Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Wire Analyzer of Mechanical Tension, Electrical Continuity, and Isolation

A digital instrument that allows the measurement of the mechanical tension of an array of wires of known length and density, and the testing of their electrical continuity and isolation, has been developed. The instrument measures wire tension by measuring the fundamental frequency of the wire. Its working principle is to apply direct high voltages on neighboring wires of a wire under test and sweeping the frequency of an alternating high voltage that is also applied on those neighbors. A resonance is observed in the readout signal of the middle wire when the frequency of the alternating high voltage coincides with its fundamental frequency. The instrument automates the process over 128 wires, with eight read out simultaneously. An accuracy of 1% in the measurement of tension is achieved by this digital wire analyzer (DWA).
Decentralized Competing Bandits in Non-Stationary Matching Markets

Understanding complex dynamics of two-sided online matching markets, where the demand-side agents compete to match with the supply-side (arms), has recently received substantial interest. To that end, in this paper, we introduce the framework of decentralized two-sided matching market under non stationary (dynamic) environments. We adhere to the serial dictatorship setting, where the demand-side agents have unknown and different preferences over the supply-side (arms), but the arms have fixed and known preference over the agents. We propose and analyze a decentralized and asynchronous learning algorithm, namely Decentralized Non-stationary Competing Bandits (\texttt{DNCB}), where the agents play (restrictive) successive elimination type learning algorithms to learn their preference over the arms. The complexity in understanding such a system stems from the fact that the competing bandits choose their actions in an asynchronous fashion, and the lower ranked agents only get to learn from a set of arms, not \emph{dominated} by the higher ranked agents, which leads to \emph{forced exploration}. With carefully defined complexity parameters, we characterize this \emph{forced exploration} and obtain sub-linear (logarithmic) regret of \texttt{DNCB}. Furthermore, we validate our theoretical findings via experiments.
Comparing feature fusion strategies for Deep Learning-based kidney stone identification

This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints with the aim to produce more discriminant object features. Our approach was specifically designed to mimic the morpho-constitutional analysis used by urologists to visually classify kidney stones by inspecting the sections and surfaces of their fragments. Deep feature fusion strategies improved the results of single view extraction backbone models by more than 10\% in terms of precision of the kidney stones classification.
Error-Bounded Approximation of Pareto Fronts in Robot Planning Problems

Many problems in robotics seek to simultaneously optimize several competing objectives under constraints. A conventional approach to solving such multi-objective optimization problems is to create a single cost function comprised of the weighted sum of the individual objectives. Solutions to this scalarized optimization problem are Pareto optimal solutions to the original multi-objective problem. However, finding an accurate representation of a Pareto front remains an important challenge. Using uniformly spaced weight vectors is often inefficient and does not provide error bounds. Thus, we address the problem of computing a finite set of weight vectors such that for any other weight vector, there exists an element in the set whose error compared to optimal is minimized. To this end, we prove fundamental properties of the optimal cost as a function of the weight vector, including its continuity and concavity. Using these, we propose an algorithm that greedily adds the weight vector least-represented by the current set, and provide bounds on the error. Finally, we illustrate that the proposed approach significantly outperforms uniformly distributed weights for different robot planning problems with varying numbers of objective functions.
Automatic differentiation of nonsmooth iterative algorithms

Differentiation along algorithms, i.e., piggyback propagation of derivatives, is now routinely used to differentiate iterative solvers in differentiable programming. Asymptotics is well understood for many smooth problems but the nondifferentiable case is hardly considered. Is there a limiting object for nonsmooth piggyback automatic differentiation (AD)? Does it have any variational meaning and can it be used effectively in machine learning? Is there a connection with classical derivative? All these questions are addressed under appropriate nonexpansivity conditions in the framework of conservative derivatives which has proved useful in understanding nonsmooth AD. For nonsmooth piggyback iterations, we characterize the attractor set of nonsmooth piggyback iterations as a set-valued fixed point which remains in the conservative framework. This has various consequences and in particular almost everywhere convergence of classical derivatives. Our results are illustrated on parametric convex optimization problems with forward-backward, Douglas-Rachford and Alternating Direction of Multiplier algorithms as well as the Heavy-Ball method.
YOLoC: deploy large-scale neural network by ROM-based computing-in-memory using residual branch on a chip

