Miracle-dz/UniFAR-FTUs huggingface dataset:
Title: Discovery of Anti-claudin-1 Antibodies as Candidate Therapeutics against Hepatitis C Virus A) a Running Title: a Claudin-1-targeted Hta

Affiliations: Laboratory of Bio-Functional Molecular Chemistry, Graduate School of Pharmaceutical Sciences, Osaka University, Osaka 565-0871, Japan (M.Y., M.I., S.N., A.W., K.K., M.K.) Division of Biological Chemistry and Biologicals, National Institute of Health Sciences, Tokyo 158-0098, Japan (M.T., A.I.) Department of Biochemistry and Cell Biology, National Institute of Infectious Diseases, Tokyo 162-8640, Japan (Y.S., M.F.) Graduate School of Biological Science, Tokyo University of Science, Chiba 278-0022, Japan (Y.S.) This article has not been copyedited and formatted. The final version may differ from this version. JPET Fast Forward. Published on January 27, 2015 as DOI: 10.1124/jpet.114. 217653
Contrasting correlation patterns between environmental factors and chlorophyll levels in the global ocean

In this study we analyze large‐scale satellite‐derived data using generalized additive models to characterize the global correlation patterns between environmental forcing and marine phytoplankton biomass. We found systematic differences in the relationships between key environmental drivers (temperature, light, and wind) and ocean chlorophyll in the subtropical/tropical and temperate oceans. For the subtropical/tropical and equatorial oceans, the chlorophyll generally declined with increasing temperature and light, while in temperate oceans, chlorophyll was best explained by bell‐shaped or positive functions of temperature and light. The relationship between chlorophyll and wind speed is generally positive in low‐latitude oceans and bell shaped in temperate oceans. Our analyses also demonstrated strong and geographically consistent positive autoregressive effects of chlorophyll from 1 month to the next and negative autoregressive effects for measurements 2 months apart. These findings imply possibly different regional phytoplankton responses to environmental forcing, suggesting that future environmental change could affect the tropical and temperate upper ocean chlorophyll levels differently.
Mineral and chemical composition of the new iron meteorite Javorje from Slovenia

Abstract– The single‐piece iron meteorite Javorje, with a mass of 4920 g, is the heaviest and largest meteorite found in the territory of Slovenia. The meteorite Javorje is a medium octahedrite with kamacite bandwidth of 0.85 ± 0.26 mm. The bulk composition of Ni (7.83 wt%), Co (0.48 wt%) and trace elements Ga (25 μg/g), Ge (47 μg/g), Ir (7.6 μg/g), As (5.8 μg/g), Au (0.47 μg/g), and Pt (13.4 μg/g) indicates that the meteorite Javorje belongs to the chemical group IIIAB. Mineral and bulk chemical compositions are consistent with other reported group IIIAB meteorites. The presence of numerous rhabdites, carlsbergite, sparse troilite, and chromite and abundance of daubréelites are in accordance with low‐Ni and low‐P IIIAB iron meteorites. The severely weathered surface and secondary weathering products in the interior of the meteorite suggest its high terrestrial age.
Fast Full-Chip Parametric Thermal Analysis Based on Enhanced Physics Enforced Neural Networks

In this work, we propose a fast full-chip thermal numerical analysis approach based on an enhanced physics-informed neural networks (PINN) framework. The new method, called ThermPINN, leverages both PINN-based DNN optimization framework and analytic solutions of simplified thermal problems for solving thermal partial differential equations (PDE). The resulting ThermPINN leads to more efficient training speed of DNN networks and more scalability for solving large PDE problems. Specifically, we propose to partially enforce physics laws based on closely related analytic solutions to simpler problems. As a result, we are able to significantly reduce the number of variables in the loss function and easily meet boundary conditions. To consider the impact of various ambient temperatures and effective convection coefficients, which are influenced by different design parameters and run-time conditions, we develop a parameterized thermal analysis technique. This technique enables design space exploration and uncertainty quantification (UQ), which are critical for ensuring the reliability of integrated circuits under various operating conditions. The numerical results on alpha21264 processor show that the proposed ThermPINN has 2× speedup and 3× better accuracy over the state-of-the-art thermal simulator, VarSim. The experimental results for 2-D full-chip thermal analysis of 3171 cases show that the proposed parameterized ThermPINN considering both training and inference time can achieve a 6× speedup over commercial COMSOL with an average mean absolute error (AE) of 0.47 K. In terms of training time, the proposed parameterized ThermPINN is 11× faster than the parameterized plain PINN with similar accuracy. The UQ analysis with 5000 samples for maximum temperature propagated from ambient temperature shows that the parameterized ThermPINN and parameterized plain PINN are 113× and 22× faster than COMSOL, respectively.
A Robust Surveillance System for AGV Using Forward-Downward Visual Measurements

