Miracle-dz/UniFAR-FTUs huggingface dataset:
Anew C-FDFD method capturing per-unit length parameter of quasi-TEM mode transmission line with reduced computational resource

Through this paper, authors will propose a new C-FDFD (compact finite-difference frequency-domain) method, which can reduce the size of computational matrix by using a new matrix formulation and quasi-TEM approximation. Generally, the decreased matrix size reduces the computational time and required memory size. When the number of numerical grids in x-and y-axis, which are employed for modeling transverse cross-section of waveguide or transmission line whose longitudinal direction is parallel to z-axis, is Nx and Ny, respectively, the computational matrix size required by the recent work previously presenting C-FDFD is (2NxNy) × (2NxNy). Although several papers have presented computationally efficient solutions for C-FDFD determining the dispersion characteristics of guided wave structures, authors have recently developed a new way to significantly reduce the computational matrix size. A new matrix formulation for newly proposed C-FDFD will be presented, based on which the required matrix size to be computed can be reduced by (2Ny) × (2Ny).
Towards Practical Privacy-Preserving Collaborative Machine Learning at a Scale

Collaborative machine learning allows multiple participants to get a global and valuable insight over their joint data. Nonetheless, in data-sensitive applications, it is crucial to maintain confidentiality across the end-to-end path the data follows from model training phase to the inference phase, preventing any form of information leakage about training data, the learned model, or the inference queries. In this paper, we present our approach to addressing this problem through PrivML, a framework for end-to-end outsourced privacy-preserving data classification over encrypted data. We provide some preliminary results comparing our proposal with state of the art solutions as well as some insight on our prospective research plan.
Forex Rates Prediction on EUR/USD with Simple Moving Average Technique and Financial Factors

International businesses usually use Forex exchange market to support their business. However, investing has a risk so they need an accuracy Forex information to make the decision. The simple moving average (SMA) has been widely used and a well-known moving average method for time series forecasting. In this empirical study, we would like to do a performance analysis of EUR/U SD Forex rates by adding simple moving averaged technique and financial factors (Dollar Index (DX), US Interest rate (federal funds rate), Inflation rate (IR) and real Gross Domestic Product (GDP)). Four Forex datasets are Forex, Forex with factors, Forex with SMA, and Forex with factors and SMA to forecast Forex rates. Research efforts on Multilayer Perceptron (MLP) and Linear Regression (LM) for forecasting EUR/USD Forex rates. It is shown by using mean square error (MSE) that by adding financial factors and simple moving average to Forex datasets, the performance has improved significantly.
First Principle Analysis of Li-Doped Armchair Graphene Nanoribbons for Nanoscale Metal Interconnect Applications

In this paper, the effect of alkali metal, Li, as substitutional doping in armchair Graphene Nanoribbons (AGNRs) is investigated. Electronic and transport properties and structural stability of Li-doped AGNRs are investigated using density functional theory (DFT) and non-equilibrium Green's function (NEGF). First principle calculations have been performed on pristine (undoped), center Li-doped, one-edge Li- terminated and both-edge Li-terminated AGNRs. Our calculations reveal that all the structures analyzed are thermodynamically stable. Based on transmission spectrum and standard two-probe setup based I-V characteristics of all the considered configurations, center Li-doped AGNRs are found to be the most suitable candidate for on-chip interconnect applications. For center Li-doped AGNRs, kinetic inductance, Lk, and quantum capacitance, CQ, are extracted as 12.51 nH/μm and 2.7 fF/μm, respectively, which results in nearly 7x, 2.5x and 1.1x higher current as compared to pristine, one-edge Li-terminated and both-edge Li-terminated AGNRs, respectively. We have also compared our results with center Fe-doped AGNRs, where center Li-doped AGNRs provide 1.71x higher current. Our study suggests about the substitutional doping of Li at the center in AGNRs make it an excellent metal that can be used in advanced nanoscale interconnect applications. In addition, this study can be extended towards the use of multiple layers of center Li-doped AGNRs in future that may further improve the interconnect performance.
Robust Adaptive Beamforming Using Support Vector Machines

