Miracle-dz/UniFAR-FTUs huggingface dataset:
Access-Aware Self-Adaptive Data Mapping onto 3D-Stacked Hybrid DRAM-PCM Based Chip-Multiprocessor

The number of cores on chip-multiprocessor is increasing due to the higher computation demand and technology advancement. Therefore, chip-multiprocessor requires larger on-chip memory as well as a high-end interconnection network. DRAM memory architect using 3D-stacked technology is a promising candidate; however, as the DRAM memory size increases, it suffers due to the overheads related to the DRAM row refreshes. Larger capacity hybrid memories architect using DRAM and PCM technology is a good solution to tackle this problem. Moreover, to utilize the benefits of the large capacity 3D-stacked hybrid memory, studies need to consider the data placement and memory management schemes for these memories. In this work, we have considered a 3D-stacked hybrid DRAM-PCM memory based chip-multiprocessor system. Moreover, we performed an access-aware self-adaptive run-time page mapping to reduce the remote access latency as well as DRAM refresh overheads of the chip-multiprocessor. Our technique performs on an average of 4% better (in terms of the execution time) as compared to the base-case (in base-case, chip-multiprocessor uses non-coherent 3D-stacked DRAM only memory). Whereas, energy consumption due to the DRAM refresh reduced by an average of 49% as compared to the base-case.
Investigation of the Impression of Storytelling with Robots to Multiple Children

The effects of using multiple robots—a reader robot and a listener robot as a side-participant—for group interaction in storytelling to children are addressed. We developed a storytelling system consisting of a reader robot, a listener robot, a display, gaze models of both robots, a depth sensor, and a human operator who gives correct and comprehensible responses to children’s questions. In the case of storytelling to two children, we examined whether the children enjoyed the storytelling and the impression of parents who observed the experiments. Experimental results showed that children enjoyed the storytelling through our system and parents conveyed a good impression.
Joint Job Offloading and Resource Allocation for Distributed Deep Learning in Edge Computing

Under the paradigm of Edge Computing, the enormous data generated at the network edge can be processed locally. Machine learning methods are often adopted to make full utilization of these data, among which the deep learning is a promising one. When considering the inherent distributed feature of these data, it is appeal to conduct distributed deep learning tasks directly at the edge. We focus on an edge computing system that conduct distributed deep learning tasks using gradient descent based approaches. To ensure the system's performance, there are at least two major challenges to cope with: how to offload the training jobs with multiple data source nodes and how to allocate the limited resources on each edge server among training jobs. In this paper, we jointly consider the two challenges, aiming to maximize the system throughput while ensuring the system's quality of service (QoS). We formulate the joint problem as an integer non-linear program and propose an efficient approximation algorithm based on reformulation and randomized rounding technique. Simulation results prove that the proposed algorithm can improve 56% of the system throughput and 53% of resource utilization when compared to the conventional baseline algorithms.
Efficient Parallel Sort on AVX-512-Based Multi-Core and Many-Core Architectures

Sorting kernels are a fundamental part of numerous applications. The performance of sorting implementations is usually limited by a variety of factors such as computing power, memory bandwidth, and branch mispredictions. In this paper we propose an efficient hybrid sorting method which takes advantage of wide vector registers and the high bandwidth memory of modern AVX-512-based multi-core and many-core processors. Our approach employs a combination of vectorized bitonic sorting and load-balanced multi-threaded merging. Thread-level and data-level parallelism are used to exploit both compute power and memory bandwidth. Our single-threaded implementation is ~30x faster than qsort in the C standard library and ~10x faster than C++'s std::sort. Compared with the Intel Performance Primitives (IPP) library which is one of the most efficient CPU-based radix sort implementation, we obtain a speedup of 1.3 to 2.6. Furthermore, we achieve a peak performance of sorting 1.14 billion floats per second on a Xeon Phi 7210 processor. Moreover, we show the extensibility of our vectorized kernels to processing units with a varying of vector lanes.
Hermes: Improving Server Utilization by Colocation-Aware Runtime Systems

