Miracle-dz/UniFAR-FTUs huggingface dataset:
Confine Keypoint Triplets for Object Detection

Corner-based object detection algorithm is a hot topic in academia and industry. Scholars have applied it to different scenarios such as 3D object detection, object tracking, pose estimation and so on. The core of this method is to detect and match corner points. Although a considerable number of papers have been studied on this issue, there are still a lot of missed detection and false detection. In this paper, we pro-posed a one-stage object detection method with confine key- point triplets(ConfineNet), which applies center pooling and corner matching limit in local feature region. This restrictions can provide stronger correlation features with the object for subsequent prediction of center points, which can help to improve the prediction results of center points and corner matching. In addition, ConfineNet learns the association distance between the upper-left corner point and the lower-right corner point, which can confine the corner matching within a local area, thereby reducing matching errors. The AP value of Confine- Net reaches 48.2% on MS-COCO test-dev. our ConfineNet not only outperforms all existing anchor-free detectors but also achieves comparable performance to the state-of-the-art of two-stage detection approaches.
An ab-initio study of 2 dimensional metal (Cu, Ag) - 1T’ ReS2 van der Waals heterostructure

In this work with density functional theory calculations, we investigated the van der Waals heterostructure (vdWh) between metallic 2 dimensional (2D) sheets of Cu and Ag with monolayer 1T’ ReS2. With structural relaxations inclusive of van der Waals forces, the stable interfacial configuration were optimized. Thereafter the various properties of the vdWh, such as density of states, electron density, total potential and electron localization function, were thoroughly investigated. The computational studies show that both the heterostructures display a metallic to semi-metallic nature, with the Cu-ReS2 structure being more metallic. In terms of electrostatics, a larger concentration of electrons were observed on Cu as compared to Ag for the two interfaces, while an electron gas like feature could be seen between the 2D Ag and ReS2 layers. The results are of interest for 2D nanoelectronics devices and flexible electronics applications.
Compact Multilayer Bandpass Filter Using Low-Temperature Additively Manufacturing Solution

This article presented an additively manufactured bandpass filter (BPF) based on a second-order stub-loaded resonator consisting of multimetal layer components. The proposed BPF is fabricated by a low-temperature (140°) additively manufactured electronics (AME) solution that can fabricate conductive and dielectric materials simultaneously with multimetal-layer and flexible interlayer distance. By reducing the interlayer distance, constant inductance and capacitance can be realized in smaller sizes, which helps to achieve device minimization. Taking advantage of this inkjet printing technology, a second-order multimetal layer resonator is proposed. To understand the principle of the BPF, an equivalent circuit with odd- and even-mode analysis is demonstrated. For verification, the frequency response of the circuit’s mathematical model is calculated to compare with the electromagnetic simulation results. Good agreement can be achieved among the calculated, simulated, and measured results. The proposed BPF is designed at 12.25 GHz with a bandwidth of 40.8% and a compact size of 2.7 mm <inline-formula> <tex-math notation="LaTeX">$\times1.425$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times0.585$ </tex-math></inline-formula> mm or <inline-formula> <tex-math notation="LaTeX">$0.186\lambda {g} \times 0.098\lambda {g}\times 0.040\lambda {g}$ </tex-math></inline-formula>, which is suitable for circuit-in-package applications in television programs, radar detection, and satellite communications.
DisMASTD: An Efficient Distributed Multi-Aspect Streaming Tensor Decomposition (Poster Abstract)

Tensor decomposition is a fundamental multi-dimensional data analysis tool for many data-driven applications. However, the rapidly increasing data requires an efficient distributed dynamic tensor decomposition without re-computing the whole tensor from scratch. In this paper, we propose DisMASTD, an efficient distributed multi-aspect streaming tensor decomposition. First, we prove the optimal tensor partitioning problem is NP-hard. Second, we present two heuristic tensor partitioning approaches to ensure the load balancing. Third, we develop a distributed multi-aspect streaming tensor decomposition computation method, which avoids repetitive computation and reduces the communication. Last but not least, extensive experiments on both real and synthetic datasets are conducted to verify the efficiency and scalability of DisMASTD.
Cost Optimization for Live Streaming in Mobile Edge Computing

