Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping and Migration Strategies for Thermal Management in Many-Core Systems

New technology nodes enable the integration of billions of transistors in a small silicon area by replicating identical structures, resulting in many-core systems. However, power density may limit the amount of energy the system can consume. A many-core at its maximum performance may lead to safe temperature violations and, consequently, result in reliability issues. Dynamic Thermal Management (DTM) techniques proposals guarantee that many-core systems run at good performance without compromising reliability. In this paper, we review recent DTM works, discussing their limitations, and propose new heuristics for thermal-aware application mapping and migration, using a hardware accelerator that enables temperature monitoring on systems with a large number of processing elements. Results show that using straightforward heuristics, with reactive actions based on runtime temperature monitoring, reduce the peak temperature in high workload scenarios (6.8%), and improve thermal distribution significantly on a large (8×8) many-core system.
Reinforcement Learning Based Adaptive Duty Cycling in LR-WPANs

For conserving energy, duty cycle is defined by setting up the active and sleep periods of network nodes. In beacon enabled networks, to provide support for duty cycle, the IEEE 802.15. 4 standard uses optional super-frame structure. This duty cycle is usually fixed and does not consider the topology changes that often occur in dynamic sensor networks. In this paper, existing energy conserving duty cycling approaches for 802.15. 4 networks especially the adaptive duty cycling techniques for wireless sensor networks are summed up. Also, this paper highlights the shortcomings of the proposals in the literature, such as induced additional latency, so that they may not support the practical scenarios of Internet of Things (IoT). Further, this study highlights a gross shortcoming that relative performance comparison of RL-based proposals cannot be performed without using a benchmarking framework and real test-bed environment. In this paper, we have presented the future research directions that would lay the foundation for successful development of energy efficient RL-based duty-cycling techniques.
Web Log Analysis in Genealogy System

A large amount of log data is generated every moment in many real-world applications. System logs, mobile application logs, web logs etc., all of which contain rich information and can be analyzed for valuable information. At the same time, genealogy attracts more and more attention in recent decades, especially for Chinese. People utilize the genealogy system to seek roots, find source of family, build a family tree and so on. Due to the complexity of genealogy data and specific features of genealogy system, how to extract and analyze the valued genealogy information effectively is a huge challenge. Motivated by this, we propose a novel analysis method using web logs in genealogy system for drawing user profile and providing personalized recommendations. More specifically, firstly, all web logs are extracted from genealogy system based on the enhanced logs which record the behaviors and data of user. Secondly, the extracted logs are formatted for analysis, and we analyze the logs to extract various user-related attributes for drawing user profile. Finally, personalized recommendations like interested people and family tree are provided for each user in system. The high availability and effectiveness of our proposed method is verified on genealogy system-Huapu system.
Towards a Scaleable 5G Fronthaul: Analog Radio-over-Fiber and Space Division Multiplexing

The introduction of millimeter wave (mm-wave) frequency bands for cellular communications with significantly larger bandwidths compared to their sub-6 GHz counterparts, the resulting densification of network deployments and the introduction of antenna arrays with beamforming result in major increases in fronthaul capacity required for 5G networks. As a result, a radical re-design of the radio access network is required since traditional fronthaul technologies are not scaleable. In this article the use of analog radio-over-fiber (ARoF) is proposed and demonstrated as a viable alternative which, combined with space division multiplexing in the optical distribution network as well as photonic integration of the required transceivers, shows a path to a scaleable fronthaul solution for 5G. The trade-off between digitized and analog fronthaul is discussed and the ARoF architecture proposed by blueSPACE is introduced. Two options for the generation of ARoF two-tone signals for mm-wave generation via optical heterodyning are discussed in detail, including designs for the implementation in photonic integrated circuits as well as measurements of their phase noise performance. The proposed photonic integrated circuit designs include the use of both InP and SiN platforms for ARoF signal generation and optical beamforming respectively, proposing a joint design that allows for true multi-beam transmission from a single antenna array. Phase noise measurements based on laboratory implementations of ARoF generation based on a Mach–Zehnder modulator with suppressed carrier and with an optical phase-locked loop are presented and the suitability of these transmitters is evaluated though phase noise simulations. Finally, the viability of the proposed ARoF fronthaul architecture for the transport of high-bandwidth mm-wave 5G signals is proven with the successful implementation of a real-time transmission link based on an ARoF baseband unit with full real-time processing of extended 5G new radio signals with 800 MHz bandwidth, achieving transmission over 10 km of 7-core single-mode multi-core fiber and 9 m mm-wave wireless at 25.5 GHz with bit error rates below the limit for a 7% overhead hard decision forward error correction.
End-To-End Speech Emotion Recognition Based on Time and Frequency Information Using Deep Neural Networks

