Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and validation of an instrument for measuring critical success factors (CSFs) of technical education – a TQM approach

Like other systems, technical education system is also dynamic in nature. It faces many challenges in responding to societal, technological and economic changes in the local and global environment. The issue today is to ensure quality education delivery system for technical education. The literature shows that many educational institutes have initiated efforts for quality improvements. However, there is neither sufficient literature (regarding identification of critical factors responsible for excellence in technical education), nor appropriate measures (research instruments) to determine those factors. In this context it is necessary to identify key critical success factors (CSFs), which may be given special attention for ensuring desired excellence in technical education. Though different authors have proposed different ways to address the quality related issues in various domains, on the basis of critical review of literature, this study proposes use of a new research instrument for identification of critical success factors by using total quality management (TQM) as the base. The developed instrument was assessed for its reliability and validity using standard methods. The results indicated that the developed instrument was reliable and valid, which can be used to identify the critical success factors important for excellence in technical education.
Skin feature point tracking using deep feature encodings

Facial feature tracking is a key component of imaging bal- listocardiography (BCG) where accurate quantiﬁcation of the displacement of facial keypoints is needed for good heart rate estimation. Skin feature tracking enables video-based quantiﬁcation of motor degradation in Parkinson’s disease. Tradi- tional computer vision algorithms include Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Lucas-Kanade method (LK). These have long represented the state-of-the-art in eﬃciency and accuracy but fail when common deformations, like aﬃne local transformations or illumination changes, are present. Over the past ﬁve years, deep convolutional neural networks have outperformed traditional methods for most computer vi- sion tasks. We propose a pipeline for feature tracking, that ap-plies a convolutional stacked autoencoder to identify the most similar crop in an image to a reference crop containing the feature of interest. The autoencoder learns to represent image crops into deep feature encodings speciﬁc to the object category it is trained on. We train the autoencoder on facial images and validate its ability to track skin features in general using manually labeled face and hand videos. The tracking errors of distinctive skin features (moles) are so small that we cannot exclude that they stem from the manual labelling based on a χ 2 -test. With a mean error of 0.6-4.2 pixels, our method outperformed the other methods in all but one scenario. More importantly, our method was the only one to not diverge. We conclude that our method creates better feature descriptors for feature tracking, feature matching, and image regis- tration than the traditional algorithms.
Phonon, electron, and magnon excitations in antiferromagnetic 
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-type MnPt

Antiferromagnetic L1$_{0}$-type MnPt is a material with relatively simple crystal and magnetic structure, recently attracting interest due to its high N{\'{e}}el temperature and wide usage as a pinning layer in magnetic devices. While it is experimentally well characterized, the theoretical understanding is much less developed, in part due to the challenging accuracy requirements dictated by the small underlying energy scales that govern magnetic ordering in antiferromagnetic metals. In this work, we use density functional theory, the Korringa-Kohn-Rostoker formalism, and a Heisenberg model to establish a comprehensive theoretical description of antiferromagnetic L1$_{0}$-type MnPt, along with accuracy limits, by thoroughly comparing to available literature data. Our simulations show that the contribution of the magnetic dipole interaction to the magnetocrystalline anisotropy energy of $K_{1}$=1.07$\times 10^{6}$\,J/m$^3$ is comparable in magnitude to the spin-orbit contribution. Using our result for the magnetic susceptibility of $5.25\times10^{-4}$, a lowest magnon frequency of about 2.02\,THz is predicted, confirming THz spin dynamics in this material. From our data for electron, phonon, and magnon dispersion we compute the individual contributions to the total heat capacity and show that the dominant term at or above 2\,K arises from phonons. From the Landau-Lifshitz-Gilbert equation, we compute a N\'{e}el temperature of 990--1070 K. Finally, we quantify the magnitude of the magneto-optical Kerr effect generated by applying an external magnetic field. Our results provide insight into the underlying physics, which is critical for a deep understanding of fundamental limits of the time scale of spin dynamics, stability of the magnetic ordering, and the possibility of magneto-optical detection of collective spin motion.
Isotuning With Applications To Scale-Free Online Learning