A robust adaptive beamformer based on the support vector machines is proposed to solve the rapid degradation of the performance of minimum variance distortionless response beamformer when the steering vector is mismatched. The proposed beamformer assumes that the angle error range in the steering vector can be estimated; the array only responds within this range, and the array response outside the angle error range is considered as the abnormal response. The proposed method is expressed in the form of support vector regression, and Vapnik’s $\varepsilon $ -insensitive quadratic loss function which can improve the robustness of beamformer is used as a regular term to punish the abnormal response. The assumption that the array only responds within a certain angle range reflects the sparsity of support vector regression and improves the ability of the algorithm against steering vector mismatch because the energy conservation theorem holds that the response is large. The regular term ensures that a deep null exists in the direction of interference, the interference can be effectively suppressed. The iterative reweighted least square procedure can converge to the real solution of the SVM regression problem and is easy to calculate; thus, it is used to solve the algorithm proposed in this study. The effectiveness of the algorithm is verified through computer simulations.
Completely Contactless Finger-Knuckle Recognition using Gabor Initialized Siamese Network

This document presents a novel approach for contactless finger-knuckle biometric modality using Gabor initialized Deep Siamese Network. For feature extraction of finger-knuckle creases, Gabor filter is used in convolutional layer of twin CNN of Siamese Network. Validation of the model uses N-way One S hot Learning technique. A database of 146 different subjects was recorded using a smartphone camera. It contains 5 different dorsal finger-knuckle images of right-hand index finger of each individual. Experimental results show an accuracy of 94.6% and a fast convergence rate of model, which illustrate the ease of use of finger-knuckle biometrics in online applications, specifically involving smartphones, laptops and other real-time systems involving biometric verifications.
Fine-tuning the BERTSUMEXT model for Clinical Report Summarization

Background: Medical personnel are expected to parse through scores of reports each day, covering the medical history of their patients. This reading task is crucial to the effectiveness of the healthcare provided. However, it has been noticed that doctors often have to spend a lot of time going through these documents, in order to get a concise gist of the most medically relevant details. This could even affect the amount of time left for doctor-patient interaction. It is in this scenario, that the potential usefulness of an automatic clinical report summarization tool becomes apparent. Such a system would save a lot of effort for the doctor, and make a lot of time available for quality patient-doctor interaction. The focus of this paper is on extractive summarization.Method: Due to its vast pre-training, BERT (Bidirectional Encoder Representations from Transformers) is one of the most knowledgeable NLP (Natural Language Processing) models currently available- making it one of the best choices for a task like summarization. BERTSUM is the BERT version fine-tuned for summarization, BERTSUMEXT being the extractive summarization variant. The BERTSUMEXT architecture has previously been used to create a model that has been extensively pre-trained on the CNN/DailyMail dataset of news articles and corresponding summaries. It was noticed through testing that this pre-trained version of BERTSUMEXT does not perform very well on clinical reports and therefore needs to be improved to be employed in a clinical report summarization system. The method adopted here is to further train the BERTSUMEXT model using different training strategies on a clinical report summarization dataset and assess the performance improvement. The idea is to expand BERTSUMEXT’s knowledge to give it a ‘medical edge’ that it lacks.Results: The training strategy that modifies the parameter values of the extractive summarization layers of the BERTSUMEXT architecture shows a clear improvement on all nine parameters of the ROUGE (Recall Oriented Understudy for Gisting Evalution) automatic evaluation metric and the human evaluation paradigm. The ROUGE metric evaluates summary quality by measuring the overlap between the reference gold summary and the candidate summary generated by the model. The Human Evaluation Paradigm is a method where we obtain a professional doctor’s opinion on the summary quality produced by the model.
Polynomial Regression Multi-color Separation Method Based on Subspace Partition

