Miracle-dz/UniFAR-FTUs huggingface dataset:
A Localized Learning Approach Applied to Human Activity Recognition

The recognition of human physical activities and postures based on sensor data has received much research attention in several human health and biomedical engineering applications. In this article, the challenges of class imbalance and ambiguity (or confusion) are discussed that frequently arise in data from human activity recognition (HAR) systems. In order to reduce the influence of imbalance and ambiguity in HAR problems, a novel hybrid localized learning approach of K-nearest neighbors least-squares support vector machine is proposed. The classifier is applied to different synthetic and real-world datasets where imbalance and ambiguity are present. In this article, it is novel to apply a hybrid localized learning algorithm to the HAR problem. When compared to different global and local approaches, higher classification performances could be obtained by using the proposed localized learning approach. Furthermore, the computational effort could be reduced in an online learning mode.
Assessment of occupational exposure in non-medical facilities of Bangladesh

The concern about occupational exposure is being increased by the worker and regulatory body day by day in Bangladesh. After establishment of Bangladesh Atomic Energy Regulatory Authority (BAERA) this type of study has been carried out extensively for creating database about the safety of occupational worker in different facilities. The present research work has been performed on major non-medical radiological facilities including non-destructive testing (NDT), nucleonic gauge and irradiation facilities. Among these facilities, the workers of Engineer Inspection Services, Bangladesh (EISB) received high radiation doses. The outcome of the present study also shows that the radiation workers of NDT facilities are exposed to more radiation than any other facilities. This is because they used to work with high activity radiation source at offsite area. Every NDT facility should be more equipped with radiation shielding and personal protective equipment. These data can be utilized by BAERA in future for further strengthening of radiation safety infrastructure for the concerned workers.
Positive and negative mood states do not influence cross-modal auditory distraction in the serial-recall paradigm

The aim of this study was to examine whether positive and negative mood states affect auditory distraction in a serial-recall task. The duplex-mechanism account differentiates two types of auditory distraction. The changing-state effect is postulated to be rooted in interference-by-process and to be automatic. The auditory-deviant effect is attributed to attentional capture by the deviant distractors. Only the auditory-deviant effect, but not the changing-state effect, should be influenced by emotional mood states according to the duplex-mechanism account. Four experiments were conducted to test how auditory distraction is affected by emotional mood states. Mood was induced by autobiographical recall (Experiments 1 and 2) or the presentation of emotional pictures (Experiments 3 and 4). Even though the manipulations were successful in inducing changes in mood, neither positive mood (Experiments 1 and 3) nor negative mood (Experiments 2 and 4) had any effect on distraction despite large samples sizes (N = 851 in total). The results thus are not in line with the hypothesis that auditory distraction is affected by changes in mood state. The results support an automatic-capture account according to which the auditory-deviant effect and the changing-state effect are mainly stimulus-driven effects that are rooted in the automatic processing of the to-be-ignored auditory stream.
Assessing Ischemic Injury in Human Intestine Ex Vivo with Electrical Impedance Spectroscopy

Abstract Electrical impedance spectroscopy is a well-established tool for monitoring changes in the electrical properties of tissue. Most tissue and organ types have been investigated in various studies. As for the small intestine, there are several published studies conducted on pig and rat models. This study investigates the changes in passive electrical properties of the complete wall of the human intestine non-invasively during ischemia. We aim to use the passive electrical properties to assess intestinal viability. The bioimpedance measurements were performed using a two-electrode set-up with a Solartron 1260 Impedance/gain-phase analyser. The small intestinal samples were resected from patients who underwent pancreaticoduodenectomy. Impedance measurements were conducted following resection by placing the electrodes on the surface of the intestine. A voltage was applied across the intestinal sample and the measured electrical impedance was obtained in the ZPlot software. Impedance data were further fitted into a Cole model to obtain the Cole parameters. The Py value was calculated from the extracted Cole parameters and used to assess the cell membrane integrity, thus evaluate the intestinal viability. Eight small intestinal segments from different patients were used in this study and impedance measurements were performed once an hour for a ten-hour period. One hour after resection, the impedance decreased, then increased the next two hours, before decreasing until the end of the experiment. For all the intestinal segments, the Py values first increased and reached a plateau which lasted for 1 - 2 hours, before it decreased irreversibly. The time interval where Py value reached the maximum is consistent with reported viable/non-viable limits from histological analysis.
Fish muscle hydrolysate obtained using largemouth bass Micropterus salmoides digestive enzymes improves largemouth bass performance in its larval stages

