Miracle-dz/UniFAR-FTUs huggingface dataset:
Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer

Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage of the developed systems in real-world applications. This paper presents a writer-independent system that recognizes characters written on plain paper with the use of a sensor-equipped pen. This system is applicable in real-world applications and requires no user-specific training for recognition. The pen provides linear acceleration, angular velocity, magnetic field, and force applied by the user, and acts as a digitizer that transforms the analogue signals of the sensors into timeseries data while writing on regular paper. The dataset we collected with this pen consists of Latin lower-case and upper-case alphabets. We present the results of a convolutional neural network model for letter classification and show that this approach is practical and achieves promising results for writer-independent character recognition. This work aims at providing a realtime handwriting recognition system to be used for writing on normal paper.
A Kernel Module Based Mechanism to Establish a TCP/IP Connection When Destination Service Changing

In this paper, we would like to change the process of establishing a new connection so that we can transfer a client to another proxy then to get connection from a new destination. Therefore, we need to modify the system call sys_connect, which is in charge of establishing a new connection. Considering that reinstalling operating systems on all clients is impractical, we chose to replace the system call table entry with a customized sys_connect by a Loadable Kernel Module (LKM).
Spatial-Temporal Graph Attention Model on Traffic Forecasting

The core of the traffic prediction problem lies in solving the problem of spatial-temporal sequence model, mainly from the aspects of temporal dependence and spatial dependence. The existing methods spent mostly in the spatial level using the method of graph convolution. But with the increase of graph convolution layers, once a certain threshold is reached, the diversity of the local network structure of each node will be greatly reduced. In term of temporal level, RNN and its deformation are the common methods, but they have gradient disappearance problem. So based on these two points in spatial and temporal respectively, we can add the attention mechanism to the neighbor nodes in order to achieve the adaptive allocation of different neighbor weights. It can greatly improve the expression ability of the graph neural network model. And in the temporal level, flexible control of the perception field can solve the above temporal dependence problems by using less layers. Experiments on two real-world datasets from the METR-LA and PEMS-BAY demonstrate that the proposed model outperforms better than other baselines.
Electromigration Checking Using a Stochastic Effective Current Model

Electromigration (EM) degradation evolves slowly towards failure, over a period of years. This is why EM checking methods use effective current models to represent the underlying circuit workload, which are typically constant (DC) currents over time. However, ignoring all input current variations around the mean can be risky, because low-frequency input variations can have a significant impact on EM, resulting in shorter than expected lifetimes. With the use of dark silicon and multimodal chip operation, such low-frequency changes in workload are becoming increasingly common in modern designs. Ignoring these variations can lead to false positives and must be avoided. We tackle this by developing a stochastic effective current model for the input current waveforms that is easy for users to specify and which allows stochastic estimation of the impact of input variability on the lifetime. User-provided guidance on the expected durations of various modes of operation is used to provide input current variances, which are then propagated to provide variances around the stress waveforms in the metal network, which gives a more realistic estimate of the EM lifetime. Variance propagation can be expensive for large systems, but a novel simulation-like framework will be presented that allows efficient variance propagation for large interconnect trees. This has revealed that the variance can be highly significant. Even when the standard deviation of the inputs is small, at around 20–30% of the mean, we see a 30–40% drop in the lifetimes.
Preventive power rescheduling for electric frequency enhancement in integrated energy systems

Under the occurrence of a critical disturbance that produces a large load-generation imbalance in an electric power system, the primary regulation control may not be sufficient to bound the steady-state error of the frequency deviation within its normal operating range. This also has an effect on the steady-state operational interdependency of multi-energy systems. To overcome this lack of controllability, this paper proposes a new, practical preventive control action for assessing the steady-state interdependency between gas and electricity infrastructures with a feasible control of the frequency deviation. The proposed approach consists of the solution of two mutually connected subproblems: one related to the gas and power flow problem and the other associated with the economic dispatch problem. These two problems are sequentially solved until a feasible equilibrium point is obtained in which the multi-energy system operates with a value of the electrical frequency within limits. The effectiveness of the proposed control action and the validity of the theoretical concepts on which the proposed approach is based are demonstrated numerically in an integrated multi-energy system consisting of a 20-node gas network and 3-bus electrical system.
Strong Read and Write Interference Induced by Breakdown Failure in Crossbar Arrays