We extend and combine several tools of the literature to design fast, adaptive, anytime and scale-free online learning algorithms. Scale-free regret bounds must scale linearly with the maximum loss, both toward large losses and toward very small losses. Adaptive regret bounds demonstrate that an algorithm can take advantage of easy data and potentially have constant regret. We seek to develop fast algorithms that depend on as few parameters as possible, in particular they should be anytime and thus not depend on the time horizon. Our first and main tool, isotuning, is a generalization of the idea of balancing the trade-off of the regret. We develop a set of tools to design and analyze such learning rates easily and show that they adapts automatically to the rate of the regret (whether constant, $O(\log T)$, $O(\sqrt{T})$, etc.) within a factor 2 of the optimal learning rate in hindsight for the same observed quantities. The second tool is an online correction, which allows us to obtain centered bounds for many algorithms, to prevent the regret bounds from being vacuous when the domain is overly large or only partially constrained. The last tool, null updates, prevents the algorithm from performing overly large updates, which could result in unbounded regret, or even invalid updates. We develop a general theory using these tools and apply it to several standard algorithms. In particular, we (almost entirely) restore the adaptivity to small losses of FTRL for unbounded domains, design and prove scale-free adaptive guarantees for a variant of Mirror Descent (at least when the Bregman divergence is convex in its second argument), extend Adapt-ML-Prod to scale-free guarantees, and provide several other minor contributions about Prod, AdaHedge, BOA and Soft-Bayes.
Regional Calibration of Solar Radiation and Reference Evapotranspiration Estimates with Minimal Data in Florida

Reference evapotranspiration and solar radiation estimation methods that use only measured temperature have been demonstrated to require regional calibration for acceptable performance. This study was conducted to evaluate regional calibration of incoming solar radiation and reference evapotranspiration in Florida, USA. Using satellite-derived estimates of solar radiation and reference evapotranspiration as the standard for comparison, regional calibration of the Hargreaves-Samani solar radiation model and the Turc temperature-based reference evapotranspiration equation were evaluated using monthly temperature data from 22 weather stations in Florida. Station-specific and regional calibration of the Hargreaves-Samani solar radiation model was found to produce comparable results, indicating the validity of this approach in regional calibration of radiation estimates. Using the regionally calibrated radiation estimates, the original Turc equation was found to underestimate reference evapotranspiration, indi.
SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor. Accurate and automatic segmentation of organs at risk (OAR) of computed tomography (CT) images is clinically significant. In recent years, deep learning models represented by U-Net have been widely applied in medical image segmentation tasks, which can help doctors with reduction of workload and get accurate results more quickly. In OAR segmentation of NPC, the sizes of OAR are variable, especially, some of them are small. Traditional deep neural networks underperform during segmentation due to the lack use of global and multi-size information. This paper proposes a new SE-Connection Pyramid Network (SECP-Net). SECP-Net extracts global and multi-size information flow with se connection (SEC) modules and a pyramid structure of network for improving the segmentation performance, especially that of small organs. SECP-Net also designs an auto-context cascaded network to further improve the segmentation performance. Comparative experiments are conducted between SECP-Net and other recently methods on a dataset with CT images of head and neck. Five-fold cross validation is used to evaluate the performance based on two metrics, i.e., Dice and Jaccard similarity. Experimental results show that SECP-Net can achieve SOTAperformance in this challenging task.
A multi-objective model for project portfolio selection to implement lean and Six Sigma concepts

Lean and Six Sigma methodologies play an important role in the quality and cost driven world with high competition. In this paper, we develop a unique decision support system that utilizes a multi-objective formulation for project portfolio selection problem in manufacturing companies. The model can be used to effectively implement Lean and Six Sigma concepts. An industrial case study that utilizes this model for implementing the Lean and Six Sigma initiatives is also presented.
Social Sensors in Epidemiological Networks via Graph Eigenvectors

In this paper, we consider epidemiological networks which are used for modeling the transmission of contagious diseases through a population. Specifically, we study the so-called social sensors problem: given an epidemiological network, can we find a small set of nodes such that by monitoring disease transmission on these nodes, we can get ahead of the overall epidemic in the full population? In spite of its societal relevance, there has not been much statistical work on this problem, and we aim to provide an exposition that will hopefully stimulate interest in the research community. Furthermore, by leveraging classical results in spectral graph theory, we propose a novel method for finding social sensors, which achieves substantial improvement over existing methods in both synthetic and real-world epidemiological networks.
Synchronized Audio-Visual Frames with Fractional Positional Encoding for Transformers in Video-to-Text Translation

Video-to-text (VTT) is the task of automatically generating descriptions for short audio-visual video clips. It can help visually impaired people to understand scenes shown in a YouTube video, for example. Transformer architectures have shown great performance in both machine translation and image captioning. In this work, we transfer promising approaches from image captioning and video processing to VTT and develop a straightforward Transformer architecture. Then, we expand this Transformer by a novel way of synchronizing audio and video features in Transformers which we call Fractional Positional Encoding (FPE). We run multiple experiments on the VATEX dataset and improve the CIDEr and BLEU-4 scores by 21.72 and 8.38 points compared to a vanilla Transformer network and achieve state-of-the art results on the MSR-VTT and MSVD datasets. Also, our novel FPE helps increase the CIDEr score by relative 8.6 %.
Calculation of Rényi entropy in realistic quantum systems