The present study utilized digestives tracts from adult largemouth bass (LMB) to hydrolyze Bighead carp muscle and obtain an optimal profile of muscle protein hydrolysates that would be easily assimilated within the primitive digestive tract of larval LMB. Specifically, muscle protein source was digested for the larva using the fully developed digestive system of the same species. The objectives of this study were: 1) to develop an optimal in vitro methodology for carp muscle hydrolysis using LMB endogenous digestive enzymes, and 2) to evaluate the effect of dietary inclusion of the carp muscle protein hydrolysate on LMB growth, survival, occurrence of skeletal deformities, and whole-body free amino acid composition. The study found that the in vitro hydrolysis method using carp intact muscle and LMB digestive tracts incubated at both acid and alkaline pH (to mimic digestive process of LMB) yielded a wide range of low molecular weight fractions (peptides), as opposed to the non-hydrolyzed muscle protein or muscle treated only with acid pH or alkaline pH without enzymes from LMB digestive tracts, which were comprised of large molecular weight fractions (polypeptides above 150 kDa). Overall, the dietary inclusion of the carp muscle hydrolysate improved growth performance of larval LMB in terms of final average weight, weight gain, DGC, SGR, and body length after 21 days of feeding compared to fish that received the diet based on non-hydrolyzed carp muscle. The study also found that hydrolysate-based feed significantly reduced skeletal deformities. The positive growth performance presented by fish in the hydrolysate-fed group possibly resulted from matching the specific requirements of the larvae with respect to their digestive organ development, levels of digestive enzymes present in the gut, and nutritional requirements.
Nested Miller compensation in low-power CMOS design

First, new stability conditions for low-power CMOS nested Miller compensated amplifiers are given in this brief. Then, an improved structure, which takes the advantages of a feedforward transconductance stage and a nulling resistor, is introduced. Experimental results prove that the proposed structure improves the frequency response, transient response, and power supply rejection ratio without increasing the power consumption and circuit complexity.
ELK1 Promotes Epithelial-Mesenchymal Transition and the Progression of Lung Adenocarcinoma by Upregulating B7-H3

In previous studies, we found that B7 homolog 3 (B7-H3) was highly expressed in lung adenocarcinoma (LUAD) and promoted epithelial-to-mesenchymal transition (EMT) of LUAD cells. However, the underlying molecular mechanism is unclear. This study is aimed at evaluating the role of Ets-like protein 1 (ELK1) as a transcriptional regulator of B7-H3 for mediating the development and progression of LUAD in vitro and in vivo. We confirmed that ELK1 is highly expressed in LUAD and is associated with poor patient prognosis. ELK1 was found to promote proliferation, invasion, migration, and EMT of LUAD cells through in vivo and in vitro experiments. In terms of mechanism, ELK1 binds to the B7-H3 promoter region and induces the upregulation of B7-H3 in LUAD. Our data suggest that ELK1 plays an important role in the development of LUAD and could be used as a prognostic marker and therapeutic target for LUAD.
Optimal dynamic inversion‐based semi‐active control of benchmark bridge using MR dampers

