Miracle-dz/UniFAR-FTUs huggingface dataset:
Membership Detection for Real-world Groups Hidden in Social Network

Real-world groups are organizations or communities existed in the real world, such as the employees of a company, the students of a school, different from the virtual communities in social networks. The members of a real-world group may also appear in the social network and form into a virtual community. However, the community detection methods are not effective to detect the real-world groups because the members may lack interaction and sensitive attributes in the social network, so that the real-world groups appear to be hidden in the social network. This paper defines three kinds of real-world group models and defines sensitive attributes and sensitive relationships of users in real-world groups. We use random walk to detect memberships for real-world groups hidden in social network with no or little edges and sensitive attributes. We evaluate our model with a Facebook dataset. The experiments show that our model has an accuracy of 95%.
Deep Learning Investigation of Mass Spectrometry Analysis from Melanoma Samples

Deep learning has yet to be widely applied in the field of chemical gas sensing, in part due to the nature of this data. Many applications of chemical gas sensing suffer from a limited number of samples of high dimensional data. In this study, a novel data approach is introduced to address these issues and is applied to a dataset from mass spectrometry (MS) for melanoma detection. Samples were taken from 32 patients presenting with a dermatological mole. Various data analyses were performed. Traditional analysis such as primary component analysis (PCA), linear discriminant analysis (LDA), and a perceptron were used. In addition, a 1-hidden layer, fully connected neural network (also known as a multilayer perceptron) and a deeper 5-hidden layer, fully connected neural network was trained to classify inputs as deriving from a melanoma or non-melanoma mole. We segmented each sample and trained the network to assign a probabilistic output interpreted as “confidence in melanoma” to each segment. Traditional inference testing on these confidence measures found highly significant differences in the outputs of the multilayer perceptrons between melanoma versus non-melanoma samples. This provides a statistically grounded approach to deep learning-based classification of small amounts of high dimensional data, given the ability to segment the samples into a sufficient number of inputs for model training.
Four-Way Filtering Power Divider Using SIW and Eighth-Mode SIW Cavities With Ultrawide Out-of-Band Rejection

A four-way filtering power divider (PD) based on a substrate-integrated waveguide (SIW) is presented. This structure is composed of two layers of the dielectric substrate. The eighth-mode SIW (EMSIW) technique is used to make four cavities in the upper substrate. In addition, an SIW cavity is provided in the lower substrate. To achieve an efficient transition between the two layers, four rectangular slots are etched on the middle metal planes. A circular slot is etched on the top metal layer of the EMSIW cavities to suppress the higher operating modes. To prove the validity, a four-way PD with a second-order bandpass filtering response is designed and fabricated. The measured results show that the PD achieves an insertion loss of 1.3 dB at 3.5 GHz, a return loss of higher than 17 dB, a 3-dB fractional bandwidth of 7.75%, and an out-of-band rejection of greater than 26 dB from 3.92 to 16.5 GHz. The results show that this PD has the advantages of compact size, high selectivity, and ultrawide out-of-band rejection.
A New Metric to Quantify the Vulnerability of Power Grids

Major blackouts are due to cascading failures in power systems. These failures usually occur at vulnerable links of the network. To identify these, indicators have already been defined using complex network theory. However, most of these indicators only depend on the topology of the grid; they fail to detect the weak links. We introduce a new metric to identify the vulnerable lines, based on the load-flow equations and the grid geometry. Contrary to the topological indicators, ours is built from the electrical equations and considers the location and magnitude of the loads and of the power generators. We apply this new metric to the IEEE 118-bus system and compare its prediction of weak links to the ones given by an industrial software. The agreement is very well and shows that using our indicator a simple examination of the network and its generator and load distribution suffices to find the weak lines.
Output and Control Characteristics of an LLC Resonant DC/DC Converter

The present paper discusses output characteristics derived from existing theoretical studies for different values of inductance ratio a and frequency ratio v which provide operation in ZVS mode. As a result of the unified analysis, the dependence of the control characteristic as a function of the load is also obtained. The influence of the inductance ratio on the control characteristics and the properties of the circuit is analyzed. Comparative data from calculated, simulated and experimentally obtained results is presented.
Count Rate Corrections for the Plant Dedicated PET System phenoPET

