Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal conductivity measurements with the 3ω method and scanning thermal microscopy

Various techniques allow measuring the thermal conductivity of materials ranging from bulk to thin-film sizes. Among them, one can find the 3ω method, scanning thermal microscopy or Raman thermometry. The standard 3ω method is not spatially-resolved since it requires a long deposited metallic wire. It is also intrusive. In contrast, the two other techniques have spatial resolution ranging from few microns to submicronic one, depending on the conditions of operation. However, their sensitivity is altered by various parameters. For instance, scanning thermal microscopy signals depend strongly on the surface state. Here, by gathering results obtained with these techniques, we report on the comparison of the experimental determination of the thermal conductivity of various silicon-based and thermoelectric materials of strong interest for microelectronics. In particular, we highlight the evolution of the thermal conductivity with the temperature, which is customarily determined by the 3ω method. While it requires lithography, which can be considered as a drawback at first, the 3ω method is easier to use when varying the temperature. We conclude on the advantages and drawbacks of these techniques and provide a matrix of choices depending on the materials and conditions.
Perception of Stigma and Its Associated Factors Among Patients With Major Depressive Disorder: A Multicenter Survey From an Asian Population

Stigma of major depressive disorder (MDD) is an important public health problem. This study aimed to examine the level of perceived stigma and its associated factors in MDD patients in five Asian countries, including China, Korea, Malaysia, Singapore, and Thailand. A total of 547 outpatients with MDD were included from Asian countries. We used the stigma scale of the Explanatory Model Interview Catalogue (EMIC) to assess stigma. The Montgomery–Asberg Depression Rating Scale (MADRS), Symptoms Checklist 90-Revised (SCL-90-R), Fatigue Severity Scale (FSS), Sheehan Disability Scale (SDS), 36-Item Short-Form Health Survey (SF-36), and Multidimensional Scale of Perceived Social Support (MSPSS) were used to assess symptoms, clinical features, functional impairment, health status, and social support. The stigma scores of patients under 55 years old were significantly higher than those equal to or greater than 55 years old (P < 0.001). The stigma scores exhibited significant negative correlation with age; MSPSS scores of family, friends, and others; and SF-36 subscale of mental health, but significant positive correlation with MADRS, FSS, SDS, and SCL-90-R subscale scores of depression, interpersonal sensitivity, obsession–compulsion, psychoticism, and somatization. Multivariate regression analysis revealed that age, SCL-90-R interpersonal sensitivity, obsession–compulsion, psychoticism, MSPSS scores of friends and others, and SF-36 of mental health were significantly associated with the level of perceived stigma. These findings suggest that MDD patients who are young, have a high degree of interpersonal sensitivity and psychoticism, have low health-related quality of life, and have low social support are the target population for stigma interventions in Asia.
Functional Supramolecular Gels Based on the Hierarchical Assembly of Porphyrins and Phthalocyanines

Supramolecular gels containing porphyrins and phthalocyanines motifs are attracting increased interests in a wide range of research areas. Based on the supramolecular gels systems, porphyrin or phthalocyanines can form assemblies with plentiful nanostructures, dynamic, and stimuli-responsive properties. And these π-conjugated molecular building blocks also afford supramolecular gels with many new features, depending on their photochemical and electrochemical characteristics. As one of the most characteristic models, the supramolecular chirality of these soft matters was investigated. Notably, the application of supramolecular gels containing porphyrins and phthalocyanines has been developed in the field of catalysis, molecular sensing, biological imaging, drug delivery and photodynamic therapy. And some photoelectric devices were also fabricated depending on the gelation of porphyrins or phthalocyanines. This paper presents an overview of the progress achieved in this issue along with some perspectives for further advances.
Can You Give Me a Reason?: Argument-inducing Online Forum by Argument Mining

This demonstration paper presents an argument-inducing online forum that stimulates participants with lack of premises for their claim in online discussions. The proposed forum provides its participants the following two subsystems: (1) Argument estimator for online discussions automatically generates a visualization of the argument structures in posts based on argument mining. The forum indicates structures such as claim-premise relations in real time by exploiting a state-of-the-art deep learning model. (2) Argument-inducing agent for online discussion (AIAD) automatically generates a reply post based on the argument estimator requesting further reasons to improve the argumentation of participants. Our experimental discussion demonstrates that the argument estimator can detect the argument structures from online discussions, and AIAD can induce premises from the participants. To the best of our knowledge, our argument-inducing online forum is the first approach to either visualize or request a real-time argument for online discussions. Our forum can be used to collect and induce claim-reasons pairs rather than only opinions to understand various lines of reasoning in online arguments such as civic discussions, online debates, and education objectives. The argument estimator code is available at https://github.com/EdoFrank/EMNLP2018-ArgMining-Morio and the demonstration video is available at https://youtu.be/T9fNJfneQV8.
Synthesizing Cluster Management Code for Distributed Systems