Computing-in-memory (CiM) is a promising technique to achieve high energy efficiency in data-intensive matrix-vector multiplication (MVM) by relieving the memory bottleneck. Unfortunately, due to the limited SRAM capacity, existing SRAM-based CiM needs to reload the weights from DRAM in large-scale networks. This undesired fact weakens the energy efficiency significantly. This work, for the first time, proposes the concept, design, and optimization of computing-in-ROM to achieve much higher on-chip memory capacity, and thus less DRAM access and lower energy consumption. Furthermore, to support different computing scenarios with varying weights, a weight fine-tune technique, namely Residual Branch (ReBranch), is also proposed. ReBranch combines ROM-CiM and assisting SRAM-CiM to achieve high versatility. YOLoC, a ReBranch-assisted ROM-CiM framework for object detection is presented and evaluated. With the same area in 28nm CMOS, YOLoC for several datasets has shown significant energy efficiency improvement by 14.8x for YOLO (DarkNet-19) and 4.8x for ResNet-18, with <8% latency overhead and almost no mean average precision (mAP) loss (−0.5% ~ +0.2%), compared with the fully SRAM-based CiM.
A Generalized Supervised Contrastive Learning Framework

Based on recent remarkable achievements of contrastive learning in self-supervised representation learning, supervised contrastive learning (SupCon) has successfully extended the batch contrastive approaches to the supervised context and outper-formed cross-entropy on various datasets on ResNet. In this work, we present GenSCL : a generalized supervised contrastive learning framework that seamlessly adapts modern image-based regularizations (such as Mixup-Cutmix) and knowledge distillation (KD) to SupCon by our generalized supervised contrastive loss . Generalized supervised contrastive loss is a further extension of supervised contrastive loss measuring cross-entropy between the similarity of labels and that of latent features. Then a model can learn to what extent contrastives should be pulled closer to an anchor in the latent space. By explicitly and fully leveraging label information, GenSCL breaks the boundary between conventional positives and negatives, and any kind of pre-trained teacher classiﬁer can be utilized. ResNet-50 trained in GenSCL with Mixup-Cutmix and KD achieves state-of-the-art accuracies of 97.6% and 84.7% on CIFAR10 and CIFAR100 without external data, which signiﬁcantly improves the results reported in the original SupCon (1.6% and 8.2%, respectively). Pytorch implementation is available at https://t.ly/yuUO .
The Mediating Role of Workaround Behavior and the Shadow IT Usage in the Relationship between Job Characteristics and Individual Performance

This research aimed to evaluate the effects of workaround behavior and the shadow IT usage as mediating variables in the relationship between job characteristics and individual performance. To achieve the objective, a survey was conducted with 415 IT users. Data analysis was performed using SPSS and SmartPLS softwares. The main results indicate that the effect of job characteristics on individual performance is explained by the mediating variables workaround behavior and the shadow IT usage. In addition, the relationship between job characteristics with the workaround behavior and the shadow IT usage were confirmed. Finally, it was confirmed that the workaround behavior and the shadow IT usage are positively related to individual performance. As theoretical contributions this study presents information about workaround behavior that has been little explored in Brazilian researches. In addition, there is a lack of researches that has assessed how the job characteristics influence alternative solutions in the organizations.
An interdisciplinary framework for participatory modeling design and evaluation—What makes models effective participatory decision tools?