A new color separation method was proposed.Multi-color printer gamut was separated into partitions;every partition was divided into subspaces.Polynomial models were created in subspaces with least square method.Optimal subspace was found first when the targeting colorimetric value was in color separation.Separation result was obtained by solving polynomial equation.The experiment showed that this separation method performs well in accuracy and stability and satisfies the requirement of multi-color high-precision printing.
Enabling Communication via Automotive Radars: An Adaptive Joint Waveform Design Approach

Large scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized for communication. For this purpose, joint automotive radar-communication (JARC) system designs have been proposed in the literature to perform both functions using the same waveform. However, employing a large band in the mmWave spectrum deteriorates the performance of both radar and communication functions due to frequency-selectivity. In this paper, we address the optimal joint waveform design problem for wideband JARC systems via Orthogonal Frequency-Division Multiplexing (OFDM). We show that the problem is a non-convex quadratically constrained quadratic fractional programming (QCQFP) problem, which is known to be NP-hard. Existing approaches to solve QCQFP include Semidefinite Relaxation (SDR) and randomization approaches, which have high time complexity. Instead, we propose an approximation method to solve QCQFP more efficiently by leveraging structured matrices in the quadratic fractional objective function. Finally, we evaluate the efficacy of the proposed approach through numerical results.
Truly Scalable K-Truss and Max-Truss Algorithms for Community Detection in Graphs

The notion of <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-truss has been introduced a decade ago in social network analysis and security for community detection, as a form of cohesive subgraphs less stringent than a clique (set of pairwise linked nodes), and more selective than a k-core (induced subgraph with minimum degree <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>). A <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-truss is an inclusion-maximal subgraph <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> in which each edge belongs to at least <inline-formula> <tex-math notation="LaTeX">$k-2$ </tex-math></inline-formula> triangles inside <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>. The truss decomposition establishes, for each edge <inline-formula> <tex-math notation="LaTeX">$e$ </tex-math></inline-formula>, the maximum <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> for which <inline-formula> <tex-math notation="LaTeX">$e$ </tex-math></inline-formula> belongs to a <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-truss. Analogously to the largest clique and to the maximum <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-core, the strongest community for <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-truss is the max-truss, which corresponds to the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-truss having the maximum <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>. Even though the computation of truss decomposition and of the max-truss takes polynomial time, on a large scale, it suffers from handling a potentially cubic number of wedges. In this paper, we provide a new algorithm FMT, which advances the state of the art on different sides: lower execution time, lower memory usage, and no need for expensive hardware. We compare FMT experimentally with the most recent state-of-the-art algorithms on a set of large real-world and synthetic networks with over a billion edges. The massive improvement allows FMT to compute the max-truss of networks of tens of billions of edges on a single standard server machine.
OST: On-Demand TSCH Scheduling with Traffic-Awareness

As the emerging Internet of Things (IoT) devices and applications flourish, demand for reliable and energy-efficient low-power wireless network protocols is surging. For this purpose, IEEE 802.15. 4 standardized time-slotted channel hopping (TSCH), a promising and viable link-layer solution that has shown outstanding performance achieving over 99% reliability with low duty-cycles. However, it lacks one thing, flexibility. It is not adaptable to a wide variety of applications with varying traffic load and unpredictable routing topology due to its static timeslot scheduling. To this end, we propose OST, an On-demand Scheduling scheme for TSCH with traffic-awareness. In OST, each node dynamically self-adjusts the frequency of timeslots at run time according to time-varying traffic intensity. Moreover, it features on-demand resource allocation to handle bursty/queued packets in a timely manner. By doing so, OST aims to minimize its energy consumption while guaranteeing reliable packet delivery. We evaluate OST on a large-scale 72-node testbed, demonstrating that it achieves improvement of 60% in reliability and 52% in energy-efficiency compared to the state of the art.
Performance Comparison of Rectangular and Circular Coplanar Textile Antennas