We propose a speech emotion recognition system based on deep neural networks, operating on raw speech data in an end-to-end manner to predict continuous emotions in arousal-valence space. The model is trained using time and frequency information of speech recordings of the publicly available part of the multi-modal RECOLA database. We use the Concordance Correlation Coefﬁcient (CCC) as it was proposed by the Audio-Visual Emotion Challenges to measure the similarity between the network prediction and gold-standard. The CCC prediction results of our model outperform the results achieved by other state-of-the-art end-to-end models. The innovative aspect of our study is an end-to-end approach to using data that previously was mostly used by approaches involving combinations of pre-processing or post-processing. Our study used only a small subset of the RECOLA dataset and obtained better results than previous studies that used the full dataset.
RF Energy Harvesting: Conducting an Urban and Suburban Outdoor Electromagnetic Site-Survey

The implementation and use of RF energy harvesting circuits has been demonstrated for many years, but only a few have been able to approach the use of the low power levels available in the ambient electromagnetic waves, produced by cell phone towers, digital television, WiFi, wireless equipment, etc. The objective of this paper is to discuss the factors to consider when an RF site survey is conducted, especially the details that are not so obvious when a handheld spectrum analyzer and antennas are used, and where the reading accuracy of each decibel is critical. It is also desired to present an overview of the results obtained for the outdoor measurements in urban and suburban areas. In this study around nine bands providing significant levels of power were found, with a maximum value detected of −23.34 dBm. All measurements were shared in a dataset through the IEEE Dataport.
Neural Collaborative Recommendation with Knowledge Graph

Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities’ long-term relational dependencies for the recommendation. In this paper, a two-channel neural interaction method named Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose a Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.
Learning to Predict Charges for Legal Judgment via Self-Attentive Capsule Network

With the rapid development of deep learning technology, more and more traditional industries are changed by Artiﬁcial Intelligence. The legal industry is such a popular scenario which attracts lots of researchers’ interests. In this work, we focus on automatic charge prediction, which predicts the ﬁnal charges according to the given fact descriptions in criminal cases. It is crucial for legal assistant systems and can help the judges improve work efﬁciency greatly. However, extremely imbalanced data distribution and lengthy fact descriptions make this task especially challenging. To tackle these two issues, we propose a novel model, namely Self-Attentive Capsule Network (dubbed as SAttCaps). In particular, we devise a self-attentive dynamic routing, which can not only capture long-range dependency more directly than vanilla dynamic routing, but also learn the high-level generalized features better. The experimental results on three real-world datasets demonstrate that our model signiﬁcantly outperforms the baselines and creates new state-of-the-art performance. Moreover, our model performs much better than the baselines especially in the low-frequency charges and can bring 5 . 7% absolute improvement under F1 score.
Hilbert-Huang Transform and the Application

The short-time fourier transform and wavelet transform are efficient non-stationary signal processing methods, but have their own limit. N.E. Huang put forward a new method, that is, to calculate the instantaneous frequency of the signal through empirical mode decomposition (EMD) of the signal and the Hilbert transform of intrinsic mode functions (IMF). This method was not accepted widely until it was proved to be efficient by a series of experiments. This article introduces the principle and insufficiency of Hilbert-Huang transform, puts forward several improved strategies, and proceeds some simulations.
Cognitive NOMA for UAV-Enabled Secure Communications: Joint 3D Trajectory Design and Power Allocation

In the paper, we investigate the physical layer security (PLS) of an unmanned aerial vehicle (UAV) enabled communication system that integrates non-orthogonal multiple access (NOMA) with cognitive radio (CR), namely CR-NOMA. Specifically, the secondary UAV transmits confidential information to multiple secondary legitimate receivers with NOMA by sharing the spectrum with the primary networks, while an eavesdropper (Eve) attempts to wiretap the communication. We aim to jointly optimize the three-dimensional (3D) UAV trajectory and power allocation to maximize the worst-case average secrecy sum rate of all secondary receivers (SRs) while keeping the interference to primal receivers (PRs) below a certain threshold. The formulated problem is mixed-binary non-convex and is generally NP-hard. To address this issue, we first equivalently transform the binary variables into continuous ones by introducing tractable inequality constraints. Then, we decompose the optimization problem into two subproblems and develop an efficient iterative algorithm based on the successive convex approximate (SCA) technique, block coordinate descent (BCD) technique, and penalty function method. Numerical results are extensively studied to show that the proposed joint design significantly outperforms the benchmark schemes in terms of secrecy rate.
A 10-bit 500 MSPS Segmented CS-DAC of > 77 dB SFDR upto the Nyquist with Hexa-decal biasing

