Miracle-dz/UniFAR-FTUs huggingface dataset:
Further Improvement of Delay-Dependent Stability Criteria for Linear Time-Varying Delays Systems: Delayed Decomposition Approach

This paper studies the problem of stability for systems with differentiable time-varying delays. By using the information of delay derivative, improved asymptotic stability conditions for time-delay systems are presented. Unlike the previous methods, the upper bound of the delay derivative is taken into consideration even if this upper bound is larger than or equal to l(h(t) ＜ 1). It is proved that the obtained results are less conservative than the existing ones. Meanwhile, the computational complexity of the presented stability criteria is reduced greatly since fewer decision variables are involved. Numerical examples are given to illustrate the effectiveness and less conservatism of the obtained stability conditions.
Biological Network Inference: A Review of Methods and Assessment of Tools and Techniques

The topic of reconstruction of genetic networks is of great interest to the scientific community today – particularly those in the biological sciences. Essentially the need for network reconstruction is motivated by the need to find relationships between regulation mechanisms for genes, the need for discoveries in medicine, drug and pharmaceutical industry, the need for improved agricultural crops. All this requires a concerted effort from multi-disciplinary sciences, e.g. physics, mathematics, biology and chemistry – which have led to disciplines such as Systems Biology and Bioinformatics. Mathematical and statistical modeling has particularly been very instrumental for engineering and software development has been very useful in biological networks inference. Sometimes the link between theory, modeling and data acquisition is unclear. The goal in this article is to discuss tools and techniques for biological network inference and the areas of application. The pros and cons of network reconstruction methods are also provided. The number of scientific articles on network inference is overwhelming. Additionally, there is a dilemma in methodology choice, which is attributed to the scarcity of novel ways to compare the performance of the existing methods on experimental data. Applications of data visualization tools, modeling and simulation, data analysis and storage are given.
Modulation recognition network of multi-scale analysis with deep threshold noise elimination

To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results, researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques. To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios, we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing. The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion. The two modules are skip-connected to work together to improve the robustness of the overall network. Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods. The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.
A Sparsity Based Approach Towards Fast MRI Acquisition

ii PREFACE. iv TABLE OF CONTENTS . viii LIST OF TABLES . x LIST OF FIGURES . xi LIST OF ACRONYMS . xv ACKNOWLEDGEMENTS . xvi DEDICATION . xvii CHAPTER 1: INTRODUCTION AND OVERVIEW . 1 1.
Detecting Medicinal Plants Using YOLOv5: A Mobile Vision Approach

The use of medicinal plants for treating ailments has been a practice since ancient times. With the advent of computing and technology, the field of automated plant species identification has rapidly advanced. While there have been many studies on classifying plant species using leaves, the automatic identification using other plant organs remains unexplored. In this paper, we aim to investigate the precise and automated detection of flowers from medicinal plants in their natural environment. To achieve this, a new medicinal plant object detection dataset is presented comprising of four classes of medicinal plant species commonly found in the Philippines. Then, trained a YOLOv5-based model that detects medicinal plant flowers with a mean average precision of 83%. And, a mobile application was developed with the integrated model. Overall, the findings demonstrate the feasibility and acceptability of real-time automated detection and identification of medicinal plant species. This research has significant implications for the future of traditional medicine and the preservation of medicinal plant species.
Influence of mesh deformation on the quality of large eddy simulations

The influence of mesh motion on the quality of large eddy simulation (LES) was studied in the present article. A three‐dimensional, turbulent pipe flow (Reτ=360) was considered as a test case. Simulations with both stretching and static meshes were carried out in order to understand how mesh motion affects the turbulence statistics. The spatial filtering of static and moving mesh direct numerical simulation (DNS) data showed how an ideal LES would perform, while the comparison of DNS cases with static and moving meshes revealed that no significant numerical errors arise from the mesh motion when the simulation is fully resolved. The comparison of the filtered fields of the DNS with a moving mesh with the corresponding LES fields revealed different responses to mesh motion from different numerical approaches. A straightforward test was applied in order to verify that the moving mesh works consistently in LES: when the mesh is stretched in the streamwise direction, the moving mesh results should be in between the two extremal resolutions between which the mesh is stretched. Numerical investigations using four different LES approaches were carried out. In addition to the Smagorinsky model, three implicit LES approaches were used: linear interpolation (non‐dissipative), the Gamma limiter (dissipative), and the scale‐selective discretisation (slightly dissipative). The results indicate that while the Smagorinsky and the scale‐selective discretisation approaches produce results consistent with the resolution of the non‐static mesh, the implicit LES with linear interpolation or the Gamma scheme do not. Copyright © 2016 John Wiley & Sons, Ltd.
High-Speed and Energy-Efficient Single-Port Content Addressable Memory to Achieve Dual-Port Operation

