Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping the Landscape of SLAM Research: A Review

Multiple publications have arisen from over three decades of research in the field of simultaneous localization and mapping (SLAM), overwhelming those who wish to delve into this area. The extensive body of work in SLAM has resulted in an abundant and, at times, confusing flow of data, lacking a straightforward explanation of the underlying principles. This article aims to address this issue by providing a clear overview of the general taxonomy of the SLAM universe, assuming the reader is completely unfamiliar with the subject area. As cameras remain the primary sensor choice for SLAM, and considering the vast number of articles available on this topic, special emphasis will be placed on Visual SLAM. Additionally, we will delve into the influence of artificial intelligence on the development of new algorithms. The article incorporates comparative tables to enable readers to assess system performance using benchmark datasets. Moreover, it offers insights into current trends and prospective pathways within the subject area.
Predictive Flow Load Profiling for Intelligent Network Routing Decisions

Since traffic patterns are highly dynamic, ensuring efficient load distribution in network architectures providing multiple paths between a pair of edges is a challenging task. Routing methods like Equal-Cost Multi-Pathing (ECMP) cannot take load state conditions into account during path determination for arising network flows. This behavior can lead to inefficiencies like imbalanced path saturation causing congestion and decreased flow experiences. To consider likely path utilization for advanced routing decisions, machine learning-assisted traffic prediction can be leveraged to estimate and aggregate flow-based load profiles.In this paper, a self-driving system for intelligent flow routing in programmable networks is proposed. A traffic prediction pipeline consuming early and focused metadata views as analysis input provides approximated flow load profiles used to derive probable future path states. Based on these, emerging packet streams are efficiently steered across available path capacities.Depending on the reliability of analysis results in terms of prediction errors, experiments show that, compared to ECMP, using established flow profiles for traffic distribution improves load sharing w.r. t. closer balanced path utilization trends over time.
Bus Load Prediction Method Based on SSA-GRU Neural Network

Bus load forecasting is of great significance for safe and stable dispatching of the power grid. For the characteristics of historical data of bus load changes such as complexity and time series, this paper proposes a bus load forecasting model (Sparrow Search algorithm-Gate Recurrent Unit, SSA-GRU) using the sparrow search algorithm (SSA) to optimize the parameters of gated cyclic units. The method first constructs a GRU neural network to capture the information available in the future of the time series. Secondly it uses the sparrow search algorithm to search for the optimal hyperparameters to obtain the optimal learning rate, the number of hidden layer neurons and the number of iterations, so as to improve the prediction accuracy and generalization. Finally the results validate the effectiveness and applicability of the proposed method through an example analysis of 220 kV busbars with different load attributes.
OPTango: Multi-central Representation Learning against Innumerable Compiler Optimization for Binary Diffing

Binary diffing, which quantitatively measures the difference between given binaries, has been broadly used in critical security areas. Previous studies have been tackling the challenge of default compiler optimization, as it can affect binary representation but overlooked the exploration of non-default optimization settings, which can also significantly affect the accuracy of diffing. Recent research indicates a growing trend of compiling applications with non-default optimization settings to magnify binary code discrepancies, enabling them to evade detection by binary diffing tools. This paper takes the first step to systematically studying the resistance of compiler optimization (including default and non-default optimization settings) on binary diffing tasks. To this end, we construct a diverse and unique dataset, OPTBinary, with 3.6 million functions compiled from 514 optimization settings. Then, we propose OPTango, an innovative transformer-based multi-central representation learning approach, exploring the solution to build a compiler optimization-agnostic binary diffing tool. We conduct extensive experiments and benchmark OPTango with state-of-the-art binary diffing approaches. Evaluation results show that OPTango is more robust and significantly outperforms existing methods against both default and non-default compiler optimization.
Motor equipment fault diagnostics via multi-feature acoustic signal fusion

In the research of fault diagnosis methods for electric machinery equipment, Deep learning-based diagnostic techniques can greatly reduce the interference of human experience, and thus have been widely applied. However, these methods assume that data have the same distribution, while in practice, rotating machinery often operates under different conditions, which makes this assumption difficult to meet and reduces the generalization ability of the model. In order to identify defects in electrical industrial equipment, this article proposes a method for exploiting acoustic signals. By analyzing the acoustic signals during operation, fault diagnosis of electric machinery equipment is carried out. The domain mixup method is used to increase the model's generalizability by reducing the distributional distance between data from various domains. Frequency analysis, time-frequency analysis, and feature fusion methods are used to detect faults in fan, bearing, and gearbox in electric machinery equipment. The results of the trial demonstrate that the model is highly accurate and available for diagnostics.
A Waveform Distortion Correction Method Based on Jitter Injection and FIR Digital Filter Using Farrow Structure