Cell-to-cell interference due to the sneak current is a known and important issue in high-density 4F2 crossbar arrays. However, the interference between normal cells and high-leakage breakdown (BD) cells is rarely discussed. We show that such interference degrades the array read and write yields dramatically based on experiments and simulation. A rule-based analytical model for BD interference is developed by leveraging the quantitative understanding of the different interference modes and their equivalent circuits. This model achieves equivalent accuracy but is at least 100 times faster than the SPICE circuit simulator. It is used in the Monte Carlo simulation for providing sensible statistics on the spatial dependence of interference. The results show that merely 3% of BD cells result in zero array yield for a crossbar array size larger than $128\times128$ . The minimal requirement of device yield (non-BD percentage) increases with the array size in order to maintain a reasonable array yield. Thus, BD interference should be recognized as one of the most critical concerns for developing future crossbar arrays.
Use of Alternative Fluids in Very High-Power Transformers: Experimental and Numerical Thermal Studies

The very high-power transformer lifespan depends mainly on the temperature that cellulose insulation reaches during its operation. Traditionally, its cooling has been carried out using mineral oil as coolant. Nowadays, alternative ester-based liquids are under study as substitutes due to their better environmental and fire-safety properties. This paper compares the cooling capacity of two ester-based fluids with that of a mineral oil using a 3D numerical model of a 100 MVA low voltage winding of a power transformer with axial cooling system and ONAN cooling mode. Heat-run test results with mineral oil have been used to validate this model. As a first approximation, according to the comparison developed, ester-based fluids could replace mineral oils in this type of transformers when they are going to work in a range of powers close to the rated one.
Fuzzy Logic based Adaptive Step Hill Climbing MPPT Algorithm for PV Energy Generation Systems

This paper presents a modified Hill Climbing (HC) based Maximum Power Point Tracking MPPT algorithm with adaptive duty cycle step using Fuzzy Logic Controller (FLC). The conventional HC MPPT algorithm is usually used in numerous applications because of its simplicity; however, it is highly affected by sudden changes in climatic conditions, and it is characterized by ripple around the Maximum Power Point MPP and slow rate of convergence. In order to overcome these issues, a modified Hill Climbing MPPT method using FLC is presented. The robustness of this method has been demonstrated using a boost DC-DC converter connected to a Kyocera kc 200st model.
Fault Tolerant Control Strategy Using Two-Layer Multiple Adaptive Models for Plant Fault

Fault tolerant control (FTC) is always a popular research direction in the domain of automatic control. Inspired by the concept of adaptive model and corresponding approaches in [1], this paper proposed an FTC design strategy for plant fault by introducing these adaptive models into the two-layer multiple model structure. The two-layer multiple model structure describes a hyper-system which considers the nominal and faulty situations of a complex system. A group of local models are selected to present the system in its full range of operation and this is the first layer multiple model. At the second layer, we create a group of model bank to describe the system in nominal and each faulty situations. By checking the validity of the second layer model banks, information of corresponding local models are used to initialize the adaptive models to have a precise approaching to the real system. Besides, model predictive control (MPC) is designed for the reference model of the adaptive process to generate proper reference input for achieving control goals while dealing the FTC problem. Simulations are given to show the validity of the proposed method.
Improving Low-Resource Neural Machine Translation With Teacher-Free Knowledge Distillation

Knowledge Distillation (KD) aims to distill the knowledge of a cumbersome teacher model into a lightweight student model. Its success is generally attributed to the privileged information on similarities among categories provided by the teacher model, and in this sense, only strong teacher models are deployed to teach weaker students in practice. However, in low-resource neural machine translation, a stronger teacher model is not available. To counteract this, We therefore propose a novel Teacher-free Knowledge Distillation framework for low-resource neural machine translation, where the model learns from manually designed regularization distribution as a virtual teacher model. The prior distribution of artificial design can not only obtain the similarity information between words, but also provide effective regularity for model training. Experimental results show that the proposed method has improved performance in low-resource language effectively.
Extraction of cutting plans in craniosynostosis using convolutional neural networks

The extraction of skull cutting trajectory from existing successful cases of experienced specialists is one of the key steps to standardize the planning of craniosynostosis surgery. This paper proposes a new method for extracting the skull cutting trajectory that combines deep learning, binocular stereo vision, and point cloud processing technology. The proposed method is the first-time application of deep learning to segmentation of the external surface of skull. The method automates the extraction and digitization of the cutting trajectory by utilizing Mask R-CNN to detect and segment the surgical area first, then a simplified contour extraction algorithm is explored to extract the cutting trajectory, followed by 2D-3D mapping of the cutting trajectory coordinates with the aid of binocular stereo vision and point cloud processing. Study showed that the proposed method is able to extract the surgical cutting trajectory accurately and efficiently, with the point cloud depth measurement error less than 3mm and the trajectory point positioning error less than 0.3mm, meeting the standard of clinical application.
Open-Circuit Fault-Tolerant Control of Five-Phase PM Machine Based On Current Space Vectors

