Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Aware Multi-Robot Task Allocation in Persistent Tasks

The applicability of the swarm robots to perform foraging tasks is inspired by their compact size and cost. A considerable amount of energy is required to perform such tasks, especially if the tasks are continuous and/or repetitive. Real-world situations in which robots perform tasks continuously while staying alive (survivability) and maximizing production (performance) require energy awareness. This paper proposes an energy-conscious distributed task allocation algorithm to solve continuous tasks (e.g., unlimited foraging) for cooperative robots to achieve highly effective missions. We consider efficiency as a function of the energy consumed by the robot during exploration and collection when food is returned to the collection bin. Finally, the proposed energy-efficient algorithm minimizes the total transit time to the charging station and time consumed while recharging and maximizes the robot's lifetime to perform maximum tasks to enhance the overall efficiency of collaborative robots. We evaluated the proposed solution against a typical greedy benchmarking strategy (assigning the closest collection bin to the available robot and recharging the robot at maximum) for efficiency and performance in various scenarios. The proposed approach significantly improved performance and efficiency over the baseline approach.
An Intelligent Self-Driving Truck System for Highway Transportation

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing studies mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this article, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real world deployment, and 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which show our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS.
Modelling matrix time series via a tensor CP-decomposition

We consider to model matrix time series based on a tensor CP-decomposition. Instead of using an iterative algorithm which is the standard practice for estimating CP-decompositions, we propose a new and one-pass estimation procedure based on a generalized eigenanalysis constructed from the serial dependence structure of the underlying process. To overcome the intricacy of solving a rank-reduced generalized eigenequation, we propose a further refined approach which projects it into a lower-dimensional full-ranked eigenequation. This refined method improves significantly the finite-sample performance of the estimation. The asymptotic theory has been established under a general setting without the stationarity. It shows, for example, that all the component coefficient vectors in the CP-decomposition are estimated consistently with certain convergence rates. The proposed model and the estimation method are also illustrated with both simulated and real data; showing effective dimension-reduction in modelling and forecasting matrix time series.
Clustering Vietnamese Conversations From Facebook Page To Build Training Dataset For Chatbot

The biggest challenge of building chatbots is training data. The required data must be realistic and large enough to train chatbots. We create a tool to get actual training data from Facebook messenger of a Facebook page. After text preprocessing steps, the newly obtained dataset generates FVnC and Sample dataset. We use the Retraining of BERT for Vietnamese (PhoBERT) to extract features of our text data. K-Means and DBSCAN clustering algorithms are used for clustering tasks based on output embeddings from PhoBERT$_{base}$. We apply V-measure score and Silhouette score to evaluate the performance of clustering algorithms. We also demonstrate the efficiency of PhoBERT compared to other models in feature extraction on the Sample dataset and wiki dataset. A GridSearch algorithm that combines both clustering evaluations is also proposed to find optimal parameters. Thanks to clustering such a number of conversations, we save a lot of time and effort to build data and storylines for training chatbot.
OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data

Search engines based on keyword retrieval can no longer adapt to the way of information acquisition in the era of intelligent Internet of Things due to the return of keyword related Internet pages. How to quickly, accurately and effectively obtain the information needed by users from massive Internet data has become one of the key issues urgently needed to be solved. We propose an intelligent question-answering system based on structured KB and unstructured data, called OpenQA, in which users can give query questions and the model can quickly give accurate answers back to users. We integrate KBQA structured question answering based on semantic parsing and deep representation learning, and two-stage unstructured question answering based on retrieval and neural machine reading comprehension into OpenQA, and return the final answer with the highest probability through the Transformer answer selection module in OpenQA. We carry out preliminary experiments on our constructed dataset, and the experimental results prove the effectiveness of the proposed intelligent question answering system. At the same time, the core technology of each module of OpenQA platform is still in the forefront of academic hot spots, and the theoretical essence and enrichment of OpenQA will be further explored based on these academic hot spots.
Large-scale modeling and optimization of en route air traffic flow