Live streaming service delivers the content in Mobile Edge Computing (MEC) to reduce the network latency and save the backhaul capacity. MEC servers have limited resources when they work independently. Hence it is beneficial that MEC servers collaborate and form an overlay to deliver. The critical challenge is how to optimize the topology among servers and allocate the link capacity so that the cost is lower while the delay is still under the constraint. Previous approaches rarely consider collaborations and the scheduling delay, especially for live streaming in MEC, leading to a sub-optimal performance.To address this, we propose a realistic overlay model where MEC collaboration, scheduling delay and capacity allocation are considered simultaneously. Links and servers are shared among all channel streams and each stream is pushed from cloud servers to MEC servers via steiner trees. Considering the optimization problem is NP-hard, we propose an effective algorithm called Cost Optimization for Live Streaming (COLS) to divide the original problem into two subproblems and solve them sequentially. COLS makes MEC servers collaborate with each other in topology generation. Then it reduces the cost and redundancy by convex programming in capacity allocation. Simulation results show that proposed scheme reduces the capacity cost by more than 40% with an acceptable delay compared with state-of-the-art schemes.
Fusing Multi-Features for Rumor tweets detection in Wechat

Recently, rumor detection has attracted lots of research interest because of the quick development of social networks and the widely spread of rumor. The exiting studies is mainly about the open social network such as Weibo and Facebook. However, there is little research about closed social networks, especially Wechat. As a famous app with more than one billion daily active user, the tweets in Wechat can attract lots of attention, which gives the spread of rumor tweets more chance. In this paper, we empirically study the tweets spread in Wechat. We first analyze the text information and users’ behavior of Wechat tweets, then we come up with several important features needed for rumor detection. Furthermore, we propose a new model which can capture the impact of both users’ behavior and text information. We conduct experiments using a large tweets dataset collected in Wechat, the result shows that our proposed method can effectively detect the rumor tweets.
Carrier Lifetime Degradation and Regeneration in Gallium- and Boron-Doped Monocrystalline Silicon Materials

In this article, carrier lifetime degradation phenomena on fired gallium-doped Czochralski-grown silicon (Cz-Si:Ga) and boron-doped float-zone silicon (FZ-Si:B) are observed. We examine lifetime degradation and regeneration as a function of illumination intensity and temperature and observe qualitatively similar degradation effects in both material classes, which are triggered by a fast-firing high-temperature step. Charge carrier injection, e.g., through illumination, is required to activate the defects responsible for degradation. The extent of degradation increases with increasing temperature, which is untypical for degradation effects reported before. Despite different degradation time constants are measured for Cz-Si:Ga and FZ-Si:B, the activation energies are for both materials in the narrow range $({0.58 \pm 0.04}){\boldsymbol{\ }}{{\bf eV}}$. The extracted activation energy is quite different compared with other degradation effects in silicon, suggesting a novel defect formation mechanism. Since the lifetime degradation is triggered by the fast-firing of the silicon wafers during the presence of a hydrogen-rich dielectric at the surface, the involvement of hydrogen in the defect reaction is very likely. During prolonged illumination at elevated temperature (135 °C), we observe a permanent regeneration of the lifetime, whereas at temperatures close to room temperature (36 °C), the defect deactivation is only temporary.
A Medical Guidance Model Driven by Subjective and Objective Knowledge

Because of the impact of Corona Virus Disease 2019 (COVID-19), online medical services have developed rapidly and are widely accepted by people. People can find doctors for diagnosis and treatment by the name of the disease online. However, patients usually lack professional medical knowledge and have their own subjective preferences for health services, which makes it difficult for patients to accurately find a doctor that suits them. To this end, we proposed a medical guidance model driven by subjective and objective knowledge to provide decision support to patients. In the proposed model, the doctor's and disease's own information is regarded as objective knowledge, and the information of doctor feature extracted from patient reviews is regarded as subjective knowledge. They are fused into a knowledge graph. On this basis, a knowledge decision engine is designed to recommend the most suitable doctor based on the patient's objective conditions and subjective preferences. Finally, a prototype system is designed and developed to demonstrate the feasibility of the model as above. The system guides patients to improve their objective conditions and subjective preferences through inquiries, and returns recommended doctors to patients in an interpretable manner. The medical guidance model can effectively meet the personalized and professional needs of patients in online medical services, which has good practical value under the digital healthcare continues to become the trend of the future.
Detection of plant diseases in the images using Deep Neural Networks

Agriculture suffers from crop diseases, and losses yield every year. Early detection of crop diseases can effectively decrease the loss. Leaves from crops are affected by the disease and can help farmers to detect any changes. Our study uses crops labelled dataset to train the Faster-RCNN model to identify if leaves are affected by any means. Our study shows more than 97% accuracy to detect disease in early stages that framers were unable to do in the past.
Dimension of a Subset of Residue Classes

This paper introduces a problem in additive number theory which is motivated by optimization of hardware implementation of QC-LDPC codes. For a fixed subset S, $S \subset {\mathbb{Z}_q}$, of residue classes modulo q the object of interest is a basis set G, $G \subset {\mathbb{Z}_q}$, of minimal size, such that every element of S is representable as a sum of several elements from G. For a fixed number k, k ≤⌈log2 q⌉, the object of interest is a function ζ(q,k) defined as a maximum number such that, for every set of cardinality < ζ(q,k), there exists a basis set of cardinality < k.
Worst-Case Response Time Analysis for Best- Effort Traffic in an Ethernet-AVB Network