PET imaging is widely used to perform clinical and preclinical studies. A rather new area is the field of plant research. PET allows the dynamic imaging of 3D carbon transport and allocation within plants shoot and root systems of plants. Here, a new system called phenoPET has been developed within the German Plant Phenotyping Network (DPPN) using Digital Photon Counters (DPC) by Philips (digital silicon photo multipliers). Those are organized in horizontal rings forming a Field of View (FOV) of 180mm diameter and 200mm height.The detected hits are arranged in time frames, which are written to disc via USB 3:0 with a maximum transfer rate of 375MBs−1. Preprocessing and coincidence sorting are done offline. To enable high dynamic range phenoPET can drop frames which are not saved on disc when the transfer rate comes to a limit.To compare carbon transport of different plants quantitative images are required. A preliminary normalization and random correction are implemented. In this context, count rate correction and dead time correction are mandatory to avoid quantitative bias. In a decay experiment the dead time of the DPCs reduces the rate of singles by up to 10% at an radioactivity of about 100MBq placed close to the center of the FOV. At this activity about 70% of the frames are dropped leaving a maximum single rate of about 2:5 • 105 cps on each module. Further count rate correction factors for coincidences due to missing frames are presented.
A Low-Energy Inductive Transceiver using Spike-Latency Encoding for Wireless 3D Integration

Recently, the use of wireless (or contactless) 3D integration has been proposed as a low-cost method of stacking disparate processing and sensor dies into singular, small form-factor ICs. Whilst such devices would be ideally suited for the Internet of Things (IoT), in the IoT, maintaining low-power consumption is of paramount importance. Contactless intertier links use significant energy when forming a magnetic field which can penetrate multiple silicon dies, and hence are often criticised for their poor power efficiency when compared to wired alternatives such as through silicon vias (TSVs). To address this, in this paper we present a novel, neuro-inspired, inductive transceiver (for transmitting data between tiers of a 3D-IC) that maintains low power consumption by encoding frames of data in terms of the latency between pulses, thereby reducing the number of transmit pulses and energy required per bit. The proposed approach is validated using commercial electromagnetic and electrical circuit simulators in 65nm CMOS technology. Results demonstrate an energy consumption of 0.79pJ/bit, representing a reduction of 31% when compared to existing state-of-the-art transceivers, or an increased communication distance of up to 1.8× for the same energy budget.
Weakly Convex Regularized Robust Sparse Recovery Methods With Theoretical Guarantees

Robust sparse signal recovery against impulsive noise is a core issue in many applications. Numerous methods have been proposed to recover the sparse signal from measurements corrupted by various impulsive noises, but most of them either lack theoretical guarantee for robust sparse recovery or are not efficient enough for large-scale problems. To this end, a general optimization problem for robust sparse signal recovery, which includes many existing works as concrete instances, is analyzed by a freshly defined Double Null Space Property (DNSP), and its solution is proved to be able to robustly reconstruct the sparse signal under mild conditions. Moreover, for computational tractability, weakly convex sparsity-inducing penalties are applied to the general problem, and properties of the solution to the resultant non-convex problem are further studied. Based on these properties, an algorithm named Robust Projected Generalized Gradient (RPGG) is devised to solve the weakly convex problem. Theoretical results prove that the sparse signal can be precisely reconstructed by RPGG from compressive measurements with sparse noise or robustly recovered from those with impulsive noise. Meanwhile, simulations demonstrate that RPGG with tuned parameters outperforms other robust sparse recovery algorithms.
InDP: In-Network Data Processing for Wireless Sensor Networks

Wireless sensor networks have emerged as an important information collection and monitoring tool. The data collected is typically uploaded to a central gateway for processing and analysis. However, the approach of forwarding all the sensed data to a sink for processing is not always practical due to the high communication cost.In this paper, we present InDP, a framework that is designed to support data dissemination and processing in the edge. InDP has a communication component and a computation component. The communication component supports a low duty cycle mode for an infrequent status update and a high throughput mode to support distributed computation. The computation component implements a distributed version of Principal Component Analysis (PCA). As an application, we have implemented an outlier detection component over InDP.InDP and the outlier detection application have been implemented on Contiki using a modified version of Codecast as the underlying many-to-many communication protocol. Our evaluations show that InDP can terminate as fast as 100ms and 1.3s on the average running on a testbed with more than 70 nodes. In terms of PCA computation, InDP’s computation uses 91.6% less data than a centralized approach and is able to detect anomalies using a fraction of the sensed data.
Essential Factors for Explosive Compaction of Nanodiamond-Metal Composite

This paper considers a process of explosive compaction of detonation-produced nanodiamond powder together with nanopowders of metals to create composites. Use of particles of the nanodiamond having unique properties (hardness, heat conductivity, etc.) will allow obtaining of composite material with increased physical and mechanical properties. We considered powders of copper and aluminum as a metal matrix. As a result of theoretical consideration of the shockwave compaction for powder mixtures, we offered and discussed the conditions of explosive compaction. The compaction process can be characterized by a set of values of the shockwave pressure which define powder compaction, deformation, and particle heating. These values are: 1. minimum shockwave pressure under which the compaction and the shockwave in the powder do not occur; 2. pressure under which there is a jet cleaning of the particle surface; 3. pressure that will result in a homogeneous compact with maximum density; 4. pressure under which there is a phase transition of particles. Our research presents the result of calculations of the pressure thresholds for various powders of metals in mix with detonation-produced nanodiamond.
A Four-Mode OAM Antenna Array With Equal Divergence Angle