A new two‐stage state feedback control design approach has been developed to monitor the voltage supplied to magnetorheological (MR) dampers for semi‐active vibration control of the benchmark highway bridge. The first stage contains a primary controller, which provides the force required to obtain a desired closed‐loop response of the system. In the second stage, an optimal dynamic inversion (ODI) approach has been developed to obtain the amount of voltage to be supplied to each of the MR dampers such that it provides the required force prescribed by the primary controller. ODI is formulated by optimization with dynamic inversion, such that an optimal voltage is supplied to each damper in a set. The proposed control design has been simulated for both phase‐I and phase‐II study of the recently developed benchmark highway bridge problem. The efficiency of the proposed controller is analyzed in terms of the performance indices defined in the benchmark problem definition. Simulation results demonstrate that the proposed approach generally reduces peak response quantities over those obtained from the sample semi‐active controller, although some response quantities have been seen to be increasing. Overall, the proposed control approach is quite competitive as compared with the sample semi‐active control approach. Copyright © 2009 John Wiley & Sons, Ltd.
Two-dimensional paired topological superfluids of Rydberg Fermi gases

Ching-Yu Huang,1 Jiapei Zhuang,2 Po-Yao Chang,2 and Daw-Wei Wang3, 4, 5, 6 Department of Applied Physics, Tunghai University, Taichung 40704,Taiwan Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan Physics Division, National Center for Theoretical Sciences, Taipei 10617, Taiwan Frontier Center for Theory and Computation, National Tsing Hua University, Hsinchu 30013, Taiwan Center for Quantum Technology, National Tsing Hua University, Hsinchu 30013, Taiwan (Dated: December 30, 2021)
Generic Equidistribution of Periodic Orbits for Area-Preserving Surface Maps

We prove that a $C^{\infty }$-generic area-preserving diffeomorphism of a closed, oriented surface admits a sequence of equidistributed periodic orbits. This is a quantitative refinement of the recently established generic density theorem for area-preserving surface diffeomorphisms. The proof has two ingredients. The first is a “Weyl law” for PFH spectral invariants, which was used to prove the generic density theorem. The second is a variational argument inspired by the work of Marques–Neves–Song and Irie on equidistribution results for minimal hypersurfaces and three-dimensional Reeb flows, respectively.
Rényi entropies of the massless Dirac field on the torus

We compute the Rényi entropies of the massless Dirac field on the Euclidean torus (the Lorentzian cylinder at non-zero temperature) for arbitrary spatial regions. We do it by the resolvent method, i.e., we express the entropies in terms of the resolvent of a certain operator and then use the explicit form of that resolvent, which was obtained recently. Our results are different in appearance from those already existing in the literature (obtained via the replica trick), but they agree perfectly, as we show numerically for non-integer order and analytically for integer order. We also compute the Rényi mutual information, and find that, for appropriate choices of the parameters, it is non-positive and non-monotonic. This behavior is expected, but it cannot be seen with the simplest known Rényi entropies in quantum field theory because they are proportional to the entanglement entropy.
DP-FP: Differentially Private Forward Propagation for Large Models

When applied to large-scale learning problems, the conventional wisdom on privacy-preserving deep learning, known as Differential Private Stochastic Gradient Descent (DP-SGD), has met with limited success due to significant performance degradation and high memory overhead when compared to the non-privacy counterpart. We show how to mitigate the performance drop by replacing the DP-SGD with a novel DP Forward-Propagation (DP-FP) followed by an off-the-shelf non-DP optimizer. Our DP-FP employs novel (1) representation clipping followed by noise addition in the forward propagation stage, as well as (2) micro-batch construction via subsampling to achieve DP amplification and reduce noise power to $1/M$, where $M$ is the number of micro-batch in a step. When training a classification model, our DP-FP with all of the privacy-preserving operations on the representation is innately free of gradient bias, total noise proportionally to model size, and memory issues in DP-SGD. As a result, our DP-FP outperforms cutting-edge DP-SGD while retaining the same level of privacy, and it approaches non-private baselines and significantly outperforms state-of-the-art DP-SGD variants. When applied to RoBERTa-large on four downstream tasks, for example, DP-FP achieves an average accuracy of 91.34\% with privacy budgets less than 3, representing a 3.81\% performance improvement over the state-of-the-art DP-SGD and only a 0.9\% loss compared to the non-private baseline but with a significantly lower privacy leakage risk.
Robotic Perception of Object Properties using Tactile Sensing

