Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted Conformal LiDAR-Mapping for Structured SLAM

One of the main challenges in simultaneous localization and mapping (SLAM) is real-time processing. High-computational loads linked to data acquisition and processing complicate this task. This article presents an efficient feature extraction approach for mapping structured environments. The proposed methodology, weighted conformal LiDAR-mapping (WCLM), is based on the extraction of polygonal profiles and propagation of uncertainties from raw measurement data. This is achieved using conformal M bius transformation. The algorithm has been validated experimentally using 2-D data obtained from a low-cost Light Detection and Ranging (LiDAR) range finder. The results obtained suggest that computational efficiency is significantly improved with reference to other state-of-the-art SLAM approaches.
Ruthenium and Rhodium Vertical Interconnect Formation Using Gas Phase Electrodeposition

This paper presents localized gas phase electrodeposition of ruthenium (Ru) and rhodium (Rh) species into vertical interconnects. A spark discharge generates gas ions and charged species of the desired metal, which are transported by a gas flow and form a plasma jet. Prior lateral nano-bridge growth is further developed and enables the localized metal species deposition into vertical interconnect openings. This approach is additive and saves rare materials during processing. The process allows precise adjustment of the diameter, airgap size, and top finishing bump of the vertical interconnect.
Drinking Heated Water Improves Performance via Increasing Nutrient Digestibility and Ruminal Fermentation Function in Yak Calves

Simple Summary This study aims to explore whether drinking heated water is able to increase the performance of yak calves. We collected and analyzed data from a two-month experiment on yak calves. The results show that compared with drinking cold water, drinking heated water significantly improved the apparent total tract digestibility (ATTD) and ruminal fermentation function. In addition, we found that heated water significantly improved the immune function of the body through serum indexes, and the nutrients in the blood were significantly increased compared with cold water. Furthermore, the serum hormones were also changed by heated water, with higher levels of hormones related to growth. Overall, drinking heated water in the cold season could increase the performance of yak calves in terms of better ATTD, ruminal function and immunity. Abstract This study was conducted to investigate the effects of heated water intake on the growth performance, serum biochemical indexes, apparent total tract digestibility (ATTD) of nutrients and ruminal fermentation function of yak calves in winter. A total of 24 yaks (59.09 ± 3.181 kg) were randomly selected and divided into a cold water (fluctuated with the temperature of test sites at 0–10 °C) group (CW) (58.58 ± 3.592 kg) and a heated water (20 °C) group (HW) (59.61 ± 2.772 kg). After 2 months of the experiment, body weight, serum biochemical indexes, ruminal fermentation characteristics and ATTD were measured. The results showed that drinking heated water increased (p < 0.05) the total weight gain and average daily gain of yaks compared with those drinking cold water. Heated water increased (p < 0.05) the levels of immune globulin M, interleukin-6, triiodothyronine, tetraiodothyronine and growth hormone compared with cold water. In addition, yaks drinking heated water showed higher (p < 0.05) ATTD of crude protein and ether extract, as well as increased (p < 0.05) content of total protein, albumin and urea nitrogen in serum than those drinking cold water. Compared with cold water, heated water showed increased (p < 0.05) total volatile fatty acids, acetic acid and propionic acid, and a reduced (p < 0.05) acetic acid to propionic acid ratio (p < 0.05). In conclusion, drinking heated water at 20 °C could improve performance via increasing nutrient digestibility and ruminal fermentation function in yak calves.
Measurement and Optimization of Repetition Scheme in NB-IoT Uplink

Narrowband Internet of Things (NB-IoT) is an low-power wide area network based on cellar architecture. The repetition scheme is a key solution to achieve enhanced coverage with low complexity in the uplink. However, the impact of the current repetition scheme on energy consumption and coverage performance of NB-IoT are still unclear. In this paper, we conduct field measurements of the repetition scheme in terms of energy efficiency. We find that most of repetition values configured by the eNodeB lead to non-optimal energy efficiency. Then we propose an adaptive repetition scheme based on a regression block delivery rate (BDR) model which can be derived from a theoretical model and a small number of measurements. We conduct simulations based on real-world measurement data. The results show that the proposed adaptive repetition scheme outperforms the default repetition scheme in both energy efficiency and data transmission rate.
Exposing Adaptive Cruise Control in Advanced Driving Assistance Systems

