Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Perturbation Consistency to Improve Multi-hop Knowledge Base Question Answering

Multi-hop knowledge base question answering aims to answer natural language questions through multi-hop relation reasoning in the knowledge base. An important challenge of the task is the lack of labels for reasoning paths, which leads to the possibility to produce correct answers through incorrect paths in the training, and cannot generalize well in testing. Recently research has attempted to handle the challenge by devising reward shaping or introducing additional information to generate supervision signals of intermediate paths. But they required extra expert experience and label information. To address this situation, we propose a novel method under the teacher-student framework, it leverages perturbation consistency to learn intermediate paths. In the teacher network, we construct close data points for intermediate path prediction by applying random perturbations. Inspired by the data smoothing assumption that labels of close data points should be the same, a consistency loss over predictions of constructed data points and original ones is evaluated. The student network is used to answer questions more precisely by leveraging the intermediate distribution learned from the teacher network. Extensive experiments on two benchmark datasets are conducted, and the results have demonstrated the effectiveness of the proposed method.
Future Trends in Cellular Infrastructure and Radio Technology for Sustainable Networks

Wireless standards and radio requirements are evolving as the roll-out of 5G, emergence of open radio access networks (O-RAN), and early investigation of 6G are progressing forward. Particularly, multi-band macro-cell frequency-division duplexing (FDD) radio heads, massive multiple-input multiple-output (MIMO) time-division duplexing (TDD) radio units, and emerging frequency bands beyond sub-6 GHz present a unique challenge to the industry, striving for higher energy efficiency and capacity. This paper aims to provide an overview and discuss the future directions for research in radio architectures, radio frequency (RF) power amplifiers (PA), and digital transformation, leveraging machine learning and linearization techniques for cellular infrastructure.
Temperature Effects on Gallium Nitride Field Emitter Arrays

Gallium nitride (GaN) field emitter arrays are being studied for use as vacuum channel transistors (VCTs). In this work, arrays of 150×150 GaN field emitters were characterized before and after heat treatment at 400° C. Collector voltage was kept at 200V DC, and the gate voltage was swept from 0 to 75V. From the I-V measurements, a jump in emission current, a form of conditioning, was observed after a few voltage sweeps resulting in a stable emission current. After heat treatment at 400° C for 10 minutes, ≈ 4 times increase in current was observed, reaching a maximum field emission current of ≈ 10 μA, at 75 V.
Compact 3D-Printed Bandpass Filters Using Coaxial and Dual-mode Ridged-Waveguide Resonators

This paper presents a new integration concept for 3D bandpass filters (BPFs) where coaxial resonators and dual-mode ridged waveguide resonators are combined within the same volume for miniaturization and RF performance enhancement. Size compactness is achieved by: i) two types of miniaturized resonators namely a coaxial and a dual-mode ridge waveguide resonator and ii) monolithic integration using stereolithography apparatus (SLA) 3D printing. A connectorized integration concept is also employed to facilitate the inter-connection with other SMA-probed RF devices. RF performance is enhanced through the combination of the two resonator types where the dual-mode resonator generates a transmission zero (TZ) close to the passband. A design process using coupled-resonator synthesis and eigenmode analysis is provided. The concept is experimentally validated at 4.9 GHz through the design, manufacturing, and testing of a BPF prototype with 3 dB fractional bandwidth of 7.3%, in-band insertion loss of 0.31 dB and Qeff of 1210.
How Fast is My Software? Latency Evaluation for a ROS 2 Autonomous Driving Software

Violations of real-time properties and high latencies have emerged as crucial issues in autonomous vehicles since they can lead to unwanted vehicle behavior and critical maneuvers. Our study aims to provide a comprehensive understanding of latencies in a software stack for autonomous vehicles. In this paper, we present an evaluation workflow to inspect software and the occurring latencies for ROS 2 applications. This workflow was used to analyze the open-source autonomous driving stack Autoware. Universe by showing the influence of different soft- and hardware configurations. Our focus is on the evaluation of end-to-end, communication, computation, and idle latencies. Based on the results, we show the bottlenecks and motivate future directions to optimize ROS 2 autonomous driving software.
DOPNet: Dynamic Optimized Pruning Net for Model Compression

Model pruning is one of the effective ways to deploy deep models on edge computing devices. Existing pruning methods often prune the network with a static pruning policy that treat each layer with equal importance. This may ignore the fine-grained information contained in each layer and finally result in sub-optimal compression results. In this paper, we propose DOPNet for Dynamic Optimized Pruning using a self-feedback framework. Based on our analysis of the results after pruning, we propose to compute the dynamic compression factor for each layer through computing the layer-specific and the global structural variables. For easy and end-to-end optimization, we propose to optimize our dynamic policy based on the information bottleneck theory. Extensive experiments on CIFAR-10 and CIFAR-100 show the benefits of this dynamic design methodology: while maintaining the same-level accuracy, our DOPNet saves 40% – 50% parameters with VGG structures and achieves 50% – 60% parameter saving with ResNet structures compared with state-of-the-art methods.
A W-Band Phase-Shifter-Embedded PA in 40-nm CMOS for 6G Applications