Miha Srdinšek1,2,3,∗ Michele Casula, and Rodolphe Vuilleumier Institut des sciences du calcul et des données (ISCD), Sorbonne Université, 4 Place Jussieu, 75005 Paris. Sorbonne Université, Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC), CNRS UMR 7590, MNHM, 4 Place Jussieu, 75005 Paris. Processus d’Activation Sélectif par Transfert d’Energie Uni-électronique ou Radiative (PASTEUR), CNRS UMR 8640, Département de Chimie, École Normale Superieure, 24 rue Lhomond, 75005 Paris. (Dated: December 30, 2021)
Topological viewpoint of two-dimensional group III–V and IV–IV compounds in the presence of electric field and spin–orbit coupling by density functional theory and tight-binding model

Using the tight-binding (TB) model and density functional theory, the topological invariant of the two-dimensional (2D) group III–V and IV–IV compounds are studied in the absence and the presence of an external perpendicular electric field and spin–orbit coupling. It will be recognized that a critical value of these parameters changes the topological invariant of 2D graphene-like compounds. The significant effects of an external electric field and spin–orbit coupling are considered to the two-center overlap integrals of the Slater–Koster model involved in band structures, changing band-gap, and tuning the topological phase transition between ordinary and quantum spin Hall regime. These declare the good consistency between two theories: TB and density functional. So, this study reveals topological phase transition in these materials. Our finding paves a way to extend an effective Hamiltonian, and may instantly clear some computation aspects of the study in the field of spintronic based on the first-principles methods.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.

Increasingly stringent throughput and latency requirements in datacenter networks demand fast and accurate congestion control. We observe that the reaction time and accuracy of existing datacenter congestion control schemes are inherently limited. They either rely only on explicit feedback about the network state (e.g., queue lengths in DCTCP) or only on variations of state (e.g., RTT gradient in TIMELY). To overcome these limitations, we propose a novel congestion control algo-rithm, P OWER TCP, which achieves much more fine-grained congestion control by adapting to the bandwidth-window product (henceforth called power). P OWER TCP leverages in-band network telemetry to react to changes in the network instantaneously without loss of throughput and while keeping queues short. Due to its fast reaction time, our algorithm is particularly well-suited for dynamic network environments and bursty traffic patterns. We show analytically and empirically that P OWER TCP can significantly outperform the state-of-the-art in both traditional datacenter topologies and emerging reconfigurable datacenters where frequent bandwidth changes make congestion control challenging. In traditional datacenter networks, P OWER TCP reduces tail flow completion times of short flows by 80% compared to DCQCN and TIMELY, and by 33% compared to HPCC even at 60% network load. In re-configurable datacenters, P OWER TCP achieves 85% circuit utilization without incurring additional latency and cuts tail latency by at least 2x compared to existing approaches.
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction

For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN) estimator, which take label average over a ball around the query, are consistent but asymptotically biased particularly for a large radius of the ball. To eradicate such bias, local polynomial regression (LPoR) and multiscale $k$-NN (MS-$k$-NN) learn the bias term by local regression around the query and extrapolate it to the query itself. However, their theoretical optimality has been shown for the limit of the infinite number of training samples. For correcting the asymptotic bias with fewer observations, this paper proposes a \emph{local radial regression (LRR)} and its logistic regression variant called \emph{local radial logistic regression~(LRLR)}, by combining the advantages of LPoR and MS-$k$-NN. The idea is quite simple: we fit the local regression to observed labels by taking only the radial distance as the explanatory variable and then extrapolate the estimated label probability to zero distance. The usefulness of the proposed method is shown theoretically and experimentally. We prove the convergence rate of the $L^2$ risk for LRR with reference to MS-$k$-NN, and our numerical experiments, including real-world datasets of daily stock indices, demonstrate that LRLR outperforms LPoR and MS-$k$-NN.
BOTDA-nondestructive measurement of single-mode optical fiber attenuation characteristics using Brillouin interaction: theory

A theoretical investigation of Brillouin optical-fiber time-domain analysis (BOTDA) is described. BOTDA uses Brillouin interaction in optical fibers to analyze the attenuation characteristics of the optical fibers nondestructively. The dynamic range performance of BOTDA is approximately 10-dB greater than that of conventional optical time-domain reflectometry. >
Very Hairy Inflation