Advanced Driving Assistance Systems (ADAS) are expected to improve the urban driving experience by reducing accidents and enabling safe driving. Such systems provide different features to automate driving and ease the driver's tasks. ADAS features highly depend on the data received from in-vehicle networks, such as Controller Area Network (CAN) bus. CAN play an important role in in-vehicle communication by enabling efficient data exchange among electronic control units (ECUs). However, the CAN bus is vulnerable to different attack strategies, which can manipulate CAN data and cause fatal consequences on ADAS features. This paper shows how CAN bus data can be exploited to expose an industry-grade advanced driving assistance system's Adaptive Cruise Control (ACC) feature. The attack technique demonstrates the requirements of timely human intervention or automatic recovery techniques. We also provide a detection mechanism based on the high-definition vector map and Global Positioning System device installed in the vehicle.
An Urban Electric Load Forecasting Model Using Discrepancy Compensation and Short-term Sampling Contrastive Loss in LSTM

Urban electric load forecasting is an important content of urban electric system planning and dispatching. However, the problem of data imbalance in urban electric load forecasting leads to poor performance of single-model based methods. Existing multi-models based methods not only increase the cost of constructing models but also separate common time series features among different electric load profiles samples. In this paper, we propose an urban electric load forecasting model (DCSC-LSTM), which introduces the discrepancy compensation module and the short-term sampling contrastive loss to the Long Short-Term Memory. The DCSC-LSTM model uses the discrepancy compensation module to learn the discrepancies between samples with different electric load profiles, and the short-term sampling contrastive loss to regularize the training of the model. A series of experiments were conducted to validate the design of the DCSC-LSTM model. The experiments results show that the DCSC-LSTM model achieves competitive performance in the task of forecasting electric load.
Physics-Driven Probabilistic Deep Learning for the Inversion of Physical Models With Application to Phenological Parameter Retrieval From Satellite Times Series

Recent Sentinel satellite constellations and deep learning methods offer great possibilities for estimating the states and dynamics of physical parameters on a global scale. Such parameters and their corresponding uncertainties can be retrieved by machine learning methods solving probabilistic inverse problems. Nevertheless, the scarcity of reference data to train supervised methodologies is a well-known constraint for remote sensing applications. To address such limitations, this work presents a new generic physics-guided probabilistic deep learning methodology to invert physical models. The presented methodology proposes a new strategy to combine probabilistic deep learning methods and physical models avoiding simulation-driven machine learning. The inverse problem is addressed through a Bayesian inference framework by proposing a new physically constrained self-supervised representation learning methodology. To show interest in the proposed strategy, the methodology is applied to the retrieval of phenological parameters from normalized difference vegetation index (NDVI) time series. As a result, the probability distributions of the intrinsic phenological model parameters are inferred. The feasibility of the method is evaluated on both simulated and real Sentinel-2 data and compared with different standard algorithms. Promising results show satisfactory accuracy predictions and low inference times for real applications.
CNN-SVM for prediction Alzheimer disease in early step

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that first affects memory functions and then progressively affects all cognitive functions with behavioral deficiencies and possibly causing death. As the number of patients has increased, its early diagnosis is an objective to be achieved. Nowadays, deep learning, and more particularly transfer learning, is now achieving spectacular performance, unhoped for a few years ago, in areas such as computer vision and the processing of the medical image. In this context, we propose a new study for the early prediction of Alzheimer's disease can be performed using the Magnetic Resonance Imaging (MRI) image and Convolutional Neural Network (CNN). Our contribution was to merge, transfer learning and Support Vector Machine (SVM) technologies to predict Alzheimer's disease at an early stage; the Mild Cognitive Impairment (MCI). We test our method with the collection of OASIS database brain specialized to analyze Alzheimer MRI with 420 subjects; 210 normal and 210 MCI. Our system gave us 94.44% accuracy.
Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning

Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively.
Crowdsourcing Tasks Allocation based on Incentives via Group Multirole Assignment