This article demonstrates a 10-bit 500 MSPS segmented current steering digital to analog converter (CS-DAC), which is designed using CMOS 180 nm technology for wireless transmitter system. To obtain stringent linearity and high spurious-free dynamic range (SFDR) for such high speed and high-resolution DAC is the main design challenge. Considering this objective, the static and dynamic performances along with linearity of the DAC are improved using segmented architecture incorporating with CS-DAC structure. This segmented architecture negotiates between active area and high performance of the DAC. In this design, CS-DAC architecture maintains the 60% segmentation ratio, in which the 4-LSB bits are used in binary fashion and 3-LMSB bits along with 3-MSB input bits are utilized in unary structure. To improve the linearity and to mitigate the mismatch effect, an on-chip temperature and supply invariant voltage bias reference (VBR) circuit is proposed with hexa-decal biasing scheme. This technique minimizes the output glitches by reducing correlated noise generated from the bias cells. Using this architecture, the SFDR is calculated more than 77 dB up to near Nyquist input signal frequency of the 248.53 MHz. The CS-DAC consumes only 27.37 mW power at 18.06 MHz input frequency with 500 MSPS sampling rate.
The Investigation of the Silicon Fabricated Balanced Shunt Micro Pin Fins Cold Plate for High Heat Flux Devices

As an appropriate cooling solution for high heat flux device, the micro pin fin architecture can significantly enhance the fluidic uniformity and the nucleate boiling occurring inside the flow boiling cold plate. In this paper, three types balanced shunt micro pin fins (triangular, hexagonal, and circular) cold plates were fabricated by silicon processes. The fluidic spreading and the boiling states (bubble flow, slug flow, and annular flow) inside the Si-Glass cold plates could be distinguishably observed through the high-speed camera. The relationship between the local two-phase heat transfer coefficient inside the cold plate and the phenomenon of bubble states was investigated. As the results, the cusp regions of the triangular micro pin fins could promote the flow and mixing of the coolant to eliminate the slug flow. The triangular micro pin fins cold plate therefore had the best heat transfer characteristics and pressure drop performance. In addition, the Si-Si cold plate (40 mm  40 mm  1.3 mm) could satisfy 500 W total heat dissipation under 1000 W/cm2 local (4  25 mm2) heat flux density. The heat transfer coefficient of Si-Si cold plate was approximately 1.3 times higher than Si-Glass one.
Neural Network-Based Closed-Loop Deep Brain Stimulation for Modulation of Pathological Oscillation in Parkinson’s Disease

Aiming at the problem that the Proportional-Integral-Derivative (PID) control strategy needs to readjust controller parameters for different Parkinson’s disease (PD) states. This work proposes an improved control strategy that considers an artificial neural network control scheme. A backpropagation neural network (BPNN) controller is designed to solve the above problem and further to improve the performance of the closed-loop control strategy. The training data set of the BPNN controller is obtained by controlling eight different PD states (PDa - PDh) by the PID controller and the BPNN controller is trained by the training data set to obtain a set of optimal weights. By modulating other different PD states (e.g. PD1 - PD3), the effectiveness of the PID-structure controller and BPNN controller are compared. We find that the BPNN controller can modulate different PD states without changing the controller parameters and reduce energy expenditure by 58.26%. This work is helpful for the design of more effective closed-loop deep brain stimulation (DBS) systems for clinical applications and provides a framework for the further development of closed-loop DBS.
Distribution & Damage Characterisitics of Anoplophora glabripennis in Heilongjiang Province

Locations,tree species parts of trees which were damaged by Anoplophora glabripennis in Heilongjiang province were investigated.Result shows that Anoplophora glabripennis form serious damage in the surrounding region of Harbin city;the main damaged species are Populus charbinensis,Populus × xiaohei,Salix sp. ,Ulmus pumila,Betula platyphylla,Xanthoceras sorbifolia,Acer saccharum,Acer ginnala.Damaged parts are mostly concentrated in the middle of the tree.
Long Short-term Memory Network Prediction Model Based on Fuzzy Time Series

This paper proposes a long short-term memory network (FTS-LSTM) prediction model based on fuzzy time series to improve the prediction accuracy of time series. First, the fuzzy C-means clustering FCM algorithm is used to classify the time series to form a fuzzy time series and obtain the membership matrix. Second, the LSTM net-work prediction model is constructed, and the FTS-LSTM network prediction model is proposed. The previously obtained membership is used as the full connection. The weight of the layer and its membership as the weight remain unchanged. This FTS-LSTM network prediction model not only considers the non-linearity and non-stationarity of the time series, but also resolves the inherent uncertainty and ambiguity of the data. Simulation results show that the FTS-LSTM network-based prediction model has faster training speed, higher prediction accuracy, and better prediction effect on time series with large ambiguities.
An 18 mV Offset, 193 ps Sensing Delay, and Low Static Current Sense Amplifier for SRAM