Management planes for data-center systems are complicated to develop, test, maintain, and evolve. They routinely grapple with hard combinatorial optimization problems like load balancing, placement, scheduling, rolling upgrades and configuration management. To tackle these problems, developers are left with two bad choices: (i) develop ad-hoc mechanisms for systems to solve these optimization problems, or (ii) use specialized solvers that require steep engineering effort. We propose Weave, a tool that enables programmers to specify cluster management policies in a high-level declarative language, and compute policy-compliant configurations automatically and efficiently. Weave allows constraints and policies, the essence of a management plane, to be easily added, removed and modified over time, using a language familiar to developers (SQL). In this paper, we discuss our approach of management plane synthesis, its benefits, and present preliminary results from implementing a Kubernetes scheduler and a CorfuDB management plane using Weave.
Combination of Facial Recognition and Interaction with Academic Portal in Automatic Attendance System

The computer vision has been advancing rapidly recently and it has become necessary to use the technology in the education area. As a part of it, the application of facial recognition technology plays an important role in building the intelligent education system, where teaching and learning can be measured to support quality improvement. The required-attendance taking activity at the schools is one of the important parts of the teaching process. The traditional attendance method causes several inconveniences such as wasting time and effort, easy to mistake and difficult to verify. In this paper, we introduce an automatic attendance system based on the combination of facial recognition technology and interaction with the existing academic portal. We conducted a review of several modern methods to select the most suitable open framework for individual tasks. Our proposed design is flexible and be able to apply to the large-scale set of students without compromising predictive accuracy. We have tested the system on more than 2200 freshmen at FPT University in Hanoi, Vietnam. Initial numerical results show the effectiveness in both aspects of the accuracy and performance of the prototype.
Hypermetabolism in ALS is associated with greater functional decline and shorter survival

Objective To determine the prevalence of hypermetabolism, relative to body composition, in amyotrophic lateral sclerosis (ALS) and its relationship with clinical features of disease and survival. Methods Fifty-eight patients with clinically definite or probable ALS as defined by El Escorial criteria, and 58 age and sex-matched control participants underwent assessment of energy expenditure. Our primary outcome was the prevalence of hypermetabolism in cases and controls. Longitudinal changes in clinical parameters between hypermetabolic and normometabolic patients with ALS were determined for up to 12 months following metabolic assessment. Survival was monitored over a 30-month period following metabolic assessment. Results Hypermetabolism was more prevalent in patients with ALS than controls (41% vs 12%, adjusted OR=5.4; p<0.01). Change in body weight, body mass index and fat mass (%) was similar between normometabolic and hypermetabolic patients with ALS. Mean lower motor neuron score (SD) was greater in hypermetabolic patients when compared with normometabolic patients (4 (0.3) vs 3 (0.7); p=0.04). In the 12 months following metabolic assessment, there was a greater change in Revised ALS Functional Rating Scale score in hypermetabolic patients when compared with normometabolic patients (−0.68 points/month vs −0.39 points/month; p=0.01). Hypermetabolism was inversely associated with survival. Overall, hypermetabolism increased the risk of death during follow-up to 220% (HR 3.2, 95% CI 1.1 to 9.4, p=0.03). Conclusions and relevance Hypermetabolic patients with ALS have a greater level of lower motor neuron involvement, faster rate of functional decline and shorter survival. The metabolic index could be important for informing prognosis in ALS.
The Endosomal Sorting Complex Required for Transport Pathway Mediates Chemokine Receptor CXCR4-promoted Lysosomal Degradation of the Mammalian Target of Rapamycin Antagonist DEPTOR*