Automatic Guided Vehicle (AGV) has been widely used in the warehouse for transporting the bulky and heavy cargos. However, the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the server due to, e.g., cyber attacks, unexpected blocks of the wireless signals. In order to ensure AGV running safely, this paper presents a visual surveillance system by making full use of the measurements from the forward and downward cameras. Specifically, the forward camera estimates the AGV positions and attitudes by tracking the surrounding landmarks detected from the forward image sequences. Besides, the downward camera is used to detect the QR codes fixed on the floor and estimate the AGV poses in the absolute reference frame. Apart from that, the AGV poses from the downward camera could correct scale and poses estimated the forward view. The proposed method has been extensively performed on the developed platform. The results proves the effectiveness in using the complementary forward-downward visual measurements for AGV security surveillance.
An Interval Multi-objective Particle Swarm Optimization Algorithm with Niching Technology for Multimodal Problems

This paper proposes an interval multi-objective niching particle swarm optimization (IMNPSO) algorithm for solving interval multimodal multi-objective optimization problems (IMMOPs). Firstly, in order to locate and maintain multiple optimal solutions, an interval niching PSO (INPSO) algorithm using a ring topology is introduced, and a dual archive mechanism is used to store personal and neighborhood best positions of all particles. Then, a fast non-dominated sorting algorithm based on interval analysis and the interval special crowding distance (ISCD) calculation method are presented, which are combined to form the sorting mechanism of interval particles in IMNPSO. Finally, several multi-objective test functions are selected, and the results demonstrate the effectiveness of the proposed algorithm.
An Emergency Collision-Avoidance Controller for Distributed Drive Autonomous Vehicles Based on AFS and DYC

An emergency collision avoidance controller which integrates trajectory planning and tracking based on Model Predictive Control (MPC) is proposed. A collision avoidance safety domain considering vehicle’s space safety and stability safety is established as safety constraints. Moreover, the proposed controller combines Active Front Steering (AFS) and Direct Yaw-moment Control (DYC) to improve yaw response and thereby enhance the vehicle’s performance during collision avoidance. The feasibility and superiority of the proposed controller are verified by simulation experiments and vehicle ground tests. The results that the proposed controller can realize collision avoidance more effectively and safely.
Subspace learning based classification of ADHD patients

Attention deficit hyperactivity disorder (ADHD) can seriously affect children’s development. Effective diagnosis of ADHD is widely concerned, but accurate diagnosis is still a challenge. In this paper, we propose a classification method based on the subspace learning. Specifically, the corresponding control subspace and patient subspace are learned according to subspace metrics of the control group and the patient group, and linear regression constraints are used to learn the most important and distinctive information. In the learned subspace, subjects have greater energy in the corresponding subspace. Compared with several advanced machine learning methods, our method shows significant classification performance, with an accuracy rate of 94.6% in the ADHD-200 database.
A Convex Formulation of Point Process Heartbeat Dynamics using a Gamma Generalized Linear Model