A highly sensitive sense amplifier for static random access memory (SRAM) with a very low offset voltage, negligible static current, and low kickback noise is presented in this paper. We propose to use a preamplifier stage instead of positive feedback between the input and the sense amplifier to improve the sensitivity of the sense amplifier. The proposed sense amplifier is designed and simulated using 0.18 μm CMOS technology node. With a supply voltage of 1.4 V and an operating frequency of 1 GHz, the proposed sensing amplifier has a sensing delay of 193.2 ps, which is 85% better than conventional designs. The sense amplifier has an offset voltage of 18 mV.
Modelling Indium Interconnects for Ultra Fine-Pitch Focal Plane Arrays

Infrared (IR) detector chips and read-out integrated circuits (ROICs) are assembled most commonly through flip-chip bonding technologies and using indium solder interconnects. The resulting 3D stacked die structure is called Focal Plane Array (FPA). This paper details the development of analytical and finite element models of the FPA assembly for both reflow and thermo-compression bonding processes. The models are used to assess the process requirements and feasibility in relation to the IR sensor characteristics and bonding equipment placement accuracy. The results show that high-density ultra-fine pitch FPAs may be feasible to assemble only with thermo-compression bonding technology, and that this will still require very high precision placement machinery with chip alignment accuracy in the order of 1 μm or better. Quality of the formed indium joints and the resulting stand-off height are found to be very sensitive to the alignment of the IR detector chip onto the ROIC. Reflow bonding process is feasible only if the pitch of the pixel array matrix is above certain size. In this instance, the presented model of the chip motion during reflow can be used to predict if self-alignment of the assembled chips occurs.The thermo-mechanical behavior of the FPA under cryogenic temperature cycling load is also presented in the paper. Thermal fatigue damage of indium joints is found to be lower with higher resolution, smaller pitch size pixel array structures despite the larger land size of the detector chip. With detector chip thickness typically <10 μm, the warpage of the FPA is shown to be insensitive to the size of the pixel array.
Can model plants help banana improvement through biotechnology?

Bananas must cope with numerous environmental challenges, particularly with fungal and bacterial pathogens as well as pests and abiotic stresses. This situation is exacerbated by the limited diversity of cultivars. Moreover, traditional breeding strategies are problematic due to a low female fertility, sterility, ploidy and poor seed set. As a result, classical genetics is difficult and limited, as well as being extremely time consuming. Taken together, the difficulty in conventional beeeding, limited genetic diversity and poorly controlled diseases all point to the necessity of developing alternative strategies for banana improvement. Biotechnological approaches are particularly appropriate for this crop. This review will focus on two distinct, but overlapping issues: (i) the role of model plants in providing avenues leading to approaches for banana improvement through biotechnology and (ii) conceptual approaches for generating bananas with enhanced resistance to disease and other environmental stresses.
Zmiany dokonujące się w skali całego świata: globalizacja, rozszerzająca się otwar-

tość gospodarek, liberalizacja rynków, w tym 'nansowych, oraz ich szybka integracja, dynamiczny rozwój rynków kapitałowych i ekspansja kapitału prywatnego, gwałtowny rozwój technologii informatycznych i telekomunikacyjnych przyniosły szereg istotnych przewartościowań w zakresie celów przedsiębiorstw, koncepcji i praktyki zarządzania oraz priorytetów prowadzenia biznesu. Współcześnie mamy bowiem do czynienia nie tylko z olbrzymim zakresem zmian jakościowych, ale przede wszystkim z coraz mniej ewolucyjnym ich charakterem. Stają się one nieregularne, nieobliczalne i nieciągłe [26, s. 41]. Nie ulega wątpliwości, że w dzisiejszej perspektywie ekonomicznej system zarządzania wartością wymaga rewizji, zwłaszcza w kontekście rozwijania możliwości konkurowania o kapitał i przejmowania wartości powstającej na globalnym rynku. Jest to o tyle ważne, iż coraz wyraźniej zauważalna jest atomizacja koncepcji Value Based Management (VBM) i skupienie bardziej na zarządzaniu operacyjnym niż strategicznym, co sprawia, że cała koncepcja jest w pewnym sensie niekompletna, w niewielkim stopniu uwzględniająca współczesną architekturę globalnego rynku. Ponadto w koncepcji VBM intensywnie wykorzystywano i rozwijano m.in. : reengineering, outsourcing/o3shoring, customer relations management czy wiele innych, które z jednej strony poszerzają horyzonty patrzenia na proces tworzenia wartości,