This paper presents a comparative study of a rectangular and circular patch antennas above the AMC structure, operating on two 2.4 GHz and 5.8 GHz frequency bands for WLAN applications. This structure is entirely made using textile materials. The results of simulations and measurements are presented to compare the performance of the two antennas. CST Microwave Studio is used to design and compare the performance of antennas. Based on the results found, it has been found that the rectangular patch antenna has a better performance compared to the circular patch antenna. And the proposed antennas have a good performance/size ratio compared to antennas exiting in the literature.
R-Abax: A Radiation Hardening Legalisation Algorithm Satisfying TMR Spacing Constraints

Faults caused by ionising radiation have become a significant reliability issue in modern ICs. However, the Radiation Hardening (RADHARD) design flow differs from the standard design flow. Thus, there is not sufficient support from industrial EDA tools. In this work, we present a Triple Modular Redundancy (TMR) Radiation Hardening (RADHARD) methodology, based on the replacement of Flip-Flops (FFs) to a TMR structure, consisting of a FF triplet and a majority voter, as well as a custom, Displacement-driven legalisation algorithm, called R-Abax, able to satisfy user-specified, minimum distances between the FFs of each triplet. Our RADHARD legalisation algorithm is fully compatible with existing EDA tools. By ensuring a minimum spacing between triplet FFs of each TMR structure, we reduce the probability of a particle strike affecting more than one triplet instances. We present the impact of our RADHARD flow, for a set of spacing constraints, to Power, Performance and Area (PPA) on a set of 11 OpenCores benchmarks. On average, a larger spacing between FF triplets worsens a design's Quality-of-Results (QoR), but not significantly, making our RADHARD flow attractive for reducing radiation faults.
Field test of a GPS location system for moose Alces alces under Scandinavian boreal conditions

This paper reports on field tests of an animal-borne GPS telemetry system for moose Alces alces in northern Sweden. Tests involved accuracy of locations (standard mode GPS), percentage of successful location attempts under different canopy conditions, effect of movement, and performance of the GPS telemetry system on free-ranging moose. Locational accuracy was better than 92 and 183 m 95% of the time, and better than 42 and 74 m 50% of the time, respectively, dependent on whether the GPS receiver recorded a 2- or 3-dimensional location (3 or 4 satellites used to calculate the location). Percentage of successful location attempts ranged within 69–100%, and varied inversely with over-storey canopy cover and basal area of stems. Thick canopy cover and high stem basal area reduced locational accuracy and the percentage of successful location attempts. A backpack trial indicated that movement rate of 3–4 km/hour may reduce the percentage of successful location attempts under forest canopy. On moose, approximately 75% of attempts resulted in a location, the success rate being highest during winter/spring and lowest during fall. It is concluded that GPS has a great potential in wildlife telemetry studies, but effects of movement and habitat selection have to be addressed further.
Evaluation on Feed-Nutritional Value of Spent Mushroom (Pleurotus osteratus, Pleurotus eryngii, Flammulina velutupes Substrates as a Roughage Source for Ruminants)

본 연구에서는 느타리, 새송이 및 팽이버섯 폐배지를 버섯 종류별, 배지에 이용된 주원료별로 나누어 반추가축 사료로서의 영양적 가치를 구명하기 위해서 화학성분, in vitro 소실율 실험 및 in situ darcon bag 실험을 수행하였다. 버섯 폐배지는 고섬유소(NDF 64～78%), 저단백질(CP 7～11%) 조사료원에 속하였다. 주원료별 폐배지는 섬유소 성분이 가장 높은 톱밥 주원료 폐배지와 비교해서 폐면 주원료 폐배지는 NDF 함량이 낮고(P 【This study was conducted to evaluate the feed-nutritional value of spent mushroom substrates(SMS) as a roughage source for ruminants through in vitro and in situ experiments. The SMS was classified into a roughage source with high fiber(NDF 64～78%) and low protein(CP 7～11%). The chemical composition of SMS was affected mainly by the primary culture ingredient rather than mushroom species. Compared with sawdust-SMS, cotton waste-SMS contained less(P】
Upper bounds for Model-Free Row-Sparse Principal Component Analysis