Motivation is a key factor in improving the enthusiasm and work performance of crowdsourcing workers. It is important for crowdsourcing platforms to allocate tasks. In order to increase the enthusiasm of workers and thus improve their work performance, this paper formalizes the Crowdsourcing Tasks Allocation (CTA) based on incentives and proposes an adaptive task allocation method with Role-Based Collaboration (RBC) and its E-CARGO model by considering the dynamic changes of the weights of the evaluation factors and the evaluation values. Large-scale randomized experiments show that this method is effective and reliable, which can maximize the performance of the team. More importantly, it can motivate workers under the influence of multiple incentive factors and the environment of real-time growth of crowdsourcing tasks.
MIPS and RISC-V: Evaluating Virtualization Trade-off for Edge Devices

In the run to improve security on Edge Devices, separation by software appears as a well-known technique that can be achieved by different means. In this work, we consider virtualization as a solution for separation. A virtualization layer has an intrinsic relationship with the underline hardware, where the processor's architecture plays an essential role in the achieved security and software complexity. In this way, we discuss the architectural differences between MIPS and RISC- V processor families regarding virtualization support, showing how the archi-tectural differences may affect a hypervisor's implementation in terms of performance and security. Finally, we expect that this study clarifies the existing trade-offs for virtualization and helps choose the hardware and software stacks for edge devices.
Secure State Estimation Under Actuator and Sensor Attacks Using Sliding Mode Observers

Interconnections in modern systems make them vulnerable to adversarial attackers both by corrupting communication channels and compromising entire subsystems. The field of secure state estimation (SSE) aims to provide correct state estimation even when an unknown part of the measurement signals is corrupted. In this letter, we propose a solution to a novel generalized SSE problem in which full subsystems can be compromised, corrupting both the actuation and measurement signals. For a full system with <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> measurements, the proposed sliding mode observer (SMO)-based solution allows for up to <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> attack channels which can be arbitrarily distributed amongst attacks on actuation and measurement signals. This is a much larger class of attacks than considered in the existing literature. The method is demonstrated on 10 interconnected mass-spring-damper subsystems.
RAINBOW: Multi-Dimensional Hardware-Software Co-Design for DL Accelerator On-Chip Memory

Deep Learning (DL) is developing at an extremely fast pace. The increased number of applications, optimizations and hardware devices available, results in a multi-dimensional design space where the best performance is achieved with a detailed analysis of the hardware-software co-design process. Furthermore, the high demands for memory and the off-chip latency cost result in the on-chip memory becoming critical for achieving high performance and efficiency. In this work, we propose RAINBOW, a tool to assist in the hardware-software co-design for DL accelerators’ on-chip memory. The purpose is to help the design and/or deployment of a DL model to a dedicated accelerator. RAINBOW generates different analyses results and feeds them to the optimizers. The result is a heterogeneous execution plan combining different approaches and techniques depending on the dynamic requirements and constraints. In our analysis, we concluded that given the opportunity, RAINBOW’S heterogeneous plans are able to reduce the DRAM accesses to approximately half when compared to homogeneous plans.
Improving Wireless Power Transfer Efficiency Considering Rectifier Input Impedance and Load Quality Factor

This paper presents a method to maximize power transfer efficiency (PTE) in wireless power transfer (WPT) systems with low-quality factors merely based on tuning the receiver-side components. The paper also suggests an algorithm to find the optimum components values considering the effect of input impedance of receiver-side rectifier at high-frequency (MHz) applications such as portable electronic devices. Existing methods often consider the input impedance of the full-bridge rectifier as a pure resistance at low frequencies. However, at high frequencies, the complex impedance of the rectifier should be taken into account since it impacts on the components optimum values to maximize PTE. Despite conventional maximizing PTE methods for series-series and series-parallel topologies, this paper shows that a series-parallel WPT system with low load quality factor requires adjusting the resonant capacitor in addition to resistive load. The validity of the proposed method is verified based on numerical simulations and experiment tests using a 100 mW cm-scale prototype of a resonant inductive link at 6.78 MHz with varying distance between coils. The test results show a PTE improvement up to 40% in a series-parallel WPT compared with the methods that only tune the load equivalent resistance.
A Metadata-Based Approach for Research Discipline Prediction Using Machine Learning Techniques and Distance Metrics