Increased requirements for public involvement in water resources management (WRM) over the past century have stimulated the development of more collaborative decision‐making methods. Participatory modeling (PM) uses computer models to inform and engage stakeholders in the planning process in order to influence collaborative decisions in WRM. Past evaluations of participatory models focused on process and final outcomes, yet, were hindered by diversity of purpose and inconsistent documentation. This paper presents a two‐stage framework for evaluating PM based on mechanisms for improving model effectiveness as participatory tools. The five dimensions characterize the “who, when, how, and why” of each participatory effort (stage 1). Models are evaluated as “boundary objects,” a concept used to describe tools that bridge understanding and translate different bodies of knowledge to improve credibility, salience, and legitimacy (stage 2). This evaluation framework is applied to five existing case studies from the literature. Though the goals of participation can be diverse, the novel contribution of the two‐stage proposed framework is the flexibility it has to evaluate a wide range of cases that differ in scope, modeling approach, and participatory context. Also, the evaluation criteria provide a structured vocabulary based on clear mechanisms that extend beyond previous process‐based and outcome‐based evaluations. Effective models are those that take advantage of mechanisms that facilitate dialogue and resolution and improve the accessibility and applicability of technical knowledge. Furthermore, the framework can help build more complete records and systematic documentation of evidence to help standardize the field of PM.
USER ACCEPTANCE OF ONLINE TRAVEL AGENTS FOR MILLENIALS AND GEN Z

Tourism is one of the sectors that contribute the most to a country's economy. Moreover, it contributes most to sustainable development such as in Indonesia. One of tourism players is an online travel agency that caters to the needs of tourists before travelling. Therefore, it is important for travel agents to meet the needs of tourists. The purpose of this study was to determine user acceptance of online travel agents in Gen Z and Millennials. This research carried out to study Unified Theory Acceptance and Use of Technology 2 (UTAUT2) model. The population of this study is everyone who ever purchased ticket and/or booking accommodation on Online Travel Agent using non-probability purposive sampling. This research collected the data by spreading electronic questionnaire (e-questionnaire). 200 respondents met criteria in this research. To analyze the data, Partial Least Square – Structural Equation Model (PLS-SEM) analyzed using SmartPLS 3.2. 8. The result of this research indicated that Social Influence, Hedonic Motivation, and Habit has significant influence on Behavioral Intention. Behavioral Intention also has significant influence on Use Behavior. In moderating effect, only Age can moderate Social Influence on Behavioral Intention. Thus, it is recommended for next research to use the complete of UTAUT2 model and add Perceived Security variable. On the other hand, Pegipegi should add more services on their main business.
You, Me, and IoT: How Internet-connected Consumer Devices Affect Interpersonal Relationships

Internet-connected consumer devices have rapidly increased in popularity; however, relatively little is known about how these technologies are affecting interpersonal relationships in multi-occupant households. In this study, we conduct 13 semi-structured interviews and survey 508 individuals from a variety of backgrounds to discover and categorize how consumer IoT devices are affecting interpersonal relationships in the United States. We highlight several themes, providing exploratory data about the pervasiveness of interpersonal costs and benefits of consumer IoT devices. These results inform follow-up studies and design priorities for future IoT technologies to amplify positive and reduce negative interpersonal effects.
University graduates’ early career decisions and interregional mobility: self-employment versus salaried job

ABSTRACT We explore the relationship between university graduates’ early career decisions and their interregional mobility. We focus on graduates’ immediate entry into the labour market, analysing the antecedents and relations of their career decisions (self-employment versus salaried job) and mobility choices (staying in their university region or moving). We use a longitudinal dataset of 3436 students from 62 Italian universities who were surveyed at graduation and one year later. We find that self-employment most likely occurs among those who study and stay in their home region, as well as those who study in a different region and return to their home one. Conversely, salaried positions are more appealing to those who, after graduation, move to a region other than their home one. Individual characteristics explain the decision to enter self-employment or accept a salaried job. In contrast, the decision to move or stay is mainly affected by contextual factors.
A comparative study of physiological and hematological profile of preeclampsia in relation to body mass index