A phase-shifter-embedded (Δφ-embedded) power amplifier (PA) incorporating multifunctional Δφ-embedded impedance matching networks (IMN) is proposed for 6G applications. The Δφ-embedded IMN can realize impedance transformation, execute a single-ended to differential conversion, and simultaneously provide a phase shift without requiring additional circuits. Hence, the insertion loss, occupied chip area, and power dissipation can be reduced. Realized in a 40-nm digital CMOS process, the proposed PA can provide a maximum saturated output power of 10.2 dBm and a phase-shifting range wider than 90° from 75 to 89 GHz. The power consumption is 151 mW from a 1.1-V supply.
Further Studies on Using the AHFP Exponent to Choose Between Alternative Field Emission Theories

The general background to this research is set out in [1], [2]. It is based on the idea that an experimentally measured value of the so-called “AHFP exponent” kmight be used to choose between alternative theories of field electron emission (FE), in the first instance between 1928/29 Fowler-Nordheim (FN) FE theory and 1956 Murphy-Good (MG) FE theory. Amongst several factors that influence predicted theoretical ranges for x-values are the shape and local work function of a point-like or post-like field emitter. Our earlier work [1] explored a limited range of these parameters, but the overall conclusion was that the only x-value (1.63) considered adequately reliable did not yield a decisive result. As discussed in detail in this presentation, we have now explored a wider range of these parameters, but the outcome is still not a decisive result. Implications are discussed in a separate presentation [2].
Temperature and Process Calibration of HBT-Based Square-Law Power Detectors for Millimeter-Wave Built-In Self-Test

HBT-based millimeter-wave square-law power detectors exhibit high power readout uncertainties, typically in the range of ±5 dB, related to process and temperature variations. High uncertainty is unacceptable when the detector is used for built-in self-test purposes. To overcome this drawback, we propose a new calibration procedure based on an analysis of the process and temperature-dependent terms in the detector output voltage equation. Unlike previous approaches, the proposed procedure has minimal area overhead while dealing with both temperature and process variation. The calibration procedure is verified experimentally along with process corner and Monte Carlo simulations. We show that the proposed approach is able to entirely remove the limitation of the dynamic range (DR) for low inputs caused by mismatch. After calibration, the power prediction error in the DR is within ±1 dB, an accuracy improvement of at least 4 dB.
Which Doors Are Open: Reinforcement Learning-based Internet-wide Port Scanning

Internet-wide scanning is a commonly used research technique in various network surveys, such as measuring service deployment and security vulnerabilities. However, these network surveys are limited to the given port set, not comprehensively obtaining the real network landscape, and even misleading survey conclusions. In this work, we introduce PMap, a port scanning tool that efficiently discovers the majority of open ports from all 65K ports in the whole network. PMap uses the correlation of ports to build an open port correlation graph of each network, using a reinforcement learning framework to update the correlation graph based on feedback results and dynamically adjust the order of port scanning. Compared to current port scanning methods, PMap achieves better performance on hit rate, coverage, and intrusiveness. Our experiments over real-world networks show that PMap can find 90% open ports by only scanning 125 ports (90% @125) to each active address with 136× less than the state-of-the-art port probing methods. PMap reduces the number of scanned ports to decrease the intrusive nature of port scanning. PMap is the first effective practice for scanning open ports using reinforcement learning. It bridges the gap of existing scanning tools and effectively supports subsequent service discovery and security research.
Unified Pedestrian Path Prediction Framework: A Comparison Study

Pedestrian path prediction is an emerging and crucial task in numerous applications, such as autonomous vehicles. Due to the complexity of the task, various formulations are proposed throughout the literature. However, the interconnection between these formulations remains to be seen, which makes a fair comparison challenging. This work proposes a unified pedestrian path prediction framework via Markov decision process (MDP). We demonstrate that by carefully designing the components of the MDP, various standard formulations can be perceived as specific combinations of settings in our framework. Additionally, the unified framework allows us to discover new combinations of settings that integrate the benefits of current formulations improving the prediction performance. We conduct a comparison study and evaluate several formulations in well-controlled experiments. Furthermore, we carefully assess the influence of various settings, such as policy stochasticity and sequential decision-making, on prediction performance. The goal of this work is not to propose a new state-of-the- art method but to study various formulations of the pedestrian path prediction task under a unifying framework and uncover new directions that can eventually advance the current state-of-the-art.
Data Fusion for Smart Agriculture Applied to an IoT-based Use-Case for Arable Crops