Sparse principal component analysis (PCA) is a widely-used dimensionality reduction tool in statistics and machine learning. Most meth-ods mentioned in literature are either heuristics for good primal feasible solutions under statistical assumptions or ADMM-type algorithms with stationary/critical points convergence property for the regularized reformulation of sparse PCA. However, none of these methods can efﬁciently verify the quality of the solutions via comparing current objective values with their dual bounds, especially in model-free case. We propose a new framework that ﬁnds upper (dual) bounds for the sparse PCA within polynomial time via solving a convex integer program (IP). We show that, in the worst-case, the dual bounds provided by the convex IP is within an afﬁne function of the global optimal value. Moreover, in contrast to the semi-deﬁnition relaxation, this framework is much easier to scale for large instances. Numerical results on both artiﬁcial and real cases are reported to demonstrate the advantages of our method.
The prognostic value of HGF-c-MET signaling pathway in Gastric Cancer: a study based on TCGA and GEO databases

Gastric cancer is a heterogeneous tumor that underlying molecular mechanisms are largely unclear. This study aimed to elucidate the expression level of HGF-c-MET in gastric cancer patients and to investigate the prognostic and diagnostic value of HGF-c-MET. In silico analysis of the TCGA and GEO database found that HGF and c-MET mRNA expression are significantly higher in gastric cancer tissues than those in peritumor tissues. Both higher mRNA expression of HGF and c-MET were associated with a poorer prognosis. c-MET expression was modulated by methylation in the promoter regions. HGF was positively correlated with CD8+ T cell, CD4+ T cell, macrophage, neutrophil and dendritic cell. Furthermore, functional enrichment analysis and protein-protein interaction networks further shown that HGF-c-MET and related proteins mainly participated in growth factor receptor binding, protein tyrosine kinase activity and signaling receptor binding. Finally, outcome of GSEA analysis showed 13 shared KEGG pathways enriched in high expressed group of HGF and c-MET.
Effective Langevin equations for a polar tracer in an active bath

We study the motion of a polar tracer, having a concave surface, immersed in a two-dimensional suspension of active particles. Using Brownian dynamics simulations, we measure the distributions and auto-correlation functions of force and torque exerted by active particles on the tracer. The tracer experiences a finite average force along its polar axis, while all the correlation functions show exponential decay in time. Using these insights we construct the full coarse-grained Langevin description for tracer position and orientation, where the active particles are subsumed into an effective self-propulsion force and exponentially correlated noise for both translations and rotations. The ensuing mesoscopic dynamics can be described in terms of five dimensionless parameters. We perform a thorough parameter study of the mean squared displacement, which illustrates how the different parameters influence the tracer dynamics, which crosses over from a ballistic to diffusive motion. We also demonstrate that the distribution of tracer displacements evolves from a non-Gaussian shape at early stages to a Gaussian behavior for sufficiently long times. Finally, for a given set of microscopic parameters, we establish a procedure to estimate the matching parameters of our effective model, and show that the resulting dynamics is in a very good quantitative agreement with the one obtained in Brownian dynamics simulations.
Lignées d'addition sur l'espèce Gossypium hirsutum L. II. Description phénotypique de quelques lignées d'addition monosomique de G. anomalum et G. stocksii

Plusieurs lignees d'addition monosomique et disomique de chromosomes de G. anomalum et de G. stocksii sont decrites a partir des modifications que le materiel chromosomique sauvage introduit provoque sur le phenotype du cotonnier. D'apres la nomenclature proposee pour les identifier, ce sont les chromosomes E1.I, B1.III, E1.III, B1.VI, E1.VI, B1.VIII et E1.IX qui font l'objet de cette etude. Il est difficile de discerner, a partir de la description d'un phenotype, la part des modifications apportees par l'introduction de genes nouveaux et celle qui est due a la configuration caryotypique de la plante. D'autre part, on note une remarquable convergence d'actio des chromosomes homeologues, ce qui permet de discuter l'hypothese d'une relative stabilite chromosomique a l'interieur du genre Gossypium