A credit-based shaper (CBS) is adopted to guarantee the timing requirements of stream reservation (SR) class traffic in an Ethernet-audio video bridging (AVB) network, which introduces additional delay for best-effort (BE) traffic. This paper proposes an approach for calculating the worst-case response time (WCRT) of BE traffic and guaranteeing tightness; Moreover, a longest busy interval in which there exits an alternation of pending loads in SR class A and class B traffic at an output port, is defined and identified. Some suggestions for CBS parameters setting can be got form the results.
A data science approach to Predict the University Students at risk of semester dropout: Bangladeshi University Perspective

In Bangladeshi institutions, the likelihood of student semester dropout has increased in recent years. A large number of university students, particularly in science background disciplines, are enrolled in a variety of undergraduate courses. Nevertheless, the perfection rate is poor. In general, students drop out for a variety of reasons, including academic, family, personal, and political concerns. The main focus of this study is to predict the risk of semester dropout in Bangladesh so that the massive dropout can be stopped. In this research, the current student information is preprocessed to discover the major reason as well as students whoever at the threat of semester dropout will help to grow a new structure in the area of higher education. To predict the dropout risk, random forest and logistic regression were practiced for obtaining the detection model.
Electromagnetic Emission of BLDC Motor Controllers

The electrically commutated brushless direct current (BLDC) motors are constantly displacing conventional brushed DC motors in industrial usage, especially in the automotive industry. During the research, we investigated the electromagnetic noise emission of a BLDC motor operating a car engine cooling fan. During the determination of electromagnetic noise, we performed near-field measurements and examined the directed fields. The emission of the control electronics of BLDC motors with different phase and pole numbers was measured in three directions, with two antenna polarizations per direction. These data can be used to outline a three-dimensional radiation characteristic. The measurement results were compared, and the result was obtained to what extent and to which controlled electromagnetic noise emissions each component of the BLDC motor is responsible.
Modified Baugh Wooley Multiplier using Low Power Compressors

Multipliers are basic building blocks of many digital systems such as digital signal processing application, microprocessor, digital communication etc. Multipliers possess huge power and delay. So, it is inevitable to reduce the power consumption and delay of multipliers in order to get high performance digital system. Modified Baugh Wooley Multiplier is a widely used 2’s complement multiplier which multiplies negative as well as positive numbers efficiently. Modified Baugh Wooley multipliers are very efficient in handling sign bits. In this paper a 16-bit Modified Baugh Wooley Multiplier is presented. The proposed circuit is designed using low power compressors and full adders and modified Ripple Carry Adder and simulated in Cadence Virtuoso using gpdk 45nm technology. From the simulation it is observed that the design consumes less power and less delay as compared to conventional circuits. The transistor count of the design is also less.
Discriminative Admission Control for Shared-everything Database under Mixed OLTP Workloads

Due to the variability of IT applications, the back-end databases usually run the mixed OLTP workload, which comprises a variety of transactions. Some of these transactions are high-conflict and others are low-conflict. Furthermore, high-conflict transactions may contend on different groups of data stored in the database. Without precise admission control, too many transactions with conflict on the same group of records are simultaneously executed by the OLTP engine, and this will lead to the well-known problem of data-contention thrashing. Under mixed OLTP workloads, conflicting transactions would be blocked for a long time or rolled back finally, and other transactions have not enough opportunity to be processed.To achieve the optimal performance for each kind of transaction, we design a discriminative admission control mechanism for shared-everything database, referred to as DAC. DAC can quickly identify and classify high-conflict transactions according to the set of records they try to access, which is defined as a conflict zone. DAC makes admission control over OLTP transactions with the conflict zone as the granularity. By adaptively adjusting the transaction concurrency level for each zone, transaction blocking and waiting among the same kind of high-conflict transactions can be alleviated. Furthermore, thread resources are released to make the execution of low-conflict transactions less affected. We evaluate DAC using a main-memory database prototype and a classical disk-based database system. Experimental results demonstrate that DAC can help OLTP engine significantly improve the performance under mixed OLTP workloads.
Identification Procedure of a Congruency-based Anisotropic Vector Hysteresis Model Using Uniaxial Magnetic Measurements