Background: Akt signaling is activated by G protein-coupled receptors (GPCRs) via the PI3K-mTORC2 signaling cascade, but mechanistic insight is lacking. Results: GPCR signaling promotes lysosomal and ESCRT-dependent degradation of DEPTOR, an antagonist of mTORC2, that regulates Akt activation and signaling. Conclusion: ESCRTs regulate signaling by promoting degradation of a cytosolic antagonist of signaling. Significance: This study begins to identify the signaling pathways governing the physiological roles of ESCRTs and Akt. G protein-coupled receptor (GPCR) signaling mediates many cellular functions, including cell survival, proliferation, and cell motility. Many of these processes are mediated by GPCR-promoted activation of Akt signaling by mammalian target of rapamycin complex 2 (mTORC2) and the phosphatidylinositol 3-kinase (PI3K)/phosphoinositide-dependent kinase 1 (PDK1) pathway. However, the molecular mechanisms by which GPCRs govern Akt activation by these kinases remain poorly understood. Here, we show that the endosomal sorting complex required for transport (ESCRT) pathway mediates Akt signaling promoted by the chemokine receptor CXCR4. Pharmacological inhibition of heterotrimeric G protein Gαi or PI3K signaling and siRNA targeting ESCRTs blocks CXCR4-promoted degradation of DEPTOR, an endogenous antagonist of mTORC2 activity. Depletion of ESCRTs by siRNA leads to increased levels of DEPTOR and attenuated CXCR4-promoted Akt activation and signaling, consistent with decreased mTORC2 activity. In addition, ESCRTs likely have a broad role in Akt signaling because ESCRT depletion also attenuates receptor tyrosine kinase-promoted Akt activation and signaling. Our data reveal a novel role for the ESCRT pathway in promoting intracellular signaling, which may begin to identify the signal transduction pathways that are important in the physiological roles of ESCRTs and Akt.
Rapid 3-Dimensional Anthropometry by Semiautomatic Data Acquisition and Processing

A semiautomatic anthropometric caliper was developed to accelerate and simplify anthropometric data acquisition. A technical slide caliper, connected via optical data cable interface to a standard PC, was amended with a data-input switch. A data sheet was filled in with consecutive measurements in 30 volunteers and compared with 30 fully manually performed and transmitted examinations on the identical volunteer population. To examine the reliability of the semiautomatic measurements, repeat measurements were performed interindividually and intraindividually in 10 volunteers. The time required for a cephalometric measurement routine was reduced from 16 ± 3 minutes to 6 ± 2 minutes. The intraindividual standard/relative error was reduced from 2.1 ± 1.9% to 1.4 ± 1.7%, interindividually from 3.2 ± 2.1% to 2.2 ± 0.9%. The presented semiautomatic anthropometric caliper promises to be faster and reliable for anthropometric measurements of craniofacial conformation and will prospectively become applied for growth and treatment control. The relative measurement error seems to be smaller than in full-manual assessment; oversight error in manual recording can moreover be excluded effectively.
Exploring Video Game Searches on the Web

As video games are developing fast, many users issue queries related to video games in a daily fashion. While there were a few attempts to understand their behavior, little is known on how the video game-related searches are done. Digesting and analyzing this search behavior may thus be faced as an important contribution for search engines to provide better results and search services for their users. To overcome this lack of knowledge and to gain more insight into how video game searches are done, we analyze in this paper, a number of game search queries submitted to a general search engine named Parsijoo. The analysis conducted was performed on top of 372,508 game search records extracted from the query logs within 253,516 different search sessions. Different aspects of video game searches are studied, including, their temporal distribution, game version specification, popular game categories, popular game platforms, game search sessions and clicked pages. Overall, the experimental analysis on video game searches shows that the current retrieval methods used by traditional search engines cannot be applied for game searches, thus, different retrieval and search services should be considered for these searches in the future.
Estimating genetic kin relationships in prehistoric populations

Archaeogenomic research has proven to be a valuable tool to trace migrations of historic and prehistoric individuals and groups, whereas relationships within a group or burial site have not been investigated to a large extent. Knowing the genetic kinship of historic and prehistoric individuals would give important insights into social structures of ancient and historic cultures. Most archaeogenetic research concerning kinship has been restricted to uniparental markers, while studies using genome-wide information were mainly focused on comparisons between populations. Applications which infer the degree of relationship based on modern-day DNA information typically require diploid genotype data. Low concentration of endogenous DNA, fragmentation and other post-mortem damage to ancient DNA (aDNA) makes the application of such tools unfeasible for most archaeological samples. To infer family relationships for degraded samples, we developed the software READ (Relationship Estimation from Ancient DNA). We show that our heuristic approach can successfully infer up to second degree relationships with as little as 0.1x shotgun coverage per genome for pairs of individuals. We uncover previously unknown relationships among prehistoric individuals by applying READ to published aDNA data from several human remains excavated from different cultural contexts. In particular, we find a group of five closely related males from the same Corded Ware culture site in modern-day Germany, suggesting patrilocality, which highlights the possibility to uncover social structures of ancient populations by applying READ to genome-wide aDNA data.
Information Diffusion Prediction with Network Regularized Role-based User Representation Learning

In this article, we aim at developing a user representation learning model to solve the information diffusion prediction problem in social media. The main idea is to project the diffusion users into a continuous latent space as the role-based (sender and receiver) representations, which capture unique diffusion characteristics of users. The model learns the role-based representations based on a cascade modeling objective that aims at maximizing the likelihood of observed cascades, and employs the matrix factorization objective of reconstructing structural proximities as a regularization on representations. By jointly embedding the information of cascades and network, the learned representations are robust on different diffusion data. We evaluate the proposed model on three real-world datasets. The experimental results demonstrate the better performance of the proposed model than state-of-the-art diffusion embedding and network embedding models and other popular graph-based methods.
Evaluation of variable bit-width units in a RISC-V processor for approximate computing