Orbital angular momentum (OAM) waves with different modes usually perform different divergence angles, leading to a big challenge and high complexity for signal receiving. To address this issue, this letter presents a novel antenna array generating four-mode OAM waves with equal divergence angle. In the proposed antenna, a center patch antenna and three concentric uniform circular arrays (UCAs) are employed to carry 0, −1, −2, and −3 OAM modes from center to outer rings, respectively. Each UCA is carefully optimized to control the different mode OAM waves to propagate along the same divergence angle. To validate the design, a C-band prototype generating 0, −1, −2, and −3 modes is fabricated and measured. Measured results show that the proposed four-mode OAM waves can be well generated. More importantly, the equal divergence angle for −1, −2, and −3 mode OAM waves is obtained as expected. This antenna array can provide equal divergence angle multimode OAM waves, which simplifies the receiving side in an OAM wireless communication system.
A Generalized Space Vector Modulation for Cascaded H-bridge Multi-level Inverter

Nowadays. , Space Vector Modulation (SVM) technique is widely used in multi-level converters with a variety of advantages: better utilization of DC bus in comparison with Sin PWM and the lower harmonics. , providing more space vectors than the conventional two-level inverter. Consequently. , we can enhance the quality of multilevel converters. However. , as the number of required converter levels rise, the complexity and computation volume in the determination of optimized switching sequence will increase. As a result, we have some difficulties in implementing simulations and experiments. The paper provides a general SVM technique which generates any level with cascaded H-bridge (CHB) structure so as to not only simplify SVM techniques and reduce calculation volume but also create favorable conditions to apply more widely this structure in industries. The proposed method has been simulated in Matlab software and experimented with the 11-level CHB structure based on FPGA platform, indicating the accuracy of the given method.
Study on the noise coupling and shielding between through silicon vias

Noise coupling between through silicon vias (TSVs) degrades the performance and reliability of 3D integration systems. This paper focus on the research of noise coupling and shielding between TSVs. The impact of various structural parameters on TSV-TSV coupling is analyzed. Three types of noise shielding methods are investigated to evaluate the effectiveness of them to reduce the coupling noise. The TSV-TSV coupling coefficient are extracted from measurement results taking advantage of a proposed de-embedding method, and the effect of several design parameters on coupling coefficient is discussed.
Commercial Wi-Fi Based Fall Detection with Environment Influence Mitigation

Motivated by the urgent demands of indoor fall detection, significant progress has been made to explore Wi-Fi based fall detection techniques that utilize the information collected by commercial Wi-Fi signals to infer falling without carrying a dedicated device. Existing commercial Wi-Fi based fall detection systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The Wi-Fi signals arriving at the receiving devices usually carry substantial information specific to the environment where falling happens. Due to this reason, a fall detection model trained in one specific environment does not work well in the other. Furthermore, it is labor-intensive and time-consuming to acquire sufficient data and rebuild fall detection model in each new environment. To address this challenge, we propose TL-Fall, a transfer-learning based device free fall detection system. Specifically, the fall detection model is trained with labeled data to derive knowledge in the old environment. With the derived knowledge, the fall detection model working in the new environment can be trained with only a few labeled data. The effective across environment knowledge reuse mitigates the environmental influence and maintains the satisfied fall detection accuracy as well. The extensive experiments are conducted on TL-Fall in typical indoor environment. The experimental results demonstrate the superior effectiveness of TL-Fall, with 86.83% fall detection sensitivity and 84.71% fall detection specificity on average.
Adaptive Learning Knowledge Networks for Few-Shot Learning

In recent years, relying on training with thousands of labeled samples, deep learning has achieved remarkable success in the field of computer vision. However, in practice, annotating samples is a time-consuming and laborious task, which means that it is impractical to obtain thousands of labeled data. Humans have the ability to learn the knowledge of new concepts from only a handful of examples, which makes it easier to adapt to new environments. Inspired by this ability of human beings, few-shot learning aims at training a classifier that can learn to recognize new classes when only given a few labeled samples of these classes. In this paper, we propose a new framework called Adaptive Learning Knowledge Networks (ALKN) for few-shot learning. ALKN learns the knowledge of different classes from the features of labeled samples and store the learned knowledge into memory which will be dynamically updated during the learning process. We define the difficult knowledge and easy knowledge of each class so that when performing inference, our model can holistically leverage the memory of learned knowledge more efficiently. Considering the situations of standard few-shot learning and semi-supervised few-shot learning, we design different update strategies for the memory of learned knowledge. Extensive experiments are conducted on three datasets, Omniglot, Mini-Imagenet and CUB. Compared with the most existing approaches, our ALKN achieves superior results on those benchmarks.
Broadband Detection capability of a Triple Barrier Resonant Tunneling Diode