The identification procedure of a congruency-based vector hysteresis model is presented. A scalar congruency-based hysteresis model is developed using a set of measured First-Order Reversal Curves (FORCs). The scalar model is verified by comparing the model results with quasi-static measurements. An anisotropic scalar model is obtained from two scalar hysteresis models using FORCs measured parallel and perpendicular to the material rolling direction. Mayergoyz vector generalization is utilized to develop the vector model from the scalar model. The associated identification procedure is discussed in detail. The evaluation of the resulting vector hysteresis model justifies the anisotropy property of the model.
Neuromorphic Brain-Inspired Computing with Hybrid Neural Networks

Neuromorphic brain-inspired computing is believed to solve the bottleneck of traditional Von Neumann architecture computers and may promote the development of the next-generation of high-performance computer architectures. Therefore, in recent years, brain-inspired computing has received extensive attention. Some large-scale brain-inspired research projects have yielded some results, such as further increasing computer capabilities in data processing and machine learning. How there is a lack of widely used artificial neural network based on computer science and neuroscience-inspired models and algorithms. This low compatibility between software and hardware reduces the computational programming efficiency and becomes an obstacle to the development of brain-inspired computing. This paper introduces the concept of neuromorphic brain-inspired computing and then introduces the research results of Neurogrid, Spinnaker, TrueNorth, Loihi and Tianjic, respectively. Finally, the paper shares the views on the future development trend of the field of neuromorphic brain-inspired computing.
X-ray Classification of Tuberculosis Based on Convolutional Networks

Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis, which can invade many organs, and pulmonary tuberculosis is the most common infection. It is the key to treat tuberculosis to detect and diagnose the disease in the early stage. The existing computer-aided detection system has made preliminary progress in the diagnosis of pulmonary tuberculosis based on chest X-ray, but there is still a lack of further research on the classification of image signs of tuberculosis. In recent years, with the in-depth research and development in the field of deep learning, convolutional networks have emerged. Convolutional networks have achieved the best current results in image recognition, image classification, image segmentation, and other fields. Therefore, this paper applies the convolutional network to tuberculosis CT images and uses different convolutional network models to study the classification of tuberculosis CT images. Experiments show that the DenseNet121 model has higher performance than VGGNet16, VGGNet19, and ResNet152 models. As a result of classification, the accuracy rate is over 90%.
Accelerating Similarity-based Mining Tasks on High-dimensional Data by Processing-in-memory

Similarity computation is a core subroutine of many mining tasks on multi-dimensional data, which are often massive datasets at high dimensionality. In these mining tasks, the performance bottleneck is caused by the ‘memory wall’ problem as substantial amount of data needs to be transferred from memory to processors. Recent advances in non-volatile memory (NVM) enable processing-in-memory (PIM), which reduces data transfer and thus alleviates the performance bottleneck. Nevertheless, NVM PIM supports specific operations only (e.g., dot-product on non-negative integer vectors) but not arbitrary operations. In this paper, we tackle the above challenge and carefully exploit NVM PIM to accelerate similarity-based mining tasks on multi-dimensional data without compromising the accuracy of results. Experimental results on real datasets show that our proposed method achieves up to 10.5x and 8.5x speedup for state-of-art kNN classification and k-means clustering algorithms, respectively.
On the Optimality of Treating Interference as Noise: General Message Sets Revisited

We study the optimality of power control and treating interference as noise (TIN) in the M × N X channel, from the generalized degrees-of-freedom (GDoF) and constant- gap capacity perspectives. A result by Geng, Sun and Jafar shows that if there exist K = min(M, N) transmitter-receiver pairs such that each direct link strength is no less than the sum of the strongest incoming and strongest outgoing cross link strengths (all in dB), then it is optimal to reduce the M × N X channel to a K-user interference channel and use TIN. The proof of this result relies on a deterministic approximation of the original Gaussian network, specifically for the case M < N. Here we present a simpler proof by working directly with the original Gaussian network. Our proof relies on a new "less noisy under interference" order exhibited by TIN-optimal M × N X channels, akin to the "less noisy" order in broadcast channels.
Building up a Wide-Area Protection System Simulator

Wide-area protection systems (WAPS) are becoming more popular among power systems around the world, due to their applications for employing remedial action schemes to avoid partial or total blackouts. Thus, this paper deals with building up a wide-area protection system simulator, conceiving its design and implementation in a simulated environment. A typical architecture of WAP is adopted taking into consideration that a wide-area monitoring system (WAMS) is in charge of measuring the frequency across the power system. The WAPS implementation is embedded into the DIgSILENT PowerFactory© and Matlab™ environments by making use of the Digsilent Programming Language (DPL) and Matlab scripts. Then, different remedial action schemes are proposed to avoid the unbalance in duo load and generation. The feasibility of the schemes is corroborated in different scenarios varying the disturbance that takes place in a benchmark version of the New England power grid. Finally, results exhibit and validate that the WAPS implementation and remedial action schemes in a simulated environment can be successfully addressed.