Direct digital frequency synthesis (DDFS) technology is widely used in modern nanoscale CMOS integrated devices, but it suffers from large waveform distortion and timing jitter in applications. In order to achieve high frequency resolution with limited waveform memory capacity, the phase of the waveform is usually truncated to switch waveform phases. The truncation of the phase causes the synthesized waveform to produce spurious and loses waveform details during phase switching, resulting in waveform distortion. In this paper, we propose a waveform distortion correction method that combines jitter injection and FIR digital filter using Farrow structure. Based on the waveform real-time computation technique, jitters are injected into the waveform time parameter to break the harmonic components caused by non-integer multiples of the sampling rate and waveform frequency. Using this approach, waveform spurious is further suppressed. The waveform distortion correction is achieved by design functions which complement the waveform distortion features. The complementary functions are then combined as Farrow filter coefficients by polynomial fitting. In this paper, a pulse waveform with 0.2 ps timing resolution is generated at 1 GSPS sampling rate by real-time computation technique. And then the waveform distortion correction method based on the proposed jitter injection combined with Farrow structure reduces the deterministic jitter from more than 600 ps to less than 20 ps, and the random jitter from more than 60 ps rms to less than 25 ps rms. The accuracy of timing parameters such as rise time, fall time, and pulse width is improved from ±500 ps to ±50 ps.
Recurrent Neural Network-Based Fault Diagnosis Method for Multisource Information Fusion of Drilling Pump Power End Gear

Drilling pump is an essential mechanical equipment for oil and gas exploitation. The existing gear fault diagnosis technology is difficult to effectively diagnose the fault state of drilling pumps, which are complex nonlinear high-dimensional coupling systems. Most data-driven methods ignore the time sequence characteristics in the signal. This paper introduces a recurrent neural network (RNN) to process the time sequence data extracted from gear fault vibration processes. It adopts stacked denoising autoencoders (SDAE) and proposes a new data sampling method to improve the generalization ability and anti-noise interference ability of the model proposed in this paper. Moreover, 17 groups of signals were collected for four working conditions and five health states through the test bench. The collected data were used to verify the model proposed in this paper. The results show that the proposed method can effectively capture the fault characteristics of gears under different working conditions, providing an effective solution for intelligent fault diagnosis methods of drilling pump gears.
Compact Modeling of Transition Metal Dichalcogenide Ballistic Transistors

This paper describes a new compact model for the I–V characteristics of ballistic transistors based on twodimensional semiconductors (2D-FETs). Our model is explicit, fully analytical, and includes non-idealities such as short-channel effects. The obtained I–V curves are validated against rigorous numerical investigations in the literature, demonstrating excellent agreement.
Security-constrained Fast Production Cost Minimization Simulation Based on Time Domain Partitioning Method

Production cost minimization (PCM) simulation has always played an important role in power system for long-term planning and assessment. However, some simplifications are always applied in PCM modeling in previous works, which makes the results significantly deviate from reality. Besides, due to the large scale of the PCM problem, it is time-consuming to solve. Therefore, this paper proposes a security constrained PCM simulation model considering the shift factor, and develops a fast solving method based on time domain partitioning. Simulation results on IEEE 118 bus system is used to demonstrate the effectiveness of the proposed method.
Why inconsistent leadership is regarded as procedurally unfair

Three experimental studies examined to what extent leader’s consistent use of procedures constitutes an important procedural fairness rule and inﬂuences people’s reactions as a function of social self-esteem. In line with a recent claim that more attention should be devoted to different procedural fairness rules (Brockner, Ackerman, & Fairchild, 2001), the ﬁndings of Study 1 demonstrated that inconsistent leaders were evaluated as less procedurally fair and inﬂuenced feelings of uncertainty about oneself in ongoing interpersonal interactions. Study 2 showed that manipulating leader’s consistency inﬂuenced people’s procedural fairness judgments and willingness to replace the leader, but only among those low in social self-esteem (SSE). Finally, Study 3, using another consistency manipulation, demonstrated that variations in consistency made participants feel bad about themselves, particularly when they were low in SSE. These ﬁndings are discussed in light of research on relational models of justice and sociometer theory. Copyright # 2003 John Wiley & Sons, Ltd. Our daily social interactions are frequently characterized by fundamental concerns of fairness (Tyler, Boeckmann, Smith, & Huo, 1997). For example, in organizations, one of the dominating themes of people’s talk is often the issue whether they received the appropriate outcomes (i.e. distributive justice, Deutsch, 1985), and whether correct and fair procedures have been used in arriving at those outcomes (i.e. procedural justice). In the last two decades, considerable interest has been paid to the latter instance of justice in which people evaluate the fairness of used procedures (Tyler et al., 1997). Lind and Tyler (1988, p. 1) even propose that fairness judgments are inﬂuenced more strongly by procedures than by outcomes. In fact, at different levels of society an increasing demand for respectful and considerate treatment is echoed, and following relational
A Whole New World: How to Architect Beyond-FPGA Reconfigurable Acceleration Devices?

