimized policy. Empirical validation shows that the optimized policy reduces the overall cost by 7 percent and reduces the excessive cost premium by 33 percent.
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning

Simultaneous translation is widely useful but remains one of the most difficult tasks in NLP. Previous work either uses fixed-latency policies, or train a complicated two-staged model using reinforcement learning. We propose a much simpler single model that adds a “delay” token to the target vocabulary, and design a restricted dynamic oracle to greatly simplify training. Experiments on Chinese <-> English simultaneous translation show that our work leads to flexible policies that achieve better BLEU scores and lower latencies compared to both fixed and RL-learned policies.
Improving Long Distance Slot Carryover in Spoken Dialogue Systems

Tracking the state of the conversation is a central component in task-oriented spoken dialogue systems. One such approach for tracking the dialogue state is slot carryover, where a model makes a binary decision if a slot from the context is relevant to the current turn. Previous work on the slot carryover task used models that made independent decisions for each slot. A close analysis of the results show that this approach results in poor performance over longer context dialogues. In this paper, we propose to jointly model the slots. We propose two neural network architectures, one based on pointer networks that incorporate slot ordering information, and the other based on transformer networks that uses self attention mechanism to model the slot interdependencies. Our experiments on an internal dialogue benchmark dataset and on the public DSTC2 dataset demonstrate that our proposed models are able to resolve longer distance slot references and are able to achieve competitive performance.
Hamiltonian Neural Networks

Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models that learn and respect exact conservation laws in an unsupervised manner. We evaluate our models on problems where conservation of energy is important, including the two-body problem and pixel observations of a pendulum. Our model trains faster and generalizes better than a regular neural network. An interesting side effect is that our model is perfectly reversible in time.
Dilated Convolution with Dilated GRU for Music Source Separation

Stacked dilated convolutions used in Wavenet have been shown effective for generating high-quality audios. By replacing pooling/striding with dilation in convolution layers, they can preserve high-resolution information and still reach distant locations. Producing high-resolution predictions is also crucial in music source separation, whose goal is to separate different sound sources while maintain the quality of the separated sounds. Therefore, in this paper, we use stacked dilated convolutions as the backbone for music source separation. Although stacked dilated convolutions can reach wider context than standard convolutions do, their effective receptive fields are still fixed and might not be wide enough for complex music audio signals. To reach even further information at remote locations, we propose to combine a dilated convolution with a modified GRU called Dilated GRU to form a block. A Dilated GRU receives information from k-step before instead of the previous step for a fixed k. This modification allows a GRU unit to reach a location with fewer recurrent steps and run faster because it can execute in parallel partially. We show that the proposed model with a stack of such blocks performs equally well or better than the state-of-the-art for separating both vocals and accompaniment.
Embryonic development of the enteric nervous system of the grasshopper Schistocerca americana

The enteric nervous system (ENS) of the grasshopper Schistocerca americana is organized into four ganglia located in the foregut (the dorsal unpaired frontal and hypocerebral ganglia, and the paired ingluvial ganglia), and two plexuses that innervate the foregut and midgut. A dorsomedial recurrent nerve and two lateral esophageal nerves connect the ganglia. The midgut plexus is arranged in four nerves running along the midgut surface. In this study, we have focused on the embryonic development of the grasshopper ENS; we have studied the proliferation pattern, morphogenesis, and some aspects of neuronal differentiation by using a number of specific molecular markers. The grasshopper ENS develops early in embryogenesis (25–30%) from three neurogenic zones (NZs) located on the roof of the stomodeum. These NZs slightly invaginate from an epithelial placode. The expression pattern of specific cell surface proteins and the analysis of the mitotic activity showed that NZs cells delaminate from the epithelium, become neuronal precursors, divide symmetrically, and then actively migrate to their final position in the enteric ganglia or plexuses. The grasshopper enteric ganglia are composed of mixed populations of cells from different NZs. The foregut and midgut plexuses are formed by the dispersal of cells from the developing hypocerebral and ingluvial ganglia. The main ENS nerves are pioneered by axons extending anteriorly from hypocerebral and ingluvial neurons. The insect ENS exhibits an enormous variation in design. Several features of the grasshopper program of neurogenesis and pattern of cell migration are compared to other insects, and some evolutionary implications are discussed. © 1996 Wiley‐Liss, Inc.