Heartbeat dynamics have been long studied in understanding the cardiovascular and autonomic nervous systems. Traditional methods use windowed time averaging in order to analyze heartbeat data and are unable to capture the fine temporal nature of heartbeat dynamics. In 2005, Barbieri et al., revolutionized the field by introducing a history-dependent Inverse Gaussian (IG) point process model of heartbeat dynamics, which allows for analysis of such data in continuous time. However, one limitation of this approach is that the maximum likelihood estimation problem is non-convex. This thus requires judicious selection of initial conditions for model fitting and leads to longer runtimes. In this paper, we propose a convex formulation of the point process heartbeat dynamics model utilizing a history-dependent Gamma generalized linear model. Using a dataset of human interbeat intervals, we show that this model uniformly outperforms the IG formulation in runtime, and has comparable goodness of fit, as assessed by the Kolmogorov-Smirnov (KS) distance.
Technology Mapping Using Multi-Output Library Cells

Technology mapping transforms a technology-independent representation into a technology-dependent one given a library of cells. Even if technology libraries contain multi-output cells, state-of-the-art techniques fully exploit single-output cells only. Multi-output cells have limited support in logic synthesis and are typically handled as white boxes once identified. This paper presents a scalable method to increase the support of multi-output library cells in technology mapping. Our contributions include 1) an approach to detect multi-output cells, 2) a fast Boolean matching methodology, and 3) a technology mapping algorithm that supports multi-output cells. Unlike previous work, we address the mapping problem over the whole network. This has the advantage of optimizing area and delay without requiring many incremental steps. The experiments show that full adders and half adders are efficiently detected and mapped with an average area improvement of 7.48% when mapping for minimal delay compared to the default mapper in ABC. Moreover, our method improves the area of the synthesis flow in Yosys, which treats multi-output cells as white boxes, by 5 % on average with a limited run time overhead.
Model-free distributed coordination control for dual-arm reconfigurable manipulators with handling task constraints

In this paper, a distributed coordination control of dual-arm reconfigurable manipulators based on neural networks learning is presented for handling tasks. Based on Newton-Euler algorithm, the dynamic models of the manipulators and grasped object are established through kinematic analyze, respectively. According to the load distribution method, the motion-induced force is effectively distributed to each arm of the manipulator, and then the dynamics of the single reconfigurable manipulator is obtained. An improved sliding mode function is designed to reflect the error of the joint trajectory and internal force. The radial basis function neural network (RBFNN) is utilized to learn the uncertain dynamics of reconfigurable manipulator. Then the model-free distributed coordination controller is obtained. The asymptotic stability of dual-arm reconfigurable manipulators is proved through Lyapunov stability theory. Finally, the validity of the model-free distributed coordination controller is verified by simulations.
Climate of Bangladesh: An Analysis of Northwestern and Southwestern Part Using High Resolution Atmosphere-Ocean General Circulation Model (AOGCM)

The past, present and future climatic pattern (temperature and rainfall) of northwestern and southwestern part of Bangladesh was assessed based on the High Resolution Atmospheric-Ocean General Circulation Model (AOGCM) using the present rainfall and temperature data of the Bangladesh Meteorological Department (BMD). Climatology in Bangladesh is derived from 20 km mesh MRI-AGCM (Atmospheric General Circulation Model) calibrated with reference to the observed data for the period of 1979-2006. Then, projections for rainfall and temperature are made for near future (2015-2034) and future (2075-99). Two disaster prone areas (i) northwestern part (Shapahar & Porsha) and (ii) southwestern part (Kalapara & Amtoli) were selected as the study areas. AOGCM model was run for Bangladesh and also for study areas separately. The present mean temperature for Bangladesh was found to rise from the past, rises slightly, but in near future and future the rate of mean temperature rise is projected to be much more than the present rate (increase up to 4.34 °C/100 years), the rate is projected to be 5.39 °C/100 years in case of Shapahar and Porsha a while 4.37 °C/100 years in case of Kalapara and Amtoli. The present, near future and future average rainfall of Bangladesh appeared to fluctuate, but have shown a decreasing trend (decreases up to 1.96 mm/100 years). The mean average rainfall of Shapahar and Porsha presently decreases very slowly (not significant), but in near future and future will decrease slowly (0.66mm/100 years). In case of Kalapara, the average rainfall appears to decrease presently, near future and future will decrease up to 3.62 mm/100 years. The average rainfall of Amtoli appears to decrease @ 1.92mm/100 years but in near future appears to increase slightly and again decrease @ 3.27mm/100years in future. Keywords: Atmosphere-Ocean General Circulation Model (AOGCM); climatology; simulation; temperature; rainfall DOI: http://dx.doi. org/10.3329/agric.v9i1-2.9489 The Agriculturists 2011; 9(1&2): 143-154
Passive Tracking of Gait Biomarkers in Older Adults: Feasibility of an Acoustic Based Approach for Non-Intrusive gait Analysis