Field-programmable gate arrays (FPGAs) have evolved beyond a fabric of soft logic and hard blocks surrounded by programmable routing to also incorporate high-performance networks-on-chip (NoCs), general-purpose processor cores and application-specific accelerators. These new reconfigurable acceleration devices (RADs) open up a myriad of architecture research questions, but require new computer-aided design tools for quantitative evaluation. In this work, we first enhance an existing RAD architecture simulator, RAD-Sim, to model high-bandwidth memory (HBM) and conventional DDR interfaces. We also introduce RAD-Gen which evaluates the silicon area and performance of the different components of a candidate RAD. We showcase the complete flow through a case study on accelerating deep learning recommendation models (DLRMs). Using RAD-Sim and RAD-Gen, we compare traditional FPGAs to RADs that incorporate hard NoCs and matrix-vector multiplication accelerators. This study demonstrates the utility of these tools in evaluating both the performance and implementation feasibility of different combinations of NoC and accelerator architecture parameters. The resulting RAD achieves an order of magnitude improvement in DLRM inference throughput and latency compared to prior FPGA implementations.
Traffic Injection Regulation Protocol Based on Free Time-Slots Requests

Network-on-Chips (NoCs) have demonstrated be a favorable alternative to conventional bus-based communication architectures for interconnecting programming elements (PEs). However, NoCs consist of numerous shared resources such as routers and links leading to traffic contention and hence packet transmission delays. Existing works rely on various mechanisms, e.g., leaky buckets, to regulate the network bandwidth distribution and reduce contention. However, such bandwidth regulation mechanisms rarely use runtime information to decide which PEs can inject packets in the network. In this paper, we propose a traffic injection regulation protocol where PEs can dynamically request other PEs to relinquish some network bandwidth. The proposed solution prevents starvation and excessive communication delays due to PEs being unable to inject their flits on the network. Moreover, since the proposed solution uses runtime information, it does not waste communication bandwidth. Experimental results show that our solution leads to a more equitable distribution of network bandwidth among PEs compared to leaky bucket-based mechanisms.
Improved Temporal Convolutional Network Based Ultra-Short-Term Photovoltaic Power Prediction

Accurate ultra-short-term power predictions are important for eliminating fluctuations in new energy power generation systems. To improve the accuracy of ultra-short-term photovoltaic (PV) power prediction, this paper proposes an ultra-short-term PV power prediction method based on improved temporal convolutional neural (TCN) network and feature modeling. First, the Spearman coefficient is applied to filter existing meteorological features while simultaneously combining solar illumination and the three-dimensional modeling of PV panels to identify the key factors affecting PV power generation and to mine astronomical features that affect PV power prediction. Second, the high correlation between astronomical features and PV power prediction is analyzed based on the correlation coefficient, which theoretically proves the feasibility and necessity of astronomical features. Third, an improved TCN network model is proposed for the algorithm. Multiple experiments indicate that compared with the existing prediction models, the proposed forecasting method is superior, the accuracy of PV power prediction over the next 4 h in the absence of meteorological conditions improves by 20.5%.
Application of Neural Networks in Data Communication Analysis

The purpose of this paper is to analyze modern trends in the field of data communication analysis with a specific focus on identifying commonly used methodologies in the detection of unusual communication patterns. This paper provides a brief overview of the techniques employed in data communication analysis. We have examined particular techniques used for identifying abnormal communication patterns within network packets, associated with malicious attacks and their features. Given the information, we analyzed the utilization of artificial intelligence techniques for identifying unusual communication patterns, using feedforward neural network topologies. We proposed two types of topologies to address this problem – multilayer perceptron and convolutional neural network. The study includes the creation of unique datasets that contain both our generated data and openly available datasets, which are subsequently analyzed. The outcomes of this research include an assessment of the classification capabilities, solution complexity and processing time of multilayer perceptron and convolutional neural network topologies used in data communication analysis, based on the specific data flow and used computational system.
Asymptotic Approximations of a Regulator and a Convergent Iteration Process for a Singularly Perturbed Problem

