Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemerin acts via CMKLR1 and GPR1 to stimulate migration and invasion of gastric cancer cells: putative role of decreased TIMP-1 and TIMP-2

The chemokine-like peptide, chemerin, stimulates chemotaxis in several cell types. In this study we examined the expression of putative chemerin receptors in gastric cancer and the action of chemerin on cancer cell migration and invasion. Immunohistochemical studies of gastric tumors identified expression of two putative receptors, chemokine-like receptor-1 (CMKLR1) and G-protein coupled receptor 1(GPR1), in cancer cells; there was also some expression in stromal myofibroblasts although generally at a lower intensity. The expression of both receptors was detected in a gastric cancer cell line, AGS; chemerin itself was expressed in cultured gastric cancer myofibroblasts but not AGS cells. Chemerin stimulated (a) morphological transformation of AGS cells characterized by extension of processes and cell scattering, (b) migration in scratch wound assays and (c) both migration and invasion in Boyden chamber chemotaxis assays. These responses were inhibited by two putative receptor antagonists CCX832 and α-NETA. Inhibition of receptor expression by siRNA selectively reduced CMKLR1 or GPR1 and inhibited the action of chemerin indicating that both receptors contributed to the functional response. Using a proteomic approach employing stable isotope dynamic labeling of secretomes (SIDLS) to selectively label secreted proteins, we identified down regulation of tissue inhibitors of metalloproteinease (TIMP)1 and TIMP2 in media in response to chemerin. When cells were treated with chemerin and TIMP1 or TIMP2 the migration response to chemerin was reduced. The data suggest a role for chemerin in promoting the invasion of gastric cancer cells via CMKLR1 and GPR1at least partly by reducing TIMP1 and TIMP2 expression. Chemerin receptor antagonists have potential in inhibiting gastric cancer progression.
Draw-spun, photonically annealed Ag fibers as alternative electrodes for flexible CIGS solar cells

ABSTRACT We explore the feasibility of Ag fiber meshes as electron transport layer for high-efficiency flexible Cu(In,Ga)Se2 (CIGS) solar cells. Woven meshes of Ag fibers after UV illumination and millisecond flash-lamp treatment results in a sheet resistance of 17 Ω/sq and a visible transmittance above 85%. Conductive Ag meshes are integrated into flexible CIGS cells as transparent conductive electrode (TCE) alone or together with layers of Al-doped ZnO (AZO) with various thickness of 0…900 nm. The Ag mesh alone is not able to function as a current collector. If used together with a thin AZO layer (50 nm), the Ag mesh markedly improves the fill factor and cell efficiency, in spite of the adverse mesh shadowing. When Ag mesh is combined with thicker (200 nm or 900 nm) AZO layers, no improvements in photovoltaic parameters are obtained. When comparing a hybrid TCE consisting of 50 nm AZO and Ag fiber mesh with a thick 900 nm reference AZO device, an improved charge carrier collection in the near-infrared range is observed. Regardless of the AZO thickness, the presence of Ag mesh slows down cell degradation upon mechanical tensile stress, which could be interesting for implementation into flexible thin film CIGS modules. Graphical Abstract
Negative and positive self-thoughts predict subjective quality of life in people with schizophrenia

Purpose Recently, cognitive variables such as negative and positive self-belief and thoughts have attracted much attention because they are associated with functional outcomes and quality of life (QOL). However, it is unclear how cognitive variables affect subjective and objective QOL. This study aimed to investigate the relationship of negative and positive self-belief and thoughts with subjective and objective QOL. Participants and methods Thirty-six people with schizophrenia participated in this study. Subjective and objective QOL were assessed with the Schizophrenia Quality of Life Scale (SQLS) and Quality of Life Scale (QLS), respectively. Neurocognitive function was assessed with the Brief Assessment of Cognition in Schizophrenia (BACS). Clinical symptoms were assessed with the Positive and Negative Syndrome Scale and Calgary Depression Scale for Schizophrenia. Side effects were assessed with the Drug-induced Extrapyramidal Symptoms Scale (DIEPSS). Negative and positive self-belief and thoughts were assessed with the Defeatist Performance Belief Scale and Automatic Thoughts Questionnaire-Revised. A generalized linear model was tested, with subjective and objective QOL as the response variable and symptoms, neurocognitive function, and cognitive variables that were significantly correlated with subjective and objective QOL as explanatory variables. Results In the schizophrenia group, the common objects score on the QLS was predicted by the composite BACS score, and the total QLS score was predicted by the DIEPSS score. Motivation and Energy, Psychosocial, and Symptoms and Side effects scores on the SQLS were predicted by depression and by negative automatic thought (NAT) and positive automatic thought (PAT). Conclusion Our results indicated that key targets for improving objective and subjective QOL in people with schizophrenia are side effects, neurocognitive function, depression, and NAT and PAT.
Distance-Constrained Outage Probability Analysis for Device-to-Device Communications Underlaying Cellular Networks with Frequency Reuse Factor of 2