In this paper, open-circuit fault-tolerant current control for a five-phase permanent magnet (PM) machine is proposed based on adding an additional current(s) to the current space vectors (SV) of the remaining healthy phases. According to the principle, new current sets are derived by adding an additional current component to each remaining healthy phase to maintain a smooth total torque by obtaining the same resultant rotating current SV. The remaining but modified healthy phase currents are obtained for a single-phase, adjacent double-phase and non-adjacent double-phase open-circuit fault conditions. The proposed fault-tolerant control strategy is validated by finite element analysis (FEA). This new fault-tolerant control method can be applied to any multi-phase PM machine.
Vibrational Study of Dipotassium Zinc Bis(Dihydrogendiphosphate) Dihydrate, K2ZN(H2P2O7)2·2H2O

Abstract Infrared and Raman spectra of dipotassium zinc bis(dihydrogendiphosphate) dihydrate, K2Zn(H2P2O7)2 . 2H2O have been collected and interpreted using factor group analysis. Noncoincidence of the Raman and infrared spectra bands confirms a centrosymmetric structure for K2Zn(H2P2O7)2 . 2H2O previously investigated by X-ray structural study, as well as the joint appearance of νas POP and νs POP point to a bent POP configuration. GRAPHICAL ABSTRACT
Schmitt Trigger Based Single-Ended Voltage Amplifier with Positive Feedback Control for Ultra-Low-Voltage Supplies

This work presents a Schmitt trigger based pseudo-differential single-ended voltage amplifier using a Schmitt trigger inverter cell in parallel with a push-pull inverter cell to eliminate hysteresis and improve voltage gain for ultra-low-voltage supplies. Positive feedback is controlled by changing independently the Schmitt trigger and push-pull cells body input terminal voltages, which enables high voltage gain operation without hysteresis for supply voltages as low as 200 mV and as high as 500 mV. A prototype was fabricated for a 130 nm CMOS process and its DC characteristic curve measurements show a 32 dB DC voltage gain at a 0.2 V supply voltage, and simulations show a 330 kHz and unity-gain-bandwidth product and 90° phase margin for a 10pF capacitive load, and a 450 nW power consumption also at a 200 mV supply voltage.
Ecosystem thresholds as tools to achieve an ecosystem-based approach to fisheries

Introduction Various international agreements require/encourage participating parties to implement an ecosystem-based approach to fisheries (EAF). For example, the Code of Conduct for Responsible Fisheries of the United Nations’ Food and Agriculture Organisation, adopted in 1995, states that Management measures should not only ensure the conservation of target species but also of species belonging to the same ecosystem or associated with or dependent upon the target species (http://www.fao.org/3/v9878e/v9878e00.htm). The sixth Aichi Target of the Convention on Biological Diversity’s Strategic Plan for Biodiversity 2011–2020 reads: By 2020 all fish and invertebrate stocks and aquatic plants are managed and harvested sustainably, legally and applying ecosystem based approaches, so that overfishing is avoided, recovery plans and measures are in place for all depleted species, fisheries have no significant adverse impacts on threatened species and vulnerable ecosystems and the impacts of fisheries on s tocks, species and ecosystems are within safe ecological limits (https://www.cbd.int/convention/text/default.shtml).
Representações gustativas no córtex humano, e o controle central do apetite

The control of food intake and the mechanisms of energy homeostasis are now known to depend on a series of peripheral signals that act directly on the central nervous system leading to appropriate adaptive responses. However, in humans, the increasing occurrence of associated pathologies due to abnormal food-intake preferences such as obesity and anorexia implies that food intake control depend also on cortical processing. Recent functional neuroimaging studies on human volunteers reveal that the central processing of gustatory information in humans is performed in similar areas to those of other primates, with primary gustatory cortical areas in the frontal operculum/anterior insula complex responding efficiently to stimulus decoding by isolating peripheral signals on internal physiological states whereas regions of the ventromedial prefrontal cortex seem to integrate information on the sensory aspects of taste stimuli with the abovementioned peripheral signals on the current homeostatic state of the organism.
Association Between Family Medicine Residents' Mindsets and In-Training Exam Scores.