Improving server utilization is increasingly important to service providers. Latency-critical services have strict tail latency service-level objects and safe colocation of the latencycritical service with other workloads on the same machine is difficult. This would underutilize server resources. We present Hermes, a user-level resource management layer to address this dilemma. We implement two kinds of runtime systems in Hermes, one for latency-critical workloads (LC runtime) and one for besteffort workloads (BE runtime). LC runtime implements userlevel thread management and controls the dedicated computing resources occupied by latency-critical workload through a feedback-based controller. BE runtimes schedule threads of besteffort workloads to take advantage of simultaneous multithreading technology. Runtime systems are aware of their colocation and work in a cooperative approach to improve server utlization without violating the tail latency service-level objects of the latency-critical workload. Hermes is implemented entirely at user-level on Linux. We evaluate Hermes using memcached and several sythetic micro-benchmarks, the result shows that Hermes could achieve both safe colocation and improvement of core utilization.
Adaptive Fault Estimation and Fault-Tolerant Control for Nonlinear System With Unknown Nonlinear Dynamic

This paper investigates the problems of fault estimation and fault-tolerant control for nonlinear system. The nonlinear part of the system is assumed to be unknown. Based on the adaptive approximation technique, which can be performed by fuzzy logic systems or neural networks, an adaptive fault estimation observer is designed, and the fault and the system state can be estimated simultaneously. Based on the estimation information, the observer-based fault-tolerant controller is designed. In this paper, the parameter matrices of the adaptive law and the observer and the controller can be calculated by slowing LMIs. To verify the proposed scheme, a simulation example is provided at the end of this paper.
Enhanced Performance of a Phosphorescent White LED CAP 64QAM VLC system utilizing Deep Neural Network (DNN) Post Equalization

In this paper, a phosphorescent white LED CAP 64QAM VLC system utilizing deep neural network (DNN) and LMS linear equalization (LE) has been experimentally demonstrated. We successfully achieve a data rate of 2.4Gb/s at a 1.1-m indoor free space transmission with the bit error rate (BER) below the 7% forward error correction (FEC) limit of 3.8 × 10-3. Compared to the LE and volterra NLE+LE, DNN+LE brings a better performance to mitigate nonlinear distortion. The results show that DNN+LE will be a promising solution for indoor high-speed VLC system
Obfuscatable Anonymous Authentication Scheme for Mobile Crowd Sensing

Mobile crowd sensing (MCS) is a rapidly developing technique for information collection from the users of mobile devices. This technique deals with participants’ personal information such as their identities and locations, thus raising significant security and privacy concerns. Accordingly, anonymous authentication schemes have been widely considered for preserving participants’ privacy in MCS. However, mobile devices are easy to lose and vulnerable to device capture attacks, which enables an attacker to extract the private authentication key of a mobile application and to further invade the user's privacy by linking sensed data with the user's identity. To address this issue, we have devised a special anonymous authentication scheme where the authentication request algorithm can be obfuscated into an unintelligible form and thus the authentication key is not explicitly used. This scheme not only achieves authenticity and unlinkability for participants, but also resists impersonation, replay, denial-of-service, man-in-the-middle, collusion, and insider attacks. The scheme's obfuscation algorithm is the first obfuscator for anonymous authentication, and it satisfies the average-case secure virtual black-box property. The scheme also supports batch verification of authentication requests for improving efficiency. Performance evaluations on a workstation and smart phones have indicated that our scheme works efficiently on various devices.
Measuring the Three-Dimensional Volume Scattering Functions of Microsphere Suspension: Design and Laboratory Experiments