The sense of touch plays a key role in enabling humans to understand and interact with surrounding environments. For robots, tactile sensing is also irreplaceable. While interacting with objects, tactile sensing provides useful information for the robot to understand the object, such as distributed pressure, temperature, vibrations and texture. During robot grasping, vision is often occluded by its end-effectors, whereas tactile sensing can measure areas that are not accessible by vision. In the past decades, a number of tactile sensors have been developed for robots and used for different robotic tasks. In this chapter, we focus on the use of tactile sensing for robotic grasping and investigate the recent trends in tactile perception of object properties. We first discuss works on tactile perception of three important object properties in grasping, i.e., shape, pose and material properties. We then review the recent development in grasping stability prediction with tactile sensing. Among these works, we identify the requirement for coordinating vision and tactile sensing in the robotic grasping. To demonstrate the use of tactile sensing to improve the visual perception, our recent development of vision-guided tactile perception for crack reconstruction is presented. In the proposed framework, the large receptive field of camera vision is first leveraged to achieve a quick search of candidate regions containing cracks, a high-resolution optical tactile sensor is then used to examine these candidate regions and reconstruct a refined crack shape. The experiments show that our proposed method can achieve a significant reduction of mean distance error from 0.82 mm to 0.24 mm for crack reconstruction. Finally, we conclude this chapter with a discussion of open issues and future directions for applying tactile sensing in robotic tasks.
Overcoming Mode Collapse with Adaptive Multi Adversarial Training

Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator. Investigative study using a new data generation procedure indicates that the mode collapse of the generator is driven by the discriminator's inability to maintain classification accuracy on previously seen samples, a phenomenon called Catastrophic Forgetting in continual learning. Motivated by this observation, we introduce a novel training procedure that adaptively spawns additional discriminators to remember previous modes of generation. On several datasets, we show that our training scheme can be plugged-in to existing GAN frameworks to mitigate mode collapse and improve standard metrics for GAN evaluation.
Energy dependence of the chiral magnetic effect in expanding holographic plasma

Based on a holographic far-from-equilibrium calculation of the Chiral Magnetic Effect (CME) in an expanding quark gluon plasma (QGP), we study collisions at various energies. We compute the time evolution of the CME current in the presence of a time-dependent axial charge density and subject to a time-dependent magnetic field. The plasma expansion leads to a dilution of the CME current. We study distinct combinations of how the initial magnetic field and initial axial charge behave with changing initial energy as proposed in previous literature. Most scenarios we consider lead to an increasing time-integrated CME current, when increasing the initial energy. This would make it more likely to observe the CME at higher collision energies. hydrodynamic
Automatic Morphological Classification of Galaxies: Convolutional Autoencoder and Bagging-based Multiclustering Model

In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of convolutional autoencoder (CAE) is used to reduce the dimensions and extract features from the imaging data; (2) the bagging-based multiclustering model is proposed to obtain the classifications with high confidence at the cost of rejecting the disputed sources that are inconsistently voted. We apply this method on the sample of galaxies with H < 24.5 in CANDELS. Galaxies are clustered into 100 groups, each contains galaxies with analogous characteristics. To explore the robustness of the morphological classifications, we merge 100 groups into five categories by visual verification, including spheroid, early-type disk, late-type disk, irregular, and unclassifiable. After eliminating the unclassifiable category and the sources with inconsistent voting, the purity of the remaining four subclasses are significantly improved. Massive galaxies (M * > 1010 M ⊙) are selected to investigate the connection with other physical properties. The classification scheme separates galaxies well in the U − V and V − J color space and Gini–M 20 space. The gradual tendency of Sérsic indexes and effective radii is shown from the spheroid subclass to the irregular subclass. It suggests that the combination of CAE and multiclustering strategy is an effective method to cluster galaxies with similar features and can yield high-quality morphological classifications. Our study demonstrates the feasibility of UML in morphological analysis that would develop and serve the future observations made with China Space Station telescope.
Depth and Feature Learning are Provably Beneficial for Neural Network Discriminators