The paper considers the construction of asymptotic expansions in a singularly perturbed weakly nonlinear control problem to design an approximate controller. The asymptotic expansions are used to construct interpolation and extrapolation procedures and are obtained by applying the SDRE technique and analyzing a convergent iterative process. The experiments performed showed the efficiency of the proposed approach.
Geometry Optimization of Split Ring Resonator for Terahertz Photon-Magnon Coupling

Photon-Magnon Coupling (PMC) has been studied to develop effective quantum transducers and long-lifetime quantum memories. In order to achieve PMC, a cavity structure named as a split ring resonator (SRR) has been used as a photon resonator. However, most of the studies so far has remained in the gigahertz frequency range, so that it is hard to achieve PMC in terahertz range which is essential for coupling with antiferromagnetic magnons. Here, we investigate the geometry optimization of SRR in terahertz frequency range by using simulation as well as the experiment. The simulation shows that the efficiency and resonance frequency are varied by adjusting the geometric length and gap of the SRR. The simulation results are confirmed experimentally by using terahertz time-domain spectroscopy. This work provides a design rule for terahertz photon resonator with high efficiency for future PMC experiments.
Stock Shocks Modelling and Forecasting

Stock shocks are abrupt changes in a stock price time series. Major shocks, such as the 1929 Big Crash and, more recently, the subprime mortgage crisis, have greatly influenced the world economy in the last centuries. Nevertheless, shocks of minor intensity are frequent events that often go unnoticed but whose prediction can significantly help an investor to protect their investments. In this paper, we provide a formal definition of stock shocks and use limit order-book data to implement an algorithm to forecast stock shocks. The proposed algorithm is built upon a formal mathematical model approximating stock prices and return distributions with fat-tailed Lévy-stable models. A preliminary study of different machine-learning approaches allowed us to design an algorithm based on Random Forest, hierarchical clustering, and Bayesian optimization that outperforms other machine-learning methods that exhibit tunable and higher Precision and Recall values.
Detecting Temporal Inconsistency in Biased Datasets for Android Malware Detection

Machine learning (ML) has exhibited great potential in Android malware detection. Yet, the reliability of these ML models, as well as the fairness of their evaluation, hinge significantly on the quality of the datasets used. A significant issue compromising these aspects is the presence of temporal inconsistencies within datasets, which could lead to overestimated detection performance. While previous research has acknowledged the impact of temporal inconsistencies, the proposed detection approaches often falter in accuracy and practicality. Previous studies have had limitations when it comes to dealing with complex cases of temporal inconsistencies. Additionally, their approaches require knowledge of a dataset's temporal attributes, which is often not realistic in real-world applications. In response to these challenges, we propose a novel ML-based approach to comprehensively and effectively detect temporal inconsistencies in Android malware datasets, regardless of the magnitude of these inconsistencies. Distinguishing itself from prior attempts, our approach accurately identifies inconsistencies in unknown datasets, without making any assumptions about their temporal attributes. Moreover, we introduce a new benchmark dataset of 78,000 diverse Android samples, spanning malware to benign samples from 2010 to 2022, for exploring temporal inconsistency. A rigorous evaluation of our approach using this dataset reveals its proficiency in managing temporal inconsistencies, achieving a remarkable 98.3% detection accuracy. We further validate the efficacy of our feature selection procedure and demonstrate the robustness of our approach when applied to unknown datasets. Collectively, our findings pioneer a novel performance standard in Android malware detection assessments, contributing to the enhancement of reliability in ML-based techniques.
Mechanical properties of fractured media containing intermittent fractures at different strain rates

Aimed at the dynamic stability of fractured rock mass under earthquake and explosion loading,the mechanical properties of the fractured specimens were studied under uni-axial loading with strain rates 1.7×10-5 s-1,1.7×10-3 s-1,1.7× 10-1 s-1.The specimens used in this study were made of sandstone-like modeling material and contained two or three intermittent fractures with different geometric distributions.It was shown that the deformation properties of the specimens were correlative with the process of fractures coalescence,and presented localized and progressive damage.With the strain rate increasing,the moduli of specimens increased and the differences between fractured and non-fractured specimens diminished.The peak strengths were primarily dominated by its geometry,and the minimum for the specimens containing co-planar fractures appeaed at 35°inclination angle which was different with that for the specimens containing continuous fractures whose minimum appeared at about 60°. The peak strengths for specimens with three fractures depended on the coalescence modes,and its strengths were close to those for specimens with two-fractures whose were similar to coalescence modes.Moreover,the peak strengths increased with the strain rates,and the extent of strength increment for specimens with three fractures was more obvious than that for speeimens with two fractures and non-fractured specimens.Thus the conclusion that the weaken effect of the fractures might be lowered with the strain rate increasing could be obtained.