Forecasting research disciplines associated with research projects is a significant challenge in research information systems. It can reduce the administrative effort involved in entering research project-related metadata, eliminate human errors, and enhance the quality of research project metadata. It also enables the calculation of the degree of interdisciplinarity of these projects. However, predicting scientific research disciplines and measuring interdisciplinarity in a research endeavor remain difficult. In this paper, we propose a framework for predicting the research disciplines associated with a research project and measuring the degree of interdisciplinarity based on associated metadata to address these issues. The proposed framework consists of several components to improve the performance of research disciplines prediction and interdisciplinarity measurement systems. These include a feature extraction component that utilizes a topic model to extract the most appropriate features. Further, the framework proposes a discipline encoding component that applies a data mapping strategy to lower the dimensionality of the output variables. Furthermore, a distance matrix creation component is proposed to recommend the most appropriate research disciplines and compute interdisciplinarity associated with research projects. We implemented the suggested framework on two separate research information systems databases for research projects, Dimensions and the Flemish Research Information Space. Experimental results demonstrate that the proposed framework predicts the research disciplines associated with research projects more accurately than related work.
Adaptive Weighted Low-Rank Sparse Representation for Multi-View Clustering

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation. We consider the importance of each view by assigning the weight control factor. We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange’s Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority.
A Matheuristic-based Rescheduling Method for Flexible Job Shops with Lot-streaming and Machine Reconfigurations

This paper studies a flexible job shop rescheduling problem with lot-streaming and machine reconfigurations (FJRP-LSMR) to minimize the sum of the instability and total weighted tardiness, where machine reconfigurations are performed by assembling selected auxiliary modules for processing different batches of products. In this case, a rescheduling process is triggered by dynamic events, and requires to determine the lot-sizing plan, machine assignment, and sublot sequencing simultaneously. To address the intractable problem with multiple decision-making processes, a matheuristic integrating the genetic algorithm (GA) and the mixed integer linear programming (MILP) technique is proposed, where an MILP model is developed for optimally solving the lot-sizing sub-problem, and is embedded to the GA as a local search function. The proposed matheuristic is tested on randomly-generated instances to investigate the performance of all the algorithmic components. Experimental results demonstrate that the GA representation is effective in the complicated dynamic scheduling problem, and the lot-sizing sub-problem can be well addressed by the proposed MILP-based local search.
Write-Energy Relaxation of MTJ-Based Quantized Neural-Network Hardware

This paper evaluates WKH bit-error tolerance of quantized neural networks (QNNs) for energy-efficient artificial intelligence (AI) applications utilizing stochastic properties of magnetic tunnel junction (MTJ) devices. Since QNNs have potentially high bit-error tolerance, they do not require large write currents to guarantee the certainty of the information held in the MTJ devices. By artificially adding bit errors to their weights, it is demonstrated that QNNs with binarized data representation achieve better error tolerance than any other ones in terms of the degradation rate of the recognition accuracy. In addition, based on the evaluation results, we show the possibility of reducing the write energy of MTJ devices up to 42% by exploiting high bit-error tolerance of the binarized QNN.
Coherence and superfluidity of Bose gases in reduced dimensions: from harmonic traps to uniform fluids

The dimensionality of a system strongly affects its physical properties; the phase transitions that take place and the type of order that arises depend on the dimension. In low dimensional systems phase coherence proves more difficult to achieve as both thermal and quantum fluctuations play a stronger role. The two-dimensional Bose fluid is of particular interest as even if full order is precluded, a residual "quasi-long" range order arises at low temperatures. Then two ingredients have a significant effect on the state of the system: (i) the finite size of a real system enables one to recover of a macroscopic occupation of a single-particle state; (ii) the interactions between particles lead to the emergence of a non-conventional type of phase transition toward a superfluid state. In this thesis, we present an experimental study of the two-dimensional (2D) Bose gas using two different energy landscapes to trap our atoms. In the first part, we use the spatial dependence of some local properties of an inhomogeneous gas to characterize the state of the equivalent homogeneous system. We extract its equation of state with a high accuracy from the gas density profiles and test its superfluid behavior by measuring the heating induced by a moving local perturbation. In the second part, we observe and characterize the emergence of an extended phase coherence in a 2D homogeneous gas in particular via a 3D-to-2D dimensional crossover. We investigate the dynamical establishment of the coherence via a rapid crossing of the dimensional crossover and observe topological defects in the final superfluid state. We compare our findings with the predictions for the Kibble–Zurek mechanism.