Introduction In medical practice, a mastery mindset is important for engaging in lifelong learning. The objective of this study was to examine the association between family medicine residents' scores on mindset measures and their performance on in-training examinations (ITE). Methods This was a secondary data analysis of a cohort of family medicine residents. Following ethics approval, residents' ITE scores from each of the 2 years of residency were linked with residents' responses to a mindsets survey that they had taken at the midpoint of residency training. Multiple regression analysis was used to investigate the relationship between residents' mindset scores and their ITE scores. Of 85 residents, 46 (54%) had complete data for the three data collection points. Results Residents' ITE scores in year 1 were most predictive of their ITE scores in year 2 (β=0.72; P<.001). Mastery mindset scores were negatively associated with residents' performance on the ITE in year 2 (β=-0.29; P=.004). Conclusion While the observed negative relationship between residents' mastery mindset scores and their ITE performance may be disconcerting, it is not surprising. In clinical settings, residents are individually coached by preceptors and provided with specific, actionable feedback to support their learning. With respect to formative assessments, residents likely require explicit training on how to use their assessment results (ITE scores) to support their self-directed learning. This finding has practical implications for residency programs in using ITEs as formative assessments.
HDAC6 Inhibition Aggravates the Lyme Disease-Associated Inflammation

Histones are basic proteins that wrap around DNA for conditional accessibility of DNA to their transcription; however, HDAC6 is a unique histone deacetylase capable of deacetylating both histone and non-histone proteins. Recently HDAC6 has been shown to play an inflammatory role by limiting p38-MAPK activation in an LPS sepsis model. Here, we tested the ability of HDAC6 inhibition to control inflammation initiated by B. burgdorferi both in vitro and in vivo. The data indicate inhibition of HDAC6 leads to an increase in pro-inflammatory cytokines in macrophages and mice. HDAC6 deficient macrophages revealed similar results when co-incubated with B. burgdorferi. Inhibition of HDAC6 also resulted in diverse responses when exposed to different TLR ligands, suggesting a more complex role of HDAC6 in the inflammatory process. Additionally, inhibition of HDAC6 led to an increased acetylation of tubulin; however, there was minimal impact on p38-MAPK phosphorylation.
Atomistic simulations of Mg vacancy segregation to dislocation cores in forsterite

Interactions between dislocations in olivine and extrinsic cation vacancies created under hydrous or oxidizing conditions may influence the rheology of the Earth's upper mantle. In this study, we use atomic-scale simulations to calculate segregation energies for bare and protonated Mg vacancies to M1 and M2 sites in the core regions of [100](010) and [001](010) edge dislocations, and [100] and [001] screw dislocations. Calculated segregation energies are different for the two symmetry distinct M sites. The segregation energies calculated for the tightest binding M1 sites around [100] screw and [100] (010) edge dislocations are comparable to those calculated for the tightest binding M2 sites. Concentrations of M2 vacancy-related defects will thus be low in the core regions of these dislocations, given the comparatively high energy of these defects in the bulk lattice. In contrast, segregation energies for M2 defects to [001](010) edge dislocation cores are considerably lower than for equivalent M1 defects, and M2 vacancy concentrations around these dislocations will be similar to M1 vacancy concentrations. This means that the effect of magnesium vacancies on the mobility of the [001](010)
Resonant interactions in bouncing droplet chains

In a pioneering series of experiments, Yves Couder, Emmanuel Fort and coworkers demonstrated that droplets bouncing on the surface of a vertically vibrating fluid bath exhibit phenomena reminiscent of those observed in the microscopic quantum realm. Inspired by this discovery, we here conduct a theoretical and numerical investigation into the structure and dynamics of one-dimensional chains of bouncing droplets. We demonstrate that such chains undergo an oscillatory instability as the system’s wave-induced memory is increased progressively. The predicted oscillation frequency compares well with previously reported experimental data. We then investigate the resonant oscillations excited in the chain when the drop at one end is subjected to periodic forcing in the horizontal direction. At relatively high memory, the drops may oscillate with an amplitude larger than that prescribed, suggesting that the drops eVectively extract energy from the collective wave field. We also find that dynamic stabilization of new bouncing states can be achieved by forcing the chain at high frequency. Generally, our work provides insight into the collective behavior of particles interacting through long-range and temporally nonlocal forces.
Streaming Set Cover in Practice

State-of-the-art practical algorithms for solving large Set Cover instances can all be regarded as variants of the Greedy Set Cover algorithm. These algorithms maintain the input sets in memory, which yields a substantial memory footprint. In particular, in the context of massive inputs, these sets may need to be maintained on the hard disk or on external memory, and, consequently, access to these sets is slow. In this paper, we demonstrate that simple one-pass algorithms with small memory footprints are able to compete with the more involved Greedy -like algorithms for Set Cover in practice. Our experiments show that a recent Set Cover streaming algorithm by Emek and Ros´en [ACM Trans. on Alg. 2016] produces covers whose sizes are on average within 8% of those produced by state-of-the-art algorithms, while using between 10 and 73 times less memory. We also provide a theoretical analysis of an extension of the Emek-Ros´en algorithm to multiple passes and demonstrate that multiple passes allow us to further reduce cover sizes in practice.