The H2020 DEMETER project uses advanced AI and IoT technologies in order to promote the sustainability of the agricultural sector, to decrease the costs and the environmental impact, while maintaining yields at the same time. To achieve this it developed a service-oriented architecture where various interoperable enablers (i.e., services) can be combined in order to create apps that optimize the usage of resources and yield at the same time. This paper first presents a specific use-case that optimizes rice and maize irrigation and fertilization management processes. Then, it focuses its main part in the development of a data fusion enabler that, combined with appropriate sensor, data analytics, decision support and actuation enablers was combined into the development of DEMETER apps for this use case, that achieved reductions of the used resources (water and fertilizer) while maintaining the crop yields. For the development of this fusion enabler, several preexisting fusion techniques were examined, and the most appropriate of these were used and combined to form the fusion enabler.
Through-Wall Human Trajectory Tracking Based on Multi-view Fusion Attention Mechanism

Identifying human position in through-wall scenarios is quite helpful for emergency rescue, while the multiple reflections between the wall and the radar signal substantially degrade the detection accuracy in the through-wall scenario, making it challenging to determine the human’s position. In this paper, we propose a novel approach for through-wall human trajectory tracking with radar. The group convolution mechanism is used to extract features of different views. And the soft-squeeze excitation (SSE) module is proposed to fuse multi-view features, which recalibrates channel weights by explicitly modeling the correlation between channels. With these components, the features from the bird’s eye view and front view are combined to emphasize the human’s 3D location. This method is validated on a radar-based through-wall human trajectory dataset and shows promising results.
Towards Active Motion Planning in Interactive Driving Scenarios: A Generic Utility Term of Interaction Activeness

Interacting with other vehicles while ensuring safety is a routine task for human drivers, but it can pose a challenge for autonomous vehicles. To address this challenge, we derived a generic utility term of interaction activeness (UTIA) from the driving interaction formulation which considers the rationality of interacting counterparts. Our research shows that incorporating UTIA as a supplementary utility term can improve the active interaction capability of both sampling-based and game-theoretic baseline motion planners without compromising safety. Through simulation experiments, we observed that on average, incorporating the weighted UTIA into the utility function of baseline planners can result in an 8.8% increase in the success rate of exiting a highway within a set distance.
Automatic Generation of Smell-free Unit Tests

Automated test generation tools, such as EvoSuite, typically aim to generate tests that maximize code coverage and do not adequately consider non-coverage aspects that may be relevant for developers, e.g., test’s code quality. Hence, automatically generated tests are often affected by test-specific bad programming practices, i.e., test smells, that may hinder the quality of the test’s source code and, ultimately, the quality of the code under test. Although EvoSuite uses secondary criteria and a post-processing procedure to optimize non-coverage aspects and improve the readability of the tests, it does not explicitly consider the usage of good programming practices. Thus, in this paper, we propose a novel approach to assist EvoSuite’s search algorithm in generating smell-free tests out of the box. To this aim, we first compile a set of 54 test smell metrics from several sources. Secondly, we systematically identify 30 smells that do not affect the tests generated by EvoSuite and eight smells that cannot be automatically computed. Thirdly, we incorporate the remaining 16 test smells as metrics into EvoSuite and empirically identify that only 14 smells affect the tests generated by the tool (e.g., Indirect Testing). Fourthly, we describe and integrate an approach to optimize test smell metrics into EvoSuite. Finally, we conduct an empirical study to (i) understand to what extend EvoSuite’s default mechanisms leads to the generation of fewer smelly tests. (ii) to assess whether our approach leads to the generation of fewer smelly tests. And (iii) how our approach affects the coverage and fault detection effectiveness of the generated tests. Our results report that our approach can generate 8.58% fewer smelly tests without significantly compromising their coverage or fault detection effectiveness.
Real-Time Graph-Based Optimization for GNSS-Doppler Integrated RTK-GNSS/IMU/DR Positioning System in Urban Area

Autonomous driving of vehicles and robots requires highly accurate position information, and RTK-GNSS is expected to be utilized for this purpose. In this paper, we propose a robust and real-time operation method by introducing graph optimization into the integrated RTK-GNSS/IMU method. The proposed method is an extension of a method using vehicle trajectories that can estimate positions with lane-level accuracy even in urban areas. The position is estimated by removing GNSS multipaths from the shape of a vehicle trajectory of several hundred meters and averaging the remaining GNSS results. This method does not take into account the errors in the vehicle trajectory and cannot fully benefit from the high accuracy positioning solution of RTK-GNSS. To solve this problem, we introduce graph optimization to the base method, which treats the error state as a probabilistic model. However, general graph optimization methods have problems with processing time and outlier elimination. The proposed method solves these problems by restricting the time series data to be optimized and using a two-step optimization structure. Evaluations show that the proposed method is effective because it satisfies the requirements for real-time operation and improves accuracy compared to conventional methods.
Reducing static linearity testing for ADCs