Large-scale Modeling and Optimization of En Route Air Traffic Flow by Dengfeng Sun Doctor of Philosophy in Engineering-Civil and Environmental Engineering University of California at Berkeley Professor Alexandre M. Bayen, Chair The research presented in this dissertation is motivated by the need for balancing the increasing demand and limited capacity of the National Airspace System (NAS), and more generally for large scale networked dynamical systems. A new paradigm for building an Eulerian-Lagrangian Cell Transmission Model for air traffic flow is developed. It is based on a network flow model constructed from historical air traffic data, and is applied to the entire continental NAS in the United States. This model is called Large-capacity Cell Transmission Model, CTM(L), in reference to the Cell Transmission Model in highway traffic. The CTM(L) captures fundamental characteristics, for example aircraft counts in sectors and travel times, of traffic flows in the Air Traffic Control (ATC) system. The predictive capabilities of the model are successfully validated against the recorded Enhanced Traffic Management System (ETMS) and Aircraft Situation Display to Industry (ASDI) data by showing an accurate match between predicted sector counts (based on filed flight plans) and measured sector counts.
3-D Material Style Transfer for Reconstructing Unknown Appearance in Complex Natural Materials

We propose a 3-D material style transfer framework for reconstructing invisible (or faded) appearance properties in complex natural materials. Our algorithm addresses the technical challenge of transferring appearance properties from one object to another of the same material when both objects have intricate, noncorresponding color patterns. Eggshells, exoskeletons, and minerals, for example, have patterns composed of highly randomized layers of organic and inorganic compounds. These materials pose a challenge as the distribution of compounds that determine surface color changes from object to object and within local pattern regions. Our solution adapts appearance observations from a material property distribution in an exemplar to the material property distribution of a target object to reconstruct its unknown appearance. We use measured reflectance in 3-D bispectral textures to record changing material property distributions. Our novel implementation of spherical harmonics uses principles from chemistry and biology to learn relationships between color (hue and saturation) and material composition and concentration in an exemplar. The encoded relationships are transformed to the property distribution of a target for color recovery and material assignment. Quantitative and qualitative evaluation methods show that we replicate color patterns more accurately than methods that only rely on shape correspondences and coarse-level perceptual differences. We demonstrate applications of our work for reconstructing color in extinct fossils, restoring faded artifacts and generating synthetic textures.
Working mechanism of Eternalblue and its application in ransomworm

After the leaking of exploit Eternalblue, some ransomworms utilizing this exploit have been developed to sweep over the world in recent years. Ransomworm is a global growing threat as it blocks users' access to their files unless a ransom is paid by victims. Wannacry and Notpetya are two of those ransomworms which are responsible for the loss of millions of dollar, from crippling U.K. national systems to shutting down a Honda Motor Company in Japan. Many dynamic analytic papers on Wannacry were published, however, static analytic papers about Wannacry were limited. Our aim is to present readers an systematic knowledge about exploit Eternalblue, from a high\textendash leveled semantic view to the code details. Specifically, the working mechanism of Eternalblue, the reverse engineering analysis of Eternalblue in Wannacry, and the comparison with the Metasploit's Eternalblue exploit are presented. The key finding of our analysis is that the code remains almost the same when Eternalblue is transplanted into Wannacry, which indicates its potential for signatures and thus detection.
Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation

Generating 3-D city models rapidly is crucial for many applications. Monocular height estimation (MHE) is one of the most efficient and timely ways to obtain large-scale geometric information. However, existing works focus primarily on training and testing models using unbiased datasets, which does not align well with real-world applications. Therefore, we propose a new benchmark dataset to study the transferability of height estimation models in a cross-dataset setting. To this end, we first design and construct a large-scale benchmark dataset for cross-dataset transfer learning on the height estimation task. This benchmark dataset includes a newly proposed large-scale synthetic dataset, a newly collected real-world dataset, and four existing datasets from different cities. Next, a new experimental protocol, few-shot cross-dataset transfer, is designed. Furthermore, in this article, we propose a scale-deformable convolution (SDC) module to enhance the window-based Transformer for handling the scale-variation problem in the height estimation task. Experimental results have demonstrated the effectiveness of the proposed methods in traditional and cross-dataset transfer settings. The datasets and codes are publicly available at https://mediatum.ub. tum.de/1662763 and https://thebenchmarkh.github. io/.
Spaces of polynomials with constrained real divisors, II. (Co)homology&stabilization