Device-to-device (D2D) communication is affirmed as one of the dynamic techniques in improving the network throughput and capacity and reducing traffic load to the evolved Node B (eNB). In this paper, we propose a resource allocation and power control technique in which two-pairs of D2D users can simultaneously share same uplink cellular resource. In this case, interference between D2D users and cellular users is no longer insignificant so it must be properly handled. The proposed scheme considers fractional frequency reuse (FFR) scheme as a promising method that can relatively reduce the intra-cell interference. The main objective of the proposed scheme is to maximize the D2D communication throughput and overall system throughput by minimizing outage probability. Hence, we formulate an outage probability problem and overall system throughput optimization problem while guaranteeing minimum allowable signal-to-interference-plus-noise ratio (SINR). For fair distribution of cellular resources to multiple D2D pairs, we used Jain's fairness index (JFI) method. Simulation is conducted in MATLAB and our simulation results demonstrate that the proposed scheme achieves remarkable system performance as compared with existing methods.
A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification

Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.
Profiling Director's Style Based on Camera Positioning Using Fuzzy Logic

Machinima is a computer imaging technology typically used in games and animation. It prints all movie cast properties into a virtual environment by means of a camera positioning. Since cinematography is complementary to Machinima, it is possible to simulate a director’s style via various camera placements in this environment. In a gaming application, the director’s style is one of the most impressive cinematic factors, where a whole different gaming experience can be obtained using different styles applied to the same scene. This paper describes a system capable of automatically profile a director’s style using fuzzy logic. We employed 19 output variables and 15 other calculated variables from the animation extraction data to profile two different directors’ styles from five scenes. Area plots and histograms were generated, and, by analyzing the histograms, different director’s styles could be subsequently classified.
UG-hardness to NP-hardness by losing half

The 2-to-2 Games Theorem of [16, 10, 11, 17] implies that it is NP-hard to distinguish between Unique Games instances with assignment satisfying at least (1/2 - ε) fraction of the constraints vs. no assignment satisfying more than ε fraction of the constraints, for every constant ε > 0. We show that the reduction can be transformed in a non-trivial way to give a stronger guarantee in the completeness case: For at least (1/2 - ε) fraction of the vertices on one side, all the constraints associated with them in the Unique Games instance can be satisfied. We use this guarantee to convert the known UG-hardness results to NP-hardness. We show: 1. Tight inapproximability of approximating independent sets in degree d graphs within a factor of [EQUATION], where d is a constant. 2. NP-hardness of approximate the Maximum Acyclic Subgraph problem within a factor of 2/3 + ε, improving the previous ratio of 14/15 + ε by Austrin et al. [4]. 3. For any predicate P-1(1) ⊆ [q]k supporting a balanced pairwise independent distribution, given a P-CSP instance with value at least 1/2 - ε, it is NP-hard to satisfy more than [EQUATION] fraction of constraints.
Understanding professional learning for Computing teachers from the perspective of situated learning

ABSTRACT Situated learning theory maintains that there is a relationship between learning and the social situation in which it occurs; learning is embedded in activity, context and culture. In terms of professional learning for teachers, this implies that effective learning takes place within a community where experts and novices meet and where practice is modelled; such a community needs to be deeply relevant to everyday practice in the classroom. In this paper, we discuss Computing At School, a grass-roots organisation that has grown up over the last 10 years through teacher communities, and also with broad support of academia and industry. In a time of increased interest in the inclusion of computer science in school curricular, Computing At School is a community of practice of all teachers affected by curriculum change in Computing and models an innovative approach to professional learning that is based on community and support. We describe here how Computing At School draws on situated learning theory to contribute to the development of Computing in the curriculum, evidencing both the journey and lessons learned.
Non-intrusive Tensor Reconstruction for High-Dimensional Random PDEs