Gait parameters have been established as a health biomarker for physical and cognitive health for older adults. Extracting these parameters however requires specialist equipment such as on-body sensors or video-based systems. Identifying low-cost techniques to capture gait parameters can open the opportunity for in-home monitoring of gait related biomarkers. In this study, we demonstrate that gait can be analysed in in-door at-home settings using only the sound of footsteps. To establish a comparative baseline, we use inertial measurement units (IMUs) and video footages as reference systems alongside our proposed acoustics-based approach. Gait parameters, specifically cadence, step time, and stride time, were extracted from audio, video, and IMU data streams recorded from 10 community-dwelling older adults. Bland Altman Analysis was performed to assess the agreement between the parameter values from the three different systems. We found that the gait parameters derived from our acoustics-only system exhibit relative standard errors of 0.71% and 0.58% against IMU and video-based systems, respectively.
Improved Euclidean clustering point cloud segmentation algorithm based on curvature constraint

In response to the problem that the traditional segmentation algorithm is not ideal for segmenting point cloud data in parts with large changes in geometric features and complex shapes, a curvature constraint-based Euclidean clustering algorithm is proposed. This method first calculates the curvature information corresponding to each point according to the spatial coordinate information of the point cloud, then redefines the improved Euclidean distance between the point clouds according to the coordinates and curvature, and finally performs the clustering segmentation according to the comparison relationship between this distance and the set search radius. The experimental results show that this method achieves the fine segmentation of point cloud data at the parts with complex geometric features, and can be applied to point cloud data with different degrees of variation in geometric features by adjusting the curvature constraint parameters, which is of positive significance for target identification, feature description and surface reconstruction.
Quality Aware Sleep Stage Classification over RIP Signals with Persistence Diagrams

Automated sleep stage classification is a valuable tool for analyzing sleep patterns and has numerous applications in wearable healthcare systems. However, the accuracy of sleep stage classification using signals from wearable devices can be affected by data quality issues such as signal interference or packet loss. In this study, we present an algorithm that addresses packet loss in respiratory inductive plethysmography (RIP) signals for sleep stage detection. RIP signals can be conveniently collected using abdominal and thoracic belts. By exploring the rich structural patterns in such signals, we utilize persistence diagrams to uncover macro-structures for sleep stage classification, which is particularly suitable for high data missing rates. Our model achieves a promising performance of 76% accuracy and a 0.54 Cohen’s kappa coefficient for three-stage classification. Additionally, we evaluate the model across different missing data rates and highlight the superior fault tolerance of persistence diagram features compared to other conventional temporal and spectral features.
Design and Technology Co-Optimization for Useful Skew Scheduling on Multi-Bit Flip-Flops

Utilizing multi-bit flip-flops (MBFFs) in circuit implementation offers a considerable saving on the dynamic power dissipated at the clock networks. However, indiscreetly allocating MBFFs by grouping single-bit flip-flops at the logic synthesis or placement stage in order to maximally save dynamic power severely hinders a full applicability of useful clock skew scheduling to the individual flip-flops in MBFFs, failing in effectively optimizing circuit timing. This is because the two internal clock inverters, consequently, the clock skew value, in an MBFF are shared by all of the flip-flops in the MBFF. This work overcomes this inherent limitation of inflexibility in MBFFs for useful clock skew scheduling by proposing a comprehensive DTCO (design and technology co-optimization) framework integrating two viable techniques, called in-place MBFF debanking and skew driven cell layout diversification. Precisely, we proposed a two-step DTCO flow: (1) DTCO based on in-place MBFF debanking technique at the pre-route stage to facilitate the full applicability of useful skew scheduling at the subsequent stages and (2) DTCO utilizing MBFF cell layout diversification technique at the post-route stage, by which useful clock skew scheduling can effectively resolve timing violations. Through experiments with OpenCores benchmark circuits, it is shown that our proposed DTCO flow of reinforcing the effectiveness of useful clock skew scheduling on circuits with MBFF instances is able to reduce the worst and total negative slacks by 16.87% and 46.26% at the cost of 1.00% power overhead over that produced by the state-of-the-art conventional flow.
Accurate Hybrid Delay Models for Dynamic Timing Analysis