In the late 80s, V. Arnold and V. Vassiliev initiated the topological study of the space of real univariate polynomials of a given degree which have no real roots of multiplicity exceeding a given positive integer. Expanding their studies, we consider the spaces P d of real monic univariate polynomials of degree d whose real divisors avoid given sequences of root multiplicities. These forbidden sequences are taken from an arbitrary poset Θ of compositions that are closed under certain natural combinatorial operations. We reduce the computation of the homology H∗(P d ) to the computation of the homology of a differential complex, defined purely combinatorially in terms of the given closed poset Θ. We also obtain the stabilization results about H∗(PcΘ d ), as d→∞. These results are deduced from our description of the homology of spaces B d whose points are binary real homogeneous forms, considered up to projective equivalence, with similarly Θ-constrained real divisors. In particular, we exhibit differential complexes that calculate the homology of these spaces and obtain some stabilization results for H∗(BcΘ d ), as d→∞. In particular, we compute the homology of the discriminants of projectivized binary real forms and of their complements in Bd ∼= RP.
Energiesparende System- und Schaltungskonzepte für drahtlose Sensornetzwerke

Many Ideas have been presented how Wireless Sensor Networks can fundamentally change the way how distributed measurements can be conducted and the resulting data collected. However, energy consumption has so far prevented this technology from broad application. While improvements of battery technology and new methods of energy generation are one important way towards realization of these ideas, reduction of the energy consumption remains essential. The RF transceivers of such wireless sensors are most important in this regard as the bigger part of energy consumption is caused there. This work approaches the topic at three different levels: First of all, procedures and mechanisms of communication are optimized. The main focus lies on the idea of a dedicated wakeup receiver, whose sole purpose is to monitor the communication channel for incoming requests using minimal energy, while the rest of the sensor is on standby. Next step is the optimization of the detailed structure of the function blocks devised before. In addition to the conception of a CMOS integrable wakeup receiver, ways to structurally reduce energy consumption of frequency synthesizers are found. Especially at high frequencies, frequency synthesizers contribute significantly to the energy consumption of a transceiver. Finally the investigated structures are implemented using novel ideas also in circuit design. Design and implementation of an ultra-low-power 24GHz data transmission using direct conversion completes this work.
Distributed estimation through parallel approximants

Designing scalable estimation algorithms is a core challenge in modern statistics. Here we introduce a framework to address this challenge based on parallel approximants, which yields estimators with provable properties that operate on the entirety of very large, distributed data sets. We first formalize the class of statistics which admit straightforward calculation in distributed environments through independent parallelization. We then show how to use such statistics to approximate arbitrary functional operators in appropriate spaces, yielding a general estimation framework that does not require data to reside entirely in memory. We characterize the $L^2$ approximation properties of our approach and provide fully implemented examples of sample quantile calculation and local polynomial regression in a distributed computing environment. A variety of avenues and extensions remain open for future work.
Efficiently Enumerating Scaled Copies of Point Set Patterns

Problems on repeated geometric patterns in finite point sets in Euclidean space are extensively studied in the literature of combinatorial and computational geometry. Such problems trace their inspiration to Erd\H{o}s' original work on that topic. In this paper, we investigate the particular case of finding scaled copies of any pattern within a set of $n$ points, that is, the algorithmic task of efficiently enumerating all such copies. We initially focus on one particularly simple pattern of axis-parallel squares, and present an algorithm with an $O(n\sqrt{n})$ running time and $O(n)$ space for this task, involving various bucket-based and sweep-line techniques. Our algorithm is worst-case optimal, as it matches the known lower bound of $\Omega(n\sqrt{n})$ on the maximum number of axis-parallel squares determined by $n$ points in the plane, thereby solving an open question for more than three decades of realizing that bound for this pattern. We extend our result to an algorithm that enumerates all copies, up to scaling, of any full-dimensional fixed set of points in $d$-dimensional Euclidean space, that works in time $O(n^{1+1/d})$ and space $O(n)$, also matching the corresponding lower bound due to Elekes and Erd\H{o}s.
Deep Learning meets Liveness Detection: Recent Advancements and Challenges