High-speed and energy-efficient multi-port content addressable memory (CAM) is very important to modern superscalar processors. In order to overcome the disadvantages of multi-port CAM and improve the performance of searching stage, a high-speed and energy-efficient single-port (SP) CAM is introduced to achieve dual-port (DP) operation. For different bit cell topologies - the traditional 9T CAM cell and 6T SRAM cell, two novel peripheral schemes - CShare and VClamp are proposed. The proposed schemes are verified using all possible corners, a wide range of temperature and detailed Monte-Carlo variation analysis. With 65-nm process and 1.2 V supply, the search delay of CShare and VClamp is 0.55 ns and 0.6 ns, respectively, a reduction of approximately 87% compared to the state-of-the-art works. In addition, compared with the recently proposed IOT BCAM, CShare and VClamp can provide 84.9% and 85.1% energy reduction in the TT corner, respectively. Experimental results in an 8 Kb CAM at 1.2 V supply and across different corners show that the energy efficiency is improved by 45.56% (CShare) and 45.64% (VClamp) on average in comparison with DP CAM.
Atomic but Lazy Updating with Memory-mapped Files for Persistent Memory

Applications memory-map file data stored in the persistent memory and expect both high performance and fail-ure atomicity. State-of-the-art NOVA and Libnvmmio guarantee failure atomicity but yield inferior performance. They enforce data staying fresh and intact at the mapped addresses by continually updating the data there, thereby incurring severe write amplifications. They also lack the adaptability to dynamic workloads and entail housekeeping overheads with complex designs. We hence propose Acumen with a group of reflection pages managed for a mapped file. Using a simplistic bitmap to track fine-grained data slices, Acumen makes a reflection page and a mapped file page pair to alternately carry updates to achieve failure atomicity. Only on receiving a read request will it deploy valid data from reflection pages into target mapped file pages. The cost of deployment is amortized over subsequent read requests. Experiments show that Acumen significantly outperforms NOVA and Libnvmmio with consistently higher performance in serving a variety of workloads.
A Speed- and Energy-Driven Holistic Training Framework for Sparse CNN Accelerators

Sparse convolution neural network (CNN) accelera-tors have shown to achieve high processing speed and low energy consumption by leveraging zero weights or activations, which can be further optimized by finely tuning the sparse activation maps in training process. In this paper, we propose a CNN training frame-work targeting at reducing energy consumption and processing cycles in sparse CNN accelerators. We first model accelerator's energy consumption and processing cycles as functions of layer-wise activation map sparsity. Then we leverage the model and propose a hybrid regularization approximation method to further sparsify activation maps in the training process. The results show that our proposed framework can reduce the energy consumption of Eyeriss by 31.33%, 20.6% and 26.6% respectively on MobileNet-V2, SqueezeNet and Inception-V3. In addition, the processing speed can be increased by $\boldsymbol{1.96\times, 1.4\times}$ and $\boldsymbol{1.65}\times$ respectively.
MECALS: A Maximum Error Checking Technique for Approximate Logic Synthesis

Approximate computing is an effective computing paradigm to improve energy efficiency for error-tolerant applications. Approximate logic synthesis (ALS) methods are designed to generate approximate circuits under certain error constraints. This paper focuses on ALS methods under the maximum error constraint and proposes MECALS, a maximum error checking technique for ALS. MECALS models maximum error using partial Boolean difference and performs fast error checking with SAT sweeping. Based on MECALS, we design an efficient ALS flow. Our experimental results show that compared to a state-of-the-art ALS method, our flow is 13× faster and improves area and delay reduction by 39.2% and 26.0%, respectively.
Design, implementation and evaluation of the effectiveness of training caregivers of Alzheimer's patients by micro-learning method

Background: Alzheimer's disease is one of the most common disorders in elder that the need for daily care increases as the disease progresses. Caregivers of Alzheimer's patients are faced to a variety of pressures and job stresses. Receiving specialized and continuous training is one of the effective ways to increase their skills and abilities. One of the new educational methods is microlearning, that micro-scale educational content is provided to learners. The aim of this study is to evaluate the effectiveness of this educational method for caregivers of Alzheimer's patients.Methodology: This research was conducted by quasi-experimental one-group method with pre-test and post-test, samples were selected using census method and 8 educational topics in two groups of theoretical and practical topics were given each5 days to 23 caregivers of the elderly with Alzheimer's disease working in day care centers in Arak. Before and after the course, the test is taken from the participant by using a 21 questions researcher-made questionnaire to assess their level of knowledge in both stages. The researcher-made questionnaire examines the educational effectiveness according to kirkpatrick model in both levels of reaction and learning. Questionnaire data and pre-test and post-test results were analyzed using SPSS software.Results: the results show a significant difference between the mean scores of pre-test and post-test. Pre-test mean recorded 4.69(SD=4.47) and post-test recorded 17.82 (SD=2.08) which indicates the effectiveness of Trainings. the findings include a high and acceptable score of two levels of reaction (4.27 (P<0.001)) and learning (3.94 (P<0.001)), which indicates the effectiveness of the intervention. Examination of the mean scores shows that the reaction score was higher than learning. Conclusion: What is clear from the findings of this study is the effectiveness of micro-learning method for transferring educational materials to caregivers of Alzheimer's patients. It can also be recommended to use this method in training and transferring job and specialized skills.
Lossless Sparse Temporal Coding for SNN-based Classification of Time-Continuous Signals