Among various power reduction methods, variable bit-width arithmetic units have been proposed in approximate computing literature. In this paper, we add a variable bit-width memory unit in a RISC-V processor. Integrating both computation and memory units with variable bit-width leads to a power reduction: from 7% to 29% for Sobel filter application and from 13% to 24% for an application that computes the position of a robotic arm (forwardk2j). We also propose a global energy model for a RISC-V processor with variable bit-width units (for computation and memory). This model allows us to evaluate the impact of various parameters in both the software application (e.g., the amount of instructions that can be executed with a reduced bit-width) and the hardware architecture (e.g., impact of potential reduction for each unit).
BookCeption: A Proposed Framework for an Artificially Intelligent Recommendation Platform

BookCeption is a web-based book recommendation system that allows readers to browse and buy books across multiple platforms at the most affordable price. It fetches data from E-commerce sites like Barnes and Nobles, Amazon, eBay along with Facebook pages and retail bookstore websites. It is a unique book recommender that uses Machine Learning techniques to recommend books as well as offers from other platforms to the registered users. This paper introduces the architecture of the proposed framework, which integrates the book recommendation system with a platform for buying books. For the book recommender discussed here, four different recommendation techniques were used- SVD, Co-clustering, NMF and Deep Learning to determine the best one for the framework. In terms of computational training time, Co-clustering works best with a time of 5.21 minutes and RMSE value of 0.868. However, on the basis of RMSE value, Deep Learning Embedding Model works best with RMSE value of 0.7599 in 8.06 minutes when the number of epochs and the batch size are 20 and 200 respectively.
Self-Quotient Image based CNN: A Basic Image Processing assisting Convolutional Neural Network

The Convolutional Neural Networks (CNNs) are able to learn basic and high level features hierarchically with the highlight that it implements an end-to-end learning method. However, lacking in the ability to utilize prior information and domain knowledge has led to the neural networks hard to train. In this paper, a method using prior information is proposed, which is by appending prior feature-maps through a bypass input structure. As an implementation, we evaluate a convolutional neural network integrating with the Self-Quotient Image (SQI) algorithm. Through the bypass, we import the feature-maps from the SQI algorithm and concat them with the output of the first convolution layer. With the help of traditional image processing methods, CNNs can directly improve the accuracy and training stability, while the bypass is exactly a consistent point. Finally, the necessity of this bypass pattern is that it avoids the direct modification of original images. As CNNs are able to focus on far richer features than basic image processing methods, it is advisable for us to expose CNNs to the original data. It is exactly the main design idea that we make the output from synergistic processing algorithm bypass from the side.
Korean Song-lyrics Generation by Deep Learning

In order to create a lyrics based on the characteristics of Korean, we reversed the K-pop lyrics data and use them as learning data. It transforms the incoming data to use certain elements of the sentence, such as predicates and conjunctions, as starting points of the string generation. The proposed song lyrics generation method considers the context between lyrics. Every time the model generates the lyric, the model goes through upper randomization based on a blank. It was confirmed that the lyrics generated using the reverse data, have a more natural context than the lyrics generated using the forward data. It is also possible to generate new lyrics similar to certain lyric structure.
The Functional Network Processing Acute Electrical Itch Stimuli in Humans

The posterior insula (pIns) is a major brain region that receives itch-related signals from the periphery and transfers these signals to broad areas in the brain. Previous brain imaging studies have successfully identified brain regions that respond to itch stimuli. However, it is still unknown which brain regions receive and process itch-related signals from the pIns. Addressing this question is important in identifying key functional networks that process itch. Thus, the present study investigated brain regions with significantly increased functional connectivity with the pIns during itch stimuli with 25 healthy subjects by using functional MRI. Electrical itch stimuli was applied to the left wrist. Similar to previous brain imaging studies, many cortical and subcortical areas were activated by itch stimuli. However, not all of these regions showed significant increments of functional connectivity with the pIns during itch stimuli. While the subjects perceived the itch sensation, functional connectivity was significantly increased between the right pIns and the supplementary motor area (SMA), pre-SMA, anterior midcingulate cortex (aMCC), anterior insula (aIns), secondary somatosensory cortex (SII), and basal ganglia (BG), suggesting that this is a key network in processing itch. In particular, intensity of functional connectivity between the pIns and BG was negatively correlated with itch rating. The functional pIns-BG pathway may play an important role in regulation of subjective itch sensation. This study first identified a key brain network to process itch.