The measurement of the volume scattering function (VSF) of suspended particles is an ongoing research topic since it has the potential to provide a rapid, nondestructive means of analyzing hydrosols. Despite the success of scattering theory, the measurement of multidirectional scattering intensity distributions over a wide, continuous range of scattering angles and multiple scattering planes, which is described by a three-dimensional VSF (3D VSF), has proven to be difficult. To measure the 3D VSF of submicron particles suspended in water, a measurement approach is proposed, built and tested with several standard submicron particles with diameters ranging from 60 nm to 1 μm. The experimental results indicate that the proposed setup can obtain an accurate 3D VSF for scattering angles from 21° to 160° and azimuthal angles from 1° to 179°. Comparisons between the experiment and theory show a high level of consistency for particles with diameters less than 200 nm. The variation in the VSF in response to the polarization direction of the incident light is also discussed. Both the experiment and theory show the potential of the device to detect the 3D VSF of marine submicron biological particles.
GO-APSR: A Globally Optimal Affine Point Set Registration Method

Point set registration under affine transformation is an important problem in computer vision because not only it has many direct applications, but it is also often used as an initial step for non-rigid registration. This problem is challenging when no correspondences between the two point sets are known, and most existing methods start from an initial pose and find a local optimal transformation. This paper presents a deterministic global optimization method for affine point set registration, which is called GO-APSR. We model the two sets to be registered with Gaussian Mixture Models (GMMs) and minimize the L2 distance between the two GMMs under affine transformation. Branch-and-Bound (BnB) is employed to search the transformation parameter space, and we propose a convex quadratic function as the under-estimator of the objective function in each branch. Therefore, calculation of the lower bound in each branch is casted into a bound-constrained convex quadratic programming problem, which can be solved globally and efficiently. Experiment results verify the global optimality of the proposed method and its robustness to noise and outliers. Furthermore, it works very well in the challenging partially-overlap scenarios.
Base-Station Antennas in Wireless Communications: A Low-Profile-Loop, Resonator-Loaded Broadband Antenna [Wireless Corner]

A new low-profile antenna is proposed for modern base stations. The low profile of 0. 164λ0 (λ0 denotes the free wavelength corresponding to the center operating band) is obtained by solving the problems introduced by profile reduction. The concept of an L-shaped radiator-coupling feed-loop resonator is proposed. A new resonance is induced at lower frequency by the loop resonator. The measured results show a wide impedance bandwidth of 33.6% for a voltage standing-wave ratio (VSWR) of less than 1.7 (698–980 MHz) and isolation of more than 30 dB. Stable radiation patterns and gain are achieved over the desired band. In addition, a five-element, low-profile antenna array with different electrical downtilts of 0°–14° is developed for the modern base station. A prototype has been fabricated to verify the proposed design. The antenna array achieves a wide impedance bandwidth of 34.7% (690–980 MHz) for VSWRs of less than 1.7 and stable half-power beamwidth (HPBW) (3-dB beamwidth) of 66° ± 6° at the H-plane (YOZ-plane); antenna gain of 14.2 ± 0.9 dBi is achieved with electrical downtilts of 0°, 7°, and 14°. Owing to these broadband low-profile advantages, the proposed antenna is a promising choice for modern 5G wireless applications.
Path-Based Routing and Wavelength Assignment for Multiple Multicasts in Optical Network-on-Chip