We revisit the rollercoaster cosmology based on multiple stages of monodromy inflation. Working within the framework of effective flux monodromy field theory, we include the full range of strong coupling corrections to the inflaton sector. We find that flattened potentials $V \sim \phi^p + \ldots$ with $p \lesssim 1/2$, limited to $ N \lesssim 25 - 40$ efolds in the first stage of inflation, continue to fit the CMB. They yield $0.96 \lesssim n_s \lesssim 0.97$, and produce relic gravity waves with $0.006 \lesssim r \lesssim 0.035$, in full agreement with the most recent bounds from BICEP/{\it Keck}. The nonlinear derivative corrections generated by strong dynamics in EFT also lead to equilateral non-Gaussianity $f_{NL}^{eq} \simeq {\cal O}(1) - {\cal O}(10)$, close to the current observational bounds. Finally, in multi-stage rollercoaster, an inflaton-hidden sector $U(1)$ coupling can produce a tachyonic chiral vector background, which converts rapidly into tensors during the short interruption by matter domination. The produced stochastic gravity waves are chiral, and so they may be clearly identifiable by gravity wave instruments like LISA, Big Bang Observatory, Einstein Telescope, NANOgrav or SKA, depending on the precise model realization. We also point out that the current attempts to resolve the $H_0$ tension using early dark energy generically raise $n_s$. This may significantly alter the impact of BICEP/{\it Keck} data on models of inflation.
Universal scaling at a prethermal dark state

Recent experimental and theoretical progress as well as the prospect of commercially viable quantum technologies have inspired great interest in the study of open quantum systems and their dynamics. Many open quantum systems are well described by an eﬀective non-Hermitian Hamiltonian generating a time evolution that allows eigenstates to decay and dissipate to the environment. In this framework, quantum coherent scaling is traditionally tied to the appearance of dark states, where the eﬀect of dissipation becomes negligible. Here, we discuss the universal dynamical scaling after a sudden quench of the non-Hermitian O ( N ) model Hamiltonian. While universality is generally spoiled by non-Hermiticity, we ﬁnd that for a given set of internal parameters short-time scaling behaviour is restored with an initial slip exponent profoundly diﬀerent from that of closed quantum systems. This result is tied to the compensation of dissipation by interaction eﬀects at short times leading to a prethermal dark state, where coherent many-body dynamics can be still observed.
Design and realization of the low voltage power line carrier channel simulation platform

Proposed a visualized modeling and simulating scheme on the basis of object-oriented software for the power line communication channels. Combined with the research on the theories about transmitting lines, applies a equivalent concentrated parameter model to replace the traditional distributed parameter model of the transmitting lines, and studied the realizing process of the simulation. Constructed the network topology model database, noise model database, typical load model database aiming at the low-voltage power line features. And developed a visual, easy-to-operate power line communication simulation platform under Windows operation system.
Mutual influence between language and perception in multi-agent communication games

Language interfaces with many other cognitive domains. This paper explores how interactions at these interfaces can be studied with deep learning methods, focusing on the relation between language emergence and visual perception. To model the emergence of language, a sender and a receiver agent are trained on a reference game. The agents are implemented as deep neural networks, with dedicated vision and language modules. Motivated by the mutual influence between language and perception in cognition, we apply systematic manipulations to the agents’ (i) visual representations, to analyze the effects on emergent communication, and (ii) communication protocols, to analyze the effects on visual representations. Our analyses show that perceptual biases shape semantic categorization and communicative content. Conversely, if the communication protocol partitions object space along certain attributes, agents learn to represent visual information about these attributes more accurately, and the representations of communication partners align. Finally, an evolutionary analysis suggests that visual representations may be shaped in part to facilitate the communication of environmentally relevant distinctions. Aside from accounting for co-adaptation effects between language and perception, our results point out ways to modulate and improve visual representation learning and emergent communication in artificial agents.
Cross-User Similarities in Viewing Behavior for 360° Video and Caching Implications

The demand and usage of 360° video services are expected to increase. However, despite these services being highly bandwidth intensive, not much is known about the potential value that basic bandwidth saving techniques such as server or edge-network on-demand caching (e.g., in a CDN) could have when used for delivery of such services. This problem is both important and complicated as client-side solutions have been developed that split the full 360° view into multiple tiles, and adapt the quality of the downloaded tiles based on the user’s expected viewing direction and bandwidth conditions. This article presents new trace-based analysis methods that incorporate users’ viewports (the area of the full 360° view the user actually sees), a first characterization of the cross-user similarities of the users’ viewports, and a trace-based analysis of the potential bandwidth savings that caching-based techniques may offer under different conditions. Our analysis takes into account differences in the time granularity over which viewport overlaps can be beneficial for resource saving techniques, compares and contrasts differences between video categories, and accounts for uncertainties in the network conditions and the prediction of the future viewing direction when prefetching. The results provide substantial insight into the conditions under which overlap can be considerable and caching effective, and inform the design of new caching system policies tailored for 360° video.