Preeclampsia (PE) is a major reason for maternal morbidity and mortality globally. Studies showed that body mass index (BMI) is one of the risk factors of PE. In this study, the BMI and physiological and hematological profile were associated with predicting the severity of preeclampsia, so that proper counseling and antenatal care could be given for good pregnancy outcome. The study was carried out on 100 healthy normotensive pregnant and 100 diagnosed preeclamptic women. Healthy pregnant and PE were categorized into three groups based on BMI, on WHO criteria. BMI group 1 (<25 Kg/m2) considered as normal, group 2 (25 – 30 Kg/m2) as over-weight and group 3 (>30 Kg/m2) obese. Systolic blood pressure (SBP), diastolic blood pressure (DBP), hemoglobin (Hb), white blood corpuscles (WBC), red blood corpuscles (RBC) and platelets were compared in control and PE groups. Then the respective control groups were compared with PE groups. The prevalence of overweight was more in PE groups when compared to normotensive pregnancy (P=0.004). Statistically, a significant difference was not observed in BMI group1, group 2 and group 3 of control and PE in relation to SBP, DBP, Hb, WBC, RBC and platelets. But a statistically significant difference was made when respective control groups were compared with PE (P<0.005). BMI does not have any statistically significant association with SBP, DBP, Hb, WBC, RBC and platelets. BMI could not be considered as a predictor or severity of preeclampsia.
A New Population of Ultra‐Relativistic Electrons in the Outer Radiation Zone

Van Allen Probes measurements revealed the presence of the most unusual structures in the ultra‐relativistic radiation belts. Detailed modeling, analysis of pitch angle distributions, analysis of the difference between relativistic and ultra‐realistic electron evolution, along with theoretical studies of the scattering and wave growth, all indicate that electromagnetic ion cyclotron (EMIC) waves can produce a very efficient loss of the ultra‐relativistic electrons in the heart of the radiation belts. Moreover, a detailed analysis of the profiles of phase space densities provides direct evidence for localized loss by EMIC waves. The evolution of multi‐MeV fluxes shows dramatic and very sudden enhancements of electrons for selected storms. Analysis of phase space density profiles reveals that growing peaks at different values of the first invariant are formed at approximately the same radial distance from the Earth and show the sequential formation of the peaks from lower to higher energies, indicating that local energy diffusion is the dominant source of the acceleration from MeV to multi‐MeV energies. Further simultaneous analysis of the background density and ultra‐relativistic electron fluxes shows that the acceleration to multi‐MeV energies only occurs when plasma density is significantly depleted outside of the plasmasphere, which is consistent with the modeling of acceleration due to chorus waves.
Multiple Reconnection X‐Lines at the Magnetopause and Overlapping Cusp Ion Injections

Magnetospheric Multiscale (MMS) observations during an extended crossing of the Earth's dayside magnetopause show evidence of multiple reconnections at the boundary. This crossing occurred when the Interplanetary Magnetic Field (IMF) was southward and had a significant BY component. Approximately 2 hr after this crossing, the Twin Rocket Investigation of Cusp Electrodynamics‐2 rockets were launched into the northern hemisphere cusp and observed overlapping cusp ion injections. These overlapping injections are also evidence of multiple reconnections at the magnetopause. Observations more than 2 hr apart do not constitute conjunction between the spacecraft and the rockets. However, the IMF conditions during the magnetopause crossing and the cusp traversal were very similar. Therefore, had the magnetopause crossings and cusp traversals occurred at the same time, the observations would have been similar. Thus, these two events illustrate the link between multiple reconnections at the magnetopause and overlapping cusp ion injections.
Plasma GH, TSH Human Insulin-Li