Abstract This paper examines a completely non-intrusive, sample-based method for the computation of functional low-rank solutions of high-dimensional parametric random PDEs, which have become an area of intensive research in Uncertainty Quantification (UQ). In order to obtain a generalized polynomial chaos representation of the approximate stochastic solution, a novel black-box rank-adapted tensor reconstruction procedure is proposed. The performance of the described approach is illustrated with several numerical examples and compared to (Quasi-)Monte Carlo sampling.
Ranking Artists: An Internet-Era Analysis

To provide guidance to the vastly expanded, uncurated art world made available through the Internet, the author developed a methodology for objectively and repeatably rating artists. He then applied that methodology to Western painters in particular, creating a ranked list of the significance of nearly 10,000 of those painters. Analyzing the process, he observed that the Internet not only greatly broadens access to art but also provides the tools needed to curate that access in a meaningful, scientific manner. The analysis also exposes questions about both the methods used and more traditional art history sources, which can be explored through alternative methods.
Machine Learning Based Unified Framework for Diabetes Prediction

Machine learning gained a significant position in healthcare services (HCS) due to its ability to improve the disease prediction in HCS. Machine learning techniques and artificial intelligence have already been worked in the HCS area. Recently, diabetes is a notable public chronic disease worldwide. It is growing rapidly because of bad lifestyles, taking more junk food and also lake of health awareness. Therefore, there is a need of framework that can effectively track and monitor people's diabetes and health condition within an application view. In this study, we proposed a framework for real time diabetes prediction, monitoring and application (DPMA). Our objective is to develop an optimized and efficient machine learning (ML) application which can effectually recognize and predict the condition of the diabetes. In this work, five most important machine learning classification techniques were considered for predicting diabetes. However, we use different evaluation criteria to investigate the performance of these classification techniques. In addition, performance measurement of the classification techniques was evaluated by applying the 10-fold cross validation method. The analysis results show that Naïve Bayes achieved highest performance than the other classifiers, obtaining the F1 measure of 0.74.
Blockchain Technology for Supply Chain Traceability, Transparency and Data Provenance

The mining and metals industry is a critical component of the global economy. However, many operational and commercial practices remain inefficient and antiquated, leading to critical data omissions, security vulnerabilities, and even corruption. Mining supply chain faces several challenges like traceability, transparency, interoperability between supplier platforms and so on. Traditional systems are inefficient and hence this paper explores the use of an emerging digital technology named blockchain. The blockchain is a distributed digital ledger that keeps a record of every transaction securely and reliably without the need of third parties that reduces the exposure of the data to hackers. Blockchain technology improves productivity by replacing the standard contract with smart contracts. This paper outlines several key applications of blockchain for the mining industry.
COFFE 2

FPGAs are becoming more heteregeneous to better adapt to different markets, motivating rapid exploration of different blocks/tiles for FPGAs. To evaluate a new FPGA architectural idea, one should be able to accurately obtain the area, delay, and energy consumption of the block of interest. However, current FPGA circuit design tools can only model simple, homogeneous FPGA architectures with basic logic blocks and also lack DSP and other heterogeneous block support. Modern FPGAs are instead composed of many different tiles, some of which are designed in a full custom style and some of which mix standard cell and full custom styles. To fill this modelling gap, we introduce COFFE 2, an open-source FPGA design toolset for automatic FPGA circuit design. COFFE 2 uses a mix of full custom and standard cell flows and supports not only complex logic blocks with fracturable lookup tables and hard arithmetic but also arbitrary heterogeneous blocks. To validate COFFE 2 and demonstrate its features, we design and evaluate a multi-mode Stratix III-like DSP block and several logic tiles with fracturable LUTs and hard arithmetic. We also demonstrate how COFFE 2’s interface to VTR allows full evaluation of block-routing interfaces and various fracturable 6-LUT architectures.
How Can Affect Be Detected and Represented in Technological Support for Physical Rehabilitation?