We construct pairs of distributions $\mu_d, \nu_d$ on $\mathbb{R}^d$ such that the quantity $|\mathbb{E}_{x \sim \mu_d} [F(x)] - \mathbb{E}_{x \sim \nu_d} [F(x)]|$ decreases as $\Omega(1/d^2)$ for some three-layer ReLU network $F$ with polynomial width and weights, while declining exponentially in $d$ if $F$ is any two-layer network with polynomial weights. This shows that deep GAN discriminators are able to distinguish distributions that shallow discriminators cannot. Analogously, we build pairs of distributions $\mu_d, \nu_d$ on $\mathbb{R}^d$ such that $|\mathbb{E}_{x \sim \mu_d} [F(x)] - \mathbb{E}_{x \sim \nu_d} [F(x)]|$ decreases as $\Omega(1/(d\log d))$ for two-layer ReLU networks with polynomial weights, while declining exponentially for bounded-norm functions in the associated RKHS. This confirms that feature learning is beneficial for discriminators. Our bounds are based on Fourier transforms.
Pricing and Hedging of SOFR Derivatives under Differential Funding Costs and Collateralization

Since the 1970s, the LIBOR has served as a fundamental measure for floating term rates across multiple currencies and maturities. Loans and many derivative securities, including swaps, caps and swaptions, still rely on LIBOR as the reference forward-looking term rate. However, in 2017 the Financial Conduct Authority announced the discontinuation of LIBOR from the end of 2021 and the New York Fed declared the backward-looking SOFR as a candidate for a new reference rate for interest rate swaps denominated in U.S. dollars. We first outline the classical single-curve modelling framework before transitioning to the multi-curve framework where we examine arbitrage-free pricing and hedging of SOFR-linked swaps without and with collateral backing. As hedging instruments, we take liquidly traded SOFR futures and either common or idiosyncratic funding rates for the hedge and margin account. For concreteness, a one-factor model based on Vasicek’s equation is used to specify the joint dynamics of several overnight interest rates, including the SOFR, EFFR, and unsecured funding rate, although multi-factor term structure models could also be employed.
Coherent Atom Transport via Enhanced Shortcuts to Adiabaticity: Double-Well Optical Lattice

Theoretical studies of coherent atom transport have as yet mainly been restricted to one-dimensional model systems with harmonic trapping potentials. Here we investigate this important phenomenon -- a prerequisite for a variety of quantum-technology applications based on cold neutral atoms -- under much more complex physical circumstances. More specificially yet, we study fast atomic transport in a moving {\em double-well optical lattice}, whose three-dimensional (anharmonic) potential is nonseparable in the $x-y$ plane. We first propose specific configurations of acousto-optic modulators that give rise to the moving-lattice effect in an arbitrary direction in this plane. We then determine moving-lattice trajectories that enable single-atom transport using two classes of quantum-control methods: shortcuts to adiabaticity (STA), here utilized in the form of inverse engineering based on a quadratic-in-momentum dynamical invariant of Lewis-Riesenfeld type, and their recently proposed modification termed enhanced STA (eSTA). Subsequently, we quantify the resulting single-atom dynamics by numerically solving the relevant time-dependent Schr\"{o}dinger equations and compare the efficiency of STA- and eSTA-based transport by evaluating the respective fidelities. We show that -- except for the regime of shallow lattices -- the eSTA method consistently outperforms its STA counterpart. This study has direct implications for neutral-atom quantum computing based on collisional entangling two-qubit gates and quantum sensing of constant homogeneous forces via guided-atom interferometry.