To facilitate the analysis of timing relations between individual transitions in a signal trace, dynamic digital timing analysis offers a less accurate but much faster alternative to analog simulations of digital circuits. It primarily requires gate delay models, which account for the fact that the input-to-output delay of a particular input transition also depends on the temporal distance to the previous output transitions. In the case of multi-input gates, this delay also experiences variations caused by multi-input switching (MIS) effects, i.e., transitions at different inputs that occur in close temporal proximity. In this paper, we advocate the development of hybrid delay models for CMOS gates obtained by replacing transistors with time-variant resistors. We exemplify our approach by applying it to a NOR gate (and, hence, to the dual NAND gate) and a Muller C gate. We analytically solve the resulting first-order differential equations with non-constant coefficients and derive analytic expressions for the resulting MIS gate delays. The resulting formulas not only pave the way to a sound model parametrization procedure but are also instrumental in implementing a fast and efficient digital timing simulation. By comparison with analog simulation data, we show that our models faithfully represent all relevant MIS effects. Using an implementation in the Involution Tool, we also demonstrate that our models surpass all alternative digital delay models known to us in terms of accuracy, with comparably short running times.
Lightweight object-level monocular data association system

Mobile robots can generate point cloud maps based on continuous image sequences to realize the perception of the environment. However, in the lightweight application scenario, the sparse point cloud map generated based on monocular camera is difficult to carry out accurate object data association due to the lack of sufficient object information. Therefore, this paper proposes a lightweight object-level data association method based on the monocular camera to realize the robot’s more accurate perception and positioning of environmental objects. In this method, feature points of monocular images are tracked, semantic information is introduced to parallel data association, and map points are distinguished in three stages, and a mutually exclusive object table is proposed to improve the accuracy of data association. The method is extensively tested on several public datasets and a real robot. The results show that data association is superior to similar methods in terms of accuracy and computation cost. In addition, this paper implements a lightweight robot system, which can achieve a frame rate of 20 FPS when using only half of the system’s computing resources, achieving lightweight real-time performance.
State-of-health Monitoring of Li-ion Battery Driven by Convolutional Neural Network and Attention Mechanism

Aiming at the scarcity of lithium battery data under abnormal working conditions, we introduce a TCNN-Trans-TG hybrid model. This algorithm combines 1D temporal convolutional neural network(TCNN) with Transformer encoder. The TCNN-Trans is the base model of TCNN-Trans-TG. The sparse dataset on abnormal operating conditions is enlarged by data augmentation based on the TimeGAN neural network. This method can compensate for the lack of prediction accuracy caused by data imbalance.Compared with other networks, the model can predict data under abnormal conditions more accurately. Finally, The experiments performed on NASA datasets achieve average root mean square error(ARMSE) 0.0070, and achieve ARMSE 0.0032 with data augmentation.
A Modification of ORB-SLAM2 for Multi-Robot SLAM

ORB-SLAM2 consumes much time on descriptors calculating, which is a great challenge for real-time performance of SLAM. In this paper, we proposed a modified ORB-SLAM2 method named SD-SLAM2, which achieved better real-time performance than ORB-SLAM2, by replacing the extraction and description of ORB features with sparse direct method. Based on SD-SLAM2, we implemented centralized collaborative SLAM for multi-robot. Experimental results on popular public datasets demonstrate the performance of SD-SLAM2 in monomer SLAM. We also conducted field experiments for multi-robot, whose results demonstrate the performance of SD-SLAM2 in multi-robot SLAM.