THE administration of large amounts of recombinant human insulin-like growth factor-I (IGF-I) (rhIGF-I) stimulate plasma GH secretion through hypoglycemia in normal subjects [1]. The effects of rhIGF-I on GH secretion were therefore studied by the glucose clump methods [2] or nutritional intake [3], but the doses of rhIGF-I which cause hypoglycemia are considered to be pharmacological. In this study, we have examined plasma GH, TSH, PRL, IGF-I and insulin responses to physiological doses of IGF-I, which has no effect on plasma glucose levels, in 6 normal subjects (normal somatotrophs) and 10 patients with active acromegaly (tumorous somatotrophs). This is the first report which examined plasma GH responses to rhIGF-I in patients with acromegaly.
Reliability Assessment of Gas Pipelines with Corrosion Defects Based on In-Line Inspection

As the natural gas pipeline system in China is extremely complex and busy, people are coming to realize that existing system safety evaluation methods, which are premised on single pipelines or small-scale pipeline networks, are in fact not technical and rational enough for assessing the safety of a large-scale pipeline network. Therefore, ideas for ensuring the reliability of a large-scale natural gas pipeline network have recently been proposed. To calculate system reliability, the primary task is assessing the reliability of individual system components, such as pipelines and gas stations. This paper advances a reliability calculation model based on the Monte Carlo simulation for pipelines with corrosion def ects determined through in-line inspection. An example of the calculation method is provided; in this example, the reliability of a particular PetroChina natural gas pipeline project is ascertained in order to offer some experiences and references for pipeline reliability assessors.
Corrigendum and extension to the "Hamiltonicity in directed Toeplitz graphs $T_{n}\langle 1, 2; t_1, t_2 \rangle$"

We use [1] for notation and terminology not defined here. In Theorem 3.3 [1], we proved that Tn〈1, 2; 3, t〉 is hamiltonian for all n and t. Unfortunately, this theorem does not hold for n = 8 when t = 5. Here, we correct this error by proving that T8〈1, 2; 3, 5〉 is non-hamiltonian. i Then we generalize Theorem 3.9 of [1], for all t1. Finally, we address the conjecture stated in [1], which completes the hamiltonicity investigation in directed Toeplitz graphs Tn〈1, 2; t1, t2〉.
An (ℵ₀, k+2)-Theorem for k-Transversals

A family F of sets satisfies the ( p, q )-property if among every p members of F , some q can be pierced by a single point. The celebrated ( p, q )-theorem of Alon and Kleitman asserts that for any p ⩾ q ⩾ d + 1, any family F of compact convex sets in R d that satisfies the ( p, q )-property can be pierced by a finite number c ( p, q, d ) of points. A similar theorem with respect to piercing by ( d − 1)-dimensional flats, called ( d − 1)-transversals, was obtained by Alon and Kalai. In this paper we prove the following result, which can be viewed as an ( ℵ 0 , k + 2)-theorem with respect to k -transversals: Let F be an infinite family of sets in R d such that each A ∈ F contains a ball of radius r and is contained in a ball of radius R , and let 0 ⩽ k < d . If among every ℵ 0 elements of F , some k + 2 can be pierced by a k -dimensional flat, then F can be pierced by a finite number of k -dimensional flats. This is the first ( p, q )-theorem in which the assumption is weakened to an ( ∞ , · ) assumption. Our proofs combine geometric and topological tools.
Pattern of frustration formation in the functional brain network

The brain is a frustrated system that contains conflictual link arrangements named frustration. The frustration as a source of disorder prevents the system from settling into low energy states and provides flexibility for brain network organization. In this research, we tried to identify the pattern of frustration formation in the brain at the levels of region, connection, canonical network, and hemisphere. We found that frustration formation has not a uniform pattern. Some subcortical elements have an active role in frustration formation, despite many low contributed cortical elements. Frustrating connections are mostly between-network types and triadic frustrations are mainly formed between three regions from three distinct canonical networks. Although there were no significant differences between brain hemispheres. We also did not find any robust differences between the frustration formation patterns of various lifespan stages. Our results may be interesting for those who study the organization of brain links and promising for those who want to manipulate brain networks.