Optical Network-on-Chip (ONoC) is emerging as a key on-chip communication architecture for multiprocessor systems. While multicast communication commonly exists in various parallel applications for Chip Multi-Processors (CMPs), most existing studies for multicast in an ONoC only consider the optimization of one multicast. This limits the practical applications because real systems often have to handle multiple multicasts requested from various applications. In this paper, we investigate the wavelength assignment strategies for multiple multicasts based on path-based routing in an ONoC, with the objective of reducing the number of wavelengths required. We first propose an intra-multicast wavelength assignment strategy including two different wavelength assignment methods, Multicast with Single Wavelength (MSW) and Multicast with Multiple Wavelengths (MMW), which are two configurations by assigning a single wavelength or multiple wavelengths to the same multicast, respectively. Then, two modified conflict graphs are proposed to evaluate the wavelength requirements of MSW and MMW. By analyzing the distribution of different multicasts, we further design a Layer-based Wavelength Assignment algorithm for Multiple Multicasts (LWAMM) to implement the inter-multicast wavelength assignment. The key idea is to allocate non-overlapping paths to the same layer (a detailed explanation will be given in Section IV) that is logically duplicated from the given ONoC, and assign the same wavelength to the paths in the same layer. Since the number of wavelengths is equal to the number of layers, it can be reduced by allocating all paths to as few layers as possible. Finally, simulation results verify our theoretical results, and show that LWAMM can reduce the average number of wavelengths used by 11.31%, 15.1%, and 17.7%, respectively, when the multicast ratios are 30%, 50%, and 90%, compared to other path-based schemes.
Optimal Control for Time and Energy Minimization in the Trajectory Generation of a Mobile Robot

Trajectory generation for the point to point motion of wheeled mobile robots can be obtained using different approaches, usually with a pure geometric focus and classical controllers at the low or high level, with Cartesian coordinates or some specific coordinate transformations. Most of these approaches only consider the geometric path, without providing specific constraints on the robot energy or on the time it takes to move from the initial point to the goal. This paper presents a numeric optimal control approach that takes into account the minimization of energy as well as the final time optimization, while using the nonlinear robot kinematics throughout the motion. This approach is directly derived from the optimality conditions, turning the optimal control problem into a boundary value problem that can be solved with state-of-the-art numeric solvers. The fast computation of the undertaken approach can be used to recompute the trajectory online, acting like an MPC that indirectly closes the feedback loop. The approach has been tested in different configurations and the results show the successful application on a non-holonomic robotic structure.
PROFPRED: A Compiler-Level IR Based Performance Prediction Framework for MPI Industrial Applications

The performance prediction of parallel applications is important in high performance computing. It can discover performance bottlenecks, assist the optimization of scientific and industrial applications, and guide the job scheduling. This paper proposes PROFPRED a compiler-level IR based framework designed to predict computation and communication performance of MPI parallel industrial applications on large-scale target systems using small subsets or small-scale prototypes of target systems. We demonstrate this integrated set of techniques in our framework on Taub cluster and TianHe-2 supercomputer with seven NAS Parallel Benchmarks (NPB) and two real-world large-scale parallel applications, including CGPOP, and ASCI Sweep3D. The core algorithms of these selected benchmarks are widely used in industrial applications. The median prediction errors for these experimental applications range from 0.35% to 11.61%, and the average error is 4.28%. Compared to traditional regression-based prediction approach, PROFPRED presents better accuracy for predicting the performance of parallel applications with large scales.
An Analysis of Holistic Tail Latency Behaviors of Java Microservices

Long-tail latency values significantly affect the user experience and hence they are a major concern in modern systems. However, tail latency characteristics of applications developed using microservices architecture are still unknown. In this paper, we focus on analyzing and characterizing the behaviour of tail latency values of microservices workloads under service's peak sustainable throughput with a closed system model. Our particular focus is to investigate the impact of heap size, garbage collection, concurrency and service demand on the tail latency. Our study has two parts. First, we develop and experiment on four microservices micro benchmarks representing CPU bound, network I/O bound, memory bound and database I/O bound to find the tail characteristics under different concurrency, heap, service demand and garbage collector. Then, in the second part, we explore the tail latency characteristics identified on micro benchmarks by testing those conditions using standard microservices benchmark application, Socks Shop. We observe first that microservices latency distributions exhibit the power law behavior. Second, we systematically prove that microservices workload distributions do not exhibit the heavy tailed behavior. We show that increasing the level of concurrency and service demand increases the tail latency. We use queuing simulation to prove our claims. Finally, we show that tail latency values of memory bound and database I/O bound microservices workloads are correlated with heap size and garbage collection.
Analysis of Digital Current Controllers for Single- Phase Grid-Connected Photovoltaic Inverters