The monolithic on-chip integration and design of a high current density InP-based Triple Barrier Resonant Tunneling Diode within a bow-tie antenna structure for detection application is presented. The asymmetrical current-voltage characteristics of the Triple Barrier Resonant Tunneling Diode and its small capacitance provide a powerful candidate for THz signal detection. The integration into a planar broadband antenna structure such as a bow-tie design enables realization of a broadband detector from single GHz up to THz frequencies. In this work, experimental data are demonstrated in the frequency range of 75 to 110 GHz and from 220 to 330 GHz.
PET Image Reconstruction using Joint MR-PET Dictionary

Simultaneous magnetic resonance imaging (MRI) and positron emission tomography (PET) enable simultaneous acquisition of anatomical and functional imaging. Currently, separate reconstruction of MRI and PET images is performed. Recently, joint reconstruction of MRI-PET has been explored using gradient based priors, which have the risk of infusing cross-modality artefacts. We propose a coupled MR-PET patch based dictionary prior for the joint reconstruction of MRI and PET contrasts.
A Reconfigurable 0.1–10 Gb/s Voltage-Mode Transmitter With 0.2–1V Output Swing

We present a 0.1–10 Gb/s voltage-mode transmitter with a 0.2–1.0Vpp,diff output swing and a 2-tap FIR equalizer. It is configured as a P-over-N or N-over-N structure for high and low swings, respectively. Fabricated in a TSMC 65nm CMOS LP process, and operated under a 1.2V supply, the transmitter dissipates 10.3 and 36mW at 0.1 and 10 Gb/s, respectively, for 1Vpp,diff output swing. The power reduces to 27mW for 0.2Vpp,diff output swing at 10 Gb/s.
Development of a 3D Semantic Segmentation Camera based on Mask Regional Convolutional Neural Network

3D semantic segmentation is important for the performance improvement of a robot vision task. Detection and identification of a large variety of objects in a scene are challenging for autonomous robotic manipulation. With the semantic segmented depth information, a robotic arm can obtain the 3D regions of target objects in a complicated scene. In this paper, we present the development of a 3D semantic segmentation camera based on the Mask Regional Convolutional Neural Network (Mask R-CNN) for the understanding of 3D images at the color level. Each object class is assigned by color for representation. It efficiently separates different objects in an image while concurrently produces a high-quality segmentation for each object. This camera is developed with a structured light technique to obtain 3D information with high accuracy and density. The experimental results demonstrate that the 3D information and semantic segmentation can be combined to achieve reliable scene perception.
Towards s/engineer/bot: Principles for Program Repair Bots

Of the hundreds of billions of dollars spent on developer wages, up to 25% accounts for fixing bugs. Companies like Google save significant human effort and engineering costs with automatic bug detection tools, yet automatically fixing them is still a nascent endeavour. Very recent work (including our own) demonstrates the feasibility of automatic program repair in practice. As automated repair technology matures, it presents great appeal for integration into developer workflows. We believe software bots are a promising vehicle for realizing this integration, as they bridge the gap between human software development and automated processes. We envision repair bots orchestrating automated refactoring and bug fixing. To this end, we explore what building a repair bot entails. We draw on our understanding of patch generation, validation, and real world software development interactions to identify six principles that bear on engineering repair bots and discuss related design challenges for integrating human workflows. Ultimately, this work aims to foster critical focus and interest for making repair bots a reality.
Energy-Autonomous MCU Operating in sub-VT Regime with Tightly-Integrated Energy-Harvester : A SoC for IoT smart nodes containing a MCU with minimum-energy point of 2.9pJ/cycle and a harvester with output power range from sub-µW to 4.32mW

We describe a SoC achieving zero-net-energy operation by using energy harvesting and sub-threshold design. It consists of a microcontroller for data acquisition, data processing, and communication and an energy harvester unit enabling energy-autonomous operation. The microcontroller built with latch-based design achieves robust operation in a wide frequency range from 5 kHz to 40 MHz with a minimum energy point of 2.9 pJ/cycle @ 0.375V and 21 MHz. With this SoC, the battery lifetime is extended indefinitely thanks to the energy harvesting unit, which is capable to handle output power range from sub-µW to 4.32 mW with peak conversion efficiency of 65%. The tight integration of MCU and harvester enables the development of customized firmware algorithms for energy optimized operation with very diverse energy sources.