Facial biometrics has been recently received tremendous attention as a convenient replacement for traditional authentication systems. Consequently, detecting malicious attempts has found great significance, leading to extensive studies in face anti-spoofing~(FAS),i.e., face presentation attack detection. Deep feature learning and techniques, as opposed to hand-crafted features, have promised a dramatic increase in the FAS systems' accuracy, tackling the key challenges of materializing the real-world application of such systems. Hence, a new research area dealing with the development of more generalized as well as accurate models is increasingly attracting the attention of the research community and industry. In this paper, we present a comprehensive survey on the literature related to deep-feature-based FAS methods since 2017. To shed light on this topic, a semantic taxonomy based on various features and learning methodologies is represented. Further, we cover predominant public datasets for FAS in chronological order, their evolutional progress, and the evaluation criteria (both intra-dataset and inter-dataset). Finally, we discuss the open research challenges and future directions.
Mean-field limit of non-exchangeable systems

This paper deals with the derivation of the mean-field limit for multi-agent systems on a large class of sparse graphs. More specifically, the case of non-exchangeable multi-agent systems consisting of non-identical agents is addressed. The analysis does not only involve PDEs and stochastic analysis but also graph theory through a new concept of limits of sparse graphs (extended graphons) that reflect the structure of the connectivities in the network and has critical effects on the collective dynamics. In this article some of the main restrictive hypothesis in the previous literature on the connectivities between the agents (dense graphs) and the cooperation between them (symmetric interactions) are removed.
Response to a comment on"Vindication of entanglement-based witnesses in hybrid quantum systems"

Chiara Marletto 2 and Emanuele Marconato 4 Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore Dipartimento di Informatica, University of Pisa, Largo B. Pontecorvo 3, Pisa 56127, Italy Dipartimento di Ingegneria e Scienza dell’Informazione, University of Trento, Via Sommarive 9, Povo 38123, Italy (Dated: January 3, 2022)
Color Alignment for Relative Color Constancy via Non-Standard References

Relative colour constancy is an essential requirement for many scientific imaging applications. However, most digital cameras differ in their image formations and native sensor output is usually inaccessible, e.g., in smartphone camera applications. This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms. To resolve this issue, we propose a colour alignment model that considers the camera image formation as a black-box and formulates colour alignment as a three-step process: camera response calibration, response linearisation, and colour matching. The proposed model works with non-standard colour references, i.e., colour patches without knowing the true colour values, by utilising a novel balance-of-linear-distances feature. It is equivalent to determining the camera parameters through an unsupervised process. It also works with a minimum number of corresponding colour patches across the images to be colour aligned to deliver the applicable processing. Three challenging image datasets collected by multiple cameras under various illumination and exposure conditions, including one that imitates uncommon scenes such as scientific imaging, were used to evaluate the model. Performance benchmarks demonstrated that our model achieved superior performance compared to other popular and state-of-the-art methods.
Approximate $K$-conjugacies and $C^*$-approximate conjugacies of minimal dynamical systems

In this article, we extend H. Matui and H. Lin’s notions of approximate K-conjugacies and C∗-strongly approximate conjugacies to general minimal dynamical systems. In particular, upon modifying a result of the existence of minimal skew products, we answer a question of H. Lin and show that, associated with any Cantor minimal system (K,α), there is a class R0(α̃) of minimal skew products on K × Ω, such that for any two rigid homeomorphisms α ∈ R0(α̃) and β ∈ R0(β̃), the notions of approximate K-conjugacy and C∗-strongly approximate conjugacy coincide, which are also equivalent to a K-version of Tomiyama’s commutative diagram, where Ω is an (infinite) connected finite CW-complex with torsion free K-groups and the so-called Lipschitz-minimal-property (LMP).