Static linearity testing of Analog-to-Digital Converters (ADCs) is known to be a time-consuming process. This paper describes a method exploiting the Compressed Sensing to reduce time in ADCs for linearity testing based on the static transfer curve. The proposed method reduces randomly the input voltage values to characterize the ADC, computing reduced values of Integral Nonlinearity (INL). Subsequently, the complete INL curve is reconstructed, by exploiting the INL sparsity in the Fourier domain. By comparing the INL curve obtained by the standard method to the reconstructed INL curve, the error value is generally low. Thus, the proposed method results promising to reduce the overall duration of static linearity testing for ADCs.
Safe Decision Or Collision? Using Natural Habituated Interfaces to Increase Traffic Safety

Until fully automated vehicles are widely spread, human drivers will remain indispensable. Until then, driving tasks exceeding the operational boundaries of the vehicle would be delegated to drivers by take-over requests, with the driver as a fallback operator. In cooperative approaches, an appropriate interface is essential for driving safety to prevent accidents. In this study, we propose not to rely on the commonly used touchscreen but on the natural habituated interface (NHI) in the form of the steering wheel, which is highly trained during manual driving. Using safety-critical driving scenarios, we compared the safety, usability, and criticality of both interaction approaches in a driving simulator experiment (N = 26). Our results indicate a significantly lower number of dangerous overtakes leading to accidents; and a higher usability score when using the NHI compared to the touchscreen interface.
CRCS: Learning Synergistic Cascade Correlation for Microscopic Cascade Prediction

Information diffusion prediction is the basis of many fundamental tasks, such as social recommendation and community detection. Currently, most researchers infer user correlation based on cascade records to predict future infected users. However, they only consider whether the infected users at each location are correctly predicted, ignoring the semantic differences between users who participate in the retweets of similar content and other users. This naturally leads to learning biased user features. In this paper, we compute semantic relevance between two different cascades (e.g., cm and cn) based on user coincidence and develop the cascade correlation graph (CCG) to learn user features. Afterward, we devise a position-independent sequence similarity loss (SSL) function combined with an auxiliary predictor to optimize the user encoder. The above two methods make users participating in the relevant cascades more relevant than others. Experiments show that the two methods are effective, and the combination of them can improve performance more significantly.
Improving Vehicle Trajectory Prediction with Online Learning

In autonomous driving systems, predicting the trajectory of surrounding vehicles facilitates the decision-making and trajectory planning of ego cars. Most previous works train the trajectory predictor based on the offline dataset. While the offline general predictors capture the average distribution in the dataset, they commonly neglect the low-probability corner cases that outside the general distribution of dataset. However, the corner cases might cause inevitable displacement errors and goals missing in the evaluation. Offline data augmentation and model refinement could essentially improve the accuracy while we can leverage the historical trajectories to specialize the predictor by self-supervision. In this paper, we propose an online learning framework that updates the general trajectory predictor with sequential history data at corner cases. We design the temporal data-generation method and training scheme for online learning, which can generally accommodate learning-based predictors. Since online learning costs expensive online computation resources, we devise a switching discriminator to distinguish the corner cases by evaluating the metric improvement of online learning over the offline general predictor. We validate that the online learning method can effectively reduce the displacement errors and miss rate caused by vehicle velocity variation and promote the intention convergence at multi-modal intersections. Experiments also show that the switching discriminator limits the switching-on rate of online learning cases to 2.45% while reducing the miss rate by 33.4%. The code for generating sequentially arranged online learning dataset(Argoverse) is in https://gitee.com/mindspore/models/tree/master/research/cv/TraPred_OnlineLearning.
WiFi-based Localization for Fail-Aware Autonomous Driving in Urban Scenarios

Ego-localization is one of the most critical functions in autonomous vehicles. This paper presents a novel WiFi-based localization system for autonomous driving designed to augment onboard localization systems during critical failures or complement GNSS-denied scenarios such as parking lots. The system leverages the existing WiFi network infrastructure to provide global localization using a WiFi interface and a publicly available WiFi RSS and AP database created through survey efforts with conventional mobile devices. An LSTM-based architecture is trained to estimate the device’s position from the history of WiFi RSS, leveraging temporal correlations in the sequences. The results suggest that this system is a viable alternative even when no strong requirements are set for the quality of the GNSS measurements in the surveying phase.