Ultra-low power classification systems using spiking neural networks (SNN) promise efficient processing for mobile devices. Temporal coding represents activations in an artificial neural network (ANN) as binary signaling events in time, thereby minimizing circuit activity. Discrepancies in numeric results are inherent to common conversion schemes, as the atomic computing unit, i.e. the neuron, performs algorithmically different operations and, thus, potentially degrading SNN's quality of service (QoS). In this work, a lossless conversion method is derived in a top-down design approach for continuous time signals using electrocardiogram (ECG) classification as an example. As a result, the converted SNN achieves identical results compared to its fixed-point ANN reference. The computations, implied by proposed method, result in a novel hybrid neuron model located in between the integrate-and-fire (IF) and conventional ANN neuron, which numerical result is equivalent to the latter. Additionally, a dedicated SNN accelerator is implemented in 22 nm FDSOI CMOS suitable for continuous real-time classification. The direct comparison with an equivalent ANN counterpart shows that power reductions of $2.32\times$ and area reductions of $7.22\times$ are achievable without loss in QoS.
Analysing Word Stress and its effects on Assamese and Mizo using Machine Learning

Stress identification is an important problem in speech processing, which aims to convey special attention to the listeners. Stressed words or syllables result in changing the meaning of a sentence. In the case of tonal languages, stress identification is essential to understand how stress over words may affect the tone. This work is an attempt to identify the effects of stress on tones in Mizo and intonation in Assamese. It also aims to analyze the co-articulatory effects of stress and tones in Mizo and stress and intonation in Assamese with the help of an audio dataset. Although a few works are available to identify the effects of tones and stress, for Indian languages especially, in North East Indian languages which are extremely low in resources, to the best of our knowledge, no such work is available. For our work, we have considered one tonal language, i.e., Mizo or Lushai, spoken in and around the state of Mizoram, and one non-tonal language, i.e., Assamese, spoken in and around the state of Assam in India.
Architectural Radiation Hardening of CMOS Power Management Circuits through Bias Tuning

Within the space electronics industry, several strategies have been implemented to mitigate heavy ion, neutron, and proton induced radiation effects in CMOS processes, including radiation through process technology alterations and through circuit design, layout, and architecture. In this work, a new method is proposed that adaptively calibrates integrated analog circuits in CMOS bulk technology through bias tuning to create a radiation hardened system. In this technique, the device parameters that vary due to total ionizing dose radiation are monitored using built-in-self-test circuitry. These monitored parameters are used to tune and calibrate circuit level performance parameters. This work analyzes the TID radiation induced performance shifts in three critical power management circuits and uses bias tune in each circuit to recover circuit performance. The three circuits include: a ring oscillator, a bandgap voltage reference, and a non-overlap (dead-time) clock generator.
MANTIS: Machine Learning-Based Approximate ModeliNg of RedacTed Integrated CircuitS

With most hardware (HW) design companies now relying on third parties to fabricate their integrated circuits (ICs) it is imperative to develop methods to protect their Intellectual Property (IP). One popular approach is logic locking. One of the problems with traditional locking mechanisms is that the locking circuitry is built into the netlist that the (HW) design company delivers to the foundry which has now access to the entire design including the locking mechanism. This implies that they could potentially tamper with this circuitry or reverse engineer it to obtain the locking key. One relatively new approach that has been coined as hardware redaction is to map a portion of the design to an embedded FPGA (eFPGA). The bitstream of the eFPGA now acts as the locking key. In this case the fab receives the design without the bitstream and hence, cannot reverse engineer the functionality of the design. In this work we propose, to the best of our knowledge, the first attack on eFPGA HW redacted ICs by substituting the exact logic mapped onto the eFPGA by a synthesizable predictive model that replicates the behavior of the exact logic. This approach is particularly applicable in the context of approximate computing where hardware accelerators tolerate certain degrees of error at their outputs. One of the main issues addressed in this work is how to generate the training data to generate the synthesizable predictive model. For this we use SAT/SMT solvers as the potential attacker only has access to primary I0 of the IP. Experimental results for various degrees of maximum allowable output errors show that our proposed approach is very effective finding suitable predictive models.
Analysis of the Physical Properties of the N2 c′ 1Σ+u(0)-X 1Σ+g(0) Transition