Although clinical best practice suggests that affect awareness could enable more effective technological support for physical rehabilitation through personalisation to psychological needs, designers need to consider what affective states matter, and how they should be tracked and addressed. In this article, we set the standard by analysing how the major affective factors in chronic pain (pain, fear/anxiety, and low/depressed mood) interfere with everyday physical functioning. Further, based on discussion of the modality that should be used to track these states to enable technology to address them, we investigated the possibility of using movement behaviour to automatically detect the states. Using two body movement datasets on people with chronic pain, we show that movement behaviour enables very good discrimination between two emotional distress levels (F1=0.86), and three pain levels (F1=0.9). Performance remained high (F1=0.78 for two pain levels) with a reduced set of movement sensors. Finally, in an overall discussion, we suggest how technology-provided encouragement and awareness can be personalised given the capability to automatically monitor the relevant states, towards addressing the barriers that they pose. In addition, we highlight movement behaviour features to be tracked to provide technology with information necessary for such personalisation.
IMU-Aided Motion Estimation for the RGB-D Camera in Manhattan Structures

In this work, we develop a novel IMU (Inertial Measurement Unit)-aided plane-based visual odometry method to estimate the pose of an RGB-D camera and reconstruct large scale corridor-like environments. The camera with an IMU chip attached to it is mounted on the mobile robot which explores freely in 3 DOF. Obviously, the low textured planes and the Manhattan structure are very apparent in the corridor, we exploit those structures to estimate the rotation of the camera and meanwhile eliminate the accumulated error. In order to construct the Manhattan coordinate, we propose a fast plane segmentation method to extract the floor and the walls based on horizontal and vertical histograms induced by the inverse-depth map. Furthermore, to deal with the limitations in the presence of more clutter, we define the major planes and minor planes, and regard the IMU as the angle measuring instrument with the Gaussian noise so that we can provide a larger weight for the rotations estimated from the right Manhattan structure. By using of our real-world data, the experimental result shows the promising performance in term of accuracy and robustness.
Linear Inequality Constraints for Neural Network Activations

We propose a method to impose homogeneous linear inequality constraints of the form $Ax\leq 0$ on neural network activations. The proposed method allows a data-driven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time after unconstrained training. However, this is an expensive operation. By directly incorporating the constraints into the architecture, we can significantly speed-up inference at test time; for instance, our experiments show a speed-up of up to two orders of magnitude over a projection method. Our algorithm computes a suitable parameterization of the feasible set at initialization and uses standard variants of stochastic gradient descent to find solutions to the constrained network. Thus, the modeling constraints are always satisfied during training. Crucially, our approach avoids to solve an optimization problem at each training step or to manually trade-off data and constraint fidelity with additional hyperparameters. We consider constrained generative modeling as an important application domain and experimentally demonstrate the proposed method by constraining a variational autoencoder.
Empathic Robot for Group Learning

This work explores a group learning scenario with an autonomous empathic robot. We address two research questions: (1) Can an autonomous robot designed with empathic competencies foster collaborative learning in a group context? (2) Can an empathic robot sustain positive educational outcomes in long-term collaborative learning interactions with groups of students? To answer these questions, we developed an autonomous robot with empathic competencies that is able to interact with a group of students in a learning activity about sustainable development. Two studies were conducted. The first study compares learning outcomes in children across three conditions: learning with an empathic robot; learning with a robot without empathic capabilities; and learning without a robot. The results show that the autonomous robot with empathy fosters meaningful discussions about sustainability, which is a learning outcome in sustainability education. The second study features groups of students who interact with the robot in a school classroom for 2 months. The long-term educational interaction did not seem to provide significant learning gains, although there was a change in game-actions to achieve more sustainability during game-play. This result reflects the need to perform more long-term research in the field of educational robots for group learning.
Privacy and Utility Tradeoff in Approximate Differential Privacy

We characterize the minimum noise amplitude and power for noise-adding mechanisms in $(\epsilon, \delta)$-differential privacy for single real-valued query function. We derive new lower bounds using the duality of linear programming, and new upper bounds by proposing a new class of $(\epsilon,\delta)$-differentially private mechanisms, the \emph{truncated Laplacian} mechanisms. We show that the multiplicative gap of the lower bounds and upper bounds goes to zero in various high privacy regimes, proving the tightness of the lower and upper bounds and thus establishing the optimality of the truncated Laplacian mechanism. In particular, our results close the previous constant multiplicative gap in the discrete setting. Numeric experiments show the improvement of the truncated Laplacian mechanism over the optimal Gaussian mechanism in all privacy regimes.