This paper presents comparative analysis among digital current controllers for single-phase grid connected photovoltaic inverters with L-filter. The considered controllers are proportional and integral controller (PI), proportional and integral plus resonant controller (PI+Res), deadbeat control (DBC) and model predictive control (MPC). The considered parameters for analysis are the total harmonic distortion (THD) for the injected grid current, phase and amplitude error in steady-state and during power reference step change. Simulations were made through Matlab/Simulink® platform to guide novel projects.
Training of Artificial Neural Network Using PSO With Novel Initialization Technique

Artificial neural networks (ANN) have been widely used in the field of data classification. Normally, training of neural network is applied with the traditional back propagation technique. As, this approach has various drawbacks, training of neural network is done with Particle Swarm Optimization (PSO). PSO has been widely used to solve the diverse kind of optimization problems. Population initialization performs a significant role in meta-heuristic algorithms. This paper describes a new initialization population approach Log Logistic termed as PSOLL-NN to create the initialization of the swarm. The proposed algorithm has been tested for weight optimization of feed forward neural network; and compared with back propagation Algorithm (BPA), standard PSO (PSO-NN), PSO initialized with Halton Sequence (PSOH-NN), Torus sequence (PSOT-NN) and Sobol sequence (PSOS-NN). The experimental results show that the proposed technique performed exceptionally better than the other traditional techniques. Moreover, the outcome of our work presents a foresight that how the proposed initialization technique can be used as an efficient alternative to standard training approaches for the data classification problems.
Non-Orthogonal Coded Access Based Uplink Grant-Free Transmission for URLLC

The Ultra-Reliable and Low-Latency Communication (URLLC) for the fifth generation (5G) telecommunications is challenged by its high requirements on latency and reliability. Grant-free (GF) transmissions are known to be beneficial to reduce the latency for uplink by omitting the process of scheduling request and grant configuring. However, the reliability cannot be guaranteed due to the possible collisions among users. In this work, we propose uplink GF transmissions with Non-Orthogonal Coded Access (NOCA) for sporadic short packets withProactive retransmission scheme to meet URLLC requirements. The NOCA system contains the symbol spreading with low cross-correlation QPSK-based sequences at the transmitter and the Parallel Interference-Cancellation (PIC) mechanism at the receiver. System level simulations of NOCA are carried out to evaluate the possibility of employingReactive and Proactive retransmission schemes for URLLC services. The simulation results support that along with theProactive transmission scheme, NOCA can reduce the packet loss rate while achieving low latency. A spreading length adaptation strategy is also put forward for the proposed scheme in different scenarios.
The Structure Determined of Low Ill-condition Structure Constraint Spatial Modulation for Massive Multi-Color MIMO-VLC

Massive multi-color multiple-input multiple-output visible light communications (m-MCMIMO VLC) system sufficiently utilizing spatial resources is proposed to satisfy super-speed and high-density depth-coverage requirements of next generation indoor data connections. While high inter-channel interference and a gap between the number of LEDs and PDs are main factors limiting system performance. The activated LEDs layout is constrained to the structure with low ill-condition channel gain matrix to increase the system detection algorithm performance, and activated LEDs number matches the PDs. Based on the idea, low ill-condition structure constraint spatial modulation (LISCSM) taking the condition number as the objective function is proposed greatly dealing with the bottlenecks. This letter determine the constraint structure of LISCSM through comparing the circle closed structure with linear, because of the requirements for uniform illumination of LED layout in space. In this paper, the channel state information is known at the transmitter. The fitting curve of the optimal parameter of each structure and the channel parameters is obtained by simulating. And the circle closed structure performs better at wide suitability and BER performance than linear structure under the channel-adapted optimal parameter, so the circle closed structure determined to be the constraint structure of LISCSM.