A high-resolution, 33 mÅ (FWHM), optically thin emission study of the N2 c′ 1Σ(0)-X 1Σ(0) band transition has been performed with electron impact at 100 eV. Recently measured line oscillator strengths and lifetimes have been examined by comparing model spectra with the observed emission. Good agreement between model and data indicates consistency between the measured lifetimes and oscillator strengths. Predissociation yields for various Jj levels are estimated. The calculated predissociation yield is in the range 0.2-0.5 for Jj = 4-23 and negligible for Jj = 0-2.
FastFlow targeting FPGAs

Writing good code for FPGA is a challenge “per se”, but also running already existing and optimized FPGA kernels often requires writing specific “host side” code and some target hardware knowledge to achieve good performances. In this work, we describe a FastFlow extension supporting seamless off loading of tasks to FPGA, once an FPGA kernel is available. In particular, we show how kernels implemented in Vitis and running on XILINX Alveo FPGA boards may be integrated to implement “normal” parallel stages (pipeline stages, map/farm workers) in a structured parallel FastFlow computation. Experimental results are shown, demonstrating the feasibility of the approach.
A Low-cost Residue-based Scheme for Error-resiliency of RNN Accelerators

Acceleration and power reduction requirements are usually the main constraints for the design of Artificial Neural Network (ANN) accelerators. However, in the case of safety-critical applications like autonomous driving, reliability takes precedence over other requirements. Although ANN algorithms provide a degree of inherent resiliency, the hardware part is still vulnerable to faults and may cause catastrophic failures. This paper proposes using residue codes for detecting soft errors in Recurrent Neural Networks (RNNs), and in particular, Long Short-Term Memory (LSTM) networks. We attach Concurrent Error Detection (CED) hardware units to an entire LSTM structure or its substructures. Depending on the granularity of the components to which they are applied, CEDs are referred to as coarse-grain or fine-grain CEDs. The simulation results show that in fault detection rate and misprediction coverage rate, fine-grain CEDs have a better performance than coarse-grain. Specifically, fine-grain residue-based CEDs provide up to 97% fault detection for extremely large (10-2) bit error rates. Moreover, they reduce the misprediction rate by 84% compared to unprotected LSTM.
Stock Trend Prediction Based on Technical Indicator Graph Attention Network

In recent years, deep learning technology has been widely used in the financial industry, especially in the research of stock trend prediction. Aiming at the two aspects that most stock prediction algorithms do not make full use of the data and the model does not pay enough attention to the relationship between stocks, this paper establishes a graph attention network model based on technical indicators to better predict the stock trend. On the CSI 300 data set, we compare the model with the existing deep learning model and the single factor model in factor investment. A large number of experiments show that our model not only has higher prediction accuracy, but also has better profitability in practical application.
Fanout-Bounded Logic Synthesis for Emerging Technologies - A Top-Down Approach

In logic circuits, the number of fanouts a gate can drive is limited, and such limits are tighter in emerging technologies such as superconducting electronic circuits. In this work, we study the problem of resynthesizing a logic network with bounded-fanout gates while minimizing area. We 1) formulate this problem for a fixed target logic depth as an integer linear program (ILP) and present exact solutions for small logic networks, and 2) propose a top-down approach to construct a feasible solution to the ILP which yields an efficient algorithm for fanout bounded synthesis. When using the minimum depth achievable with unbounded fanouts as the target logic depth, our top-down approach achieves 11.82% better area as compared to the state-of-the-art with matching or better delays.
DEFORMATION RIGIDITY OF ODD LAGRANGIAN GRASSMANNIANS

Abstract. In this paper, we study the rigidity under Kahler deforma-tion of the complex structure of odd Lagrangian Grassmannians, i.e., theLagrangian case Gr ω (n,2n+1) of odd symplectic Grassmannians. To ob-tain the global deformation rigidity of the odd Lagrangian Grassmannian,we use results about the automorphism group of this manifold, the Liealgebra of inﬁnitesimal automorphisms of the aﬃne cone of the variety ofminimal rational tangents and its prolongations. 1. IntroductionThe study on deformations of the complex structure of complex manifoldsoriginates with Riemann. One hundred years after Riemann, Kodaira andSpencer developed the deformation theory of higher dimensional compact com-plex manifolds. Kodaira and Spencer showed that an inﬁnitesimal deformationof a compact complex manifold should be represented by the Kodaira-Spencerclass, which is an element of the ﬁrst cohomology group with coeﬃcients in thesheaf of germs of holomorphic vector ﬁelds (see [11] and [12]). Let Sbe a rational homogeneous manifold G/P for a complex simple Liegroup Gand a parabolic subgroup P⊂ G. As a consequence of the Bott-Borel-Weil theorem, H