Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Clustering Approach for Accident Prediction

Traffic accident prediction is a crucial task in the mobility domain. State-of-the-art accident prediction approaches are based on static and uniform grid-based geospatial aggregations, limiting their capability for fine-grained predictions. This property becomes particularly problematic in more complex regions such as city centers. In such regions, a grid cell can contain subregions with different properties; furthermore, an actual accident-prone region can be split across grid cells arbitrarily. This paper proposes Adaptive Clustering Accident Prediction (ACAP) - a novel accident prediction method based on a grid growing algorithm. ACAP applies adaptive clustering to the observed geospatial accident distribution and performs embeddings of temporal, accident-related, and regional features to increase prediction accuracy. We demonstrate the effectiveness of the proposed ACAP method using open real-world accident datasets from three cities in Germany. We demonstrate that ACAP improves the accident prediction performance for complex regions by 2–3 percent points in F1-score by adapting the geospatial aggregation to the distribution of the underlying spatio-temporal events. Our grid growing approach outperforms the clustering-based baselines by four percent points in terms of F1-score on average.
Synthesis of Predictable Global NoC by Abutment in Synchoros VLSI Design

Synchoros VLSI design style has been proposed as an alternative to the standard cell-based design style; the word synchoros is derived from the Greek word choros for space. Synchoricity discretises space with a virtual grid, the way synchronicity discretises time with clock ticks. SiLago (Silicon Lego) blocks are atomic synchoros building blocks like Lego bricks. SiLago blocks absorb all metal layer details, i.e., all wires, to enable composition by abutment of valid; valid in the sense of being technology design rules compliant, timing clean and OCV ruggedized. Effectively, composition by abutment eliminates logic and physical synthesis for the end user. Like Lego system, synchoricity does need a finite number of SiLago block types to cater to different types of designs. Global NoCs are important system level design components. In this paper, we show, how with a small library of SiLago blocks for global NoCs, it is possible to automatically synthesize arbitrary global NoCs of different types, dimensions, and topology. The synthesized global NoCs are not only valid VLSI designs, but their cost metrics (area, latency, and energy) are known with post-layout accuracy in linear time. We argue that this is essential to be able to do chip-level design space exploration. We show how the abstract timing model of such global NoC SiLago blocks can be built and used to analyse the timing of global NoC links with post layout accuracy and in linear time. We validate this claim by subjecting the same VLSI designs of global NoC to commercial EDA’s static timing analysis and show that the abstract timing analysis enabled by synchoros VLSI design gives the same results as the commercial EDA tools.
NimbleChain: Low-latency consensusless cryptocurrencies in general-purpose permissionless blockchains

—Nakamoto’s seminal work gave rise to permissionless blockchains – as well as a wide range of proposals to mitigate its performance shortcomings. Despite substantial throughput and energy efﬁciency achievements, most proposals only bring modest (or marginal) gains in transaction commit latency. Consequently, commit latencies in today’s permissionless blockchain landscape remain prohibitively high for latency-sensitive geo-distributed applications. This paper proposes NimbleChain, which extends standard permissionless blockchains with a fast path that delivers consensusless promises of commitment . This fast path supports cryptocurrency transactions and only takes a small fraction of the original commit latency, while providing consistency guarantees that are strong enough to ensure correct cryptocurrencies. Since today’s general-purpose blockchains also support smart contract transactions, which typically have (strong) sequential consistency needs, NimbleChain implements a hybrid consistency model that also supports strongly-consistent applications. To the best of our knowledge, NimbleChain is the ﬁrst system to bring together fast consensusless transactions with strongly-consistent consensus-based transactions in a permissionless setting. We implement NimbleChain as an extension of Ethereum and evaluate it in a 500-node geo-distributed deployment. The results show that the average latency to promise a transaction is an order of magnitude faster than consensus-based commit, with minimal overhead when compared with a vanilla Ethereum implementation.
EarGate: gait-based user identification with in-ear microphones

Human gait is a widely used biometric trait for user identification and recognition. Given the wide-spreading, steady diffusion of ear-worn wearables (Earables) as the new frontier of wearable devices, we investigate the feasibility of earable-based gait identification. Specifically, we look at gait-based identification from the sounds induced by walking and propagated through the musculoskeletal system in the body. Our system, EarGate, leverages an in-ear facing microphone which exploits the earable's occlusion effect to reliably detect the user's gait from inside the ear canal, without impairing the general usage of earphones. With data collected from 31 subjects, we show that EarGate achieves up to 97.26% Balanced Accuracy (BAC) with very low False Acceptance Rate (FAR) and False Rejection Rate (FRR) of 3.23% and 2.25%, respectively. Further, our measurement of power consumption and latency investigates how this gait identification model could live both as a stand-alone or cloud-coupled earable system.
Study of CH 3 NH 3 PbI 2.75 Cl 0.25 photoelectric conversion material based on the first principle

The environment-friendly perovskite solar cell materials CH 3 NH 3 PbI 2.75 Cl 0.25 was investigated by using the CASTEP software package in Materials Studio (MS) software. For the CH 3 NH 3 PbI 3 cell, six kinds of the structure and physical properties of the I anion replaced by one Cl anion were built and calculated. Firstly, the geometrical optimization and energy optimization of the six kinds of configurations are obtained by GGA+WC correlation function, and the final optimized geometrical structure and total energy are obtained. Secondly, the geometrical structure of the lowest energy is found, and the structure will be the most stable. The results show that the replacement of I atoms by Cl atoms influences the physical properties of the configuration structure, resulting from the orbit of the contribution of the Cl atoms in the calculation. It can be judged that all the configurations belong to direct semiconductor, and no magnetic moment. In addition, these materials in the low frequency range shows good absorption property, and can be used a good photoelectric conversion material.
Anomaly Detection on IT Operation Series via Online Matrix Profile

Anomaly detection on time series is a fundamental task in monitoring the Key Performance Indicators (KPIs) of IT systems. Many of the existing approaches in the literature show good performance while requiring a lot of training resources. In this paper, the online matrix profile, which requires no training, is proposed to address this issue. The anomalies are detected by referring to the past subsequence that is the closest to the current one. The distance significance is introduced based on the online matrix profile, which demonstrates a prominent pattern when an anomaly occurs. Another training-free approach spectral residual is integrated into our approach to further enhance the detection accuracy. Moreover, the proposed approach is sped up by at least four times for long time series by the introduced cache strategy. In comparison to the existing approaches, the online matrix profile makes a good trade-off between accuracy and efficiency. More importantly, it is generic to various types of time series in the sense that it works without the constraint from any trained model.
Non-Dissipative and Structure-Preserving Emulators via Spherical Optimization

Approximating a function with a finite series, e.g., involving polynomials or trigonometric functions, is a critical tool in computing and data analysis. The construction of such approximations via now-standard approaches like least squares or compressive sampling does not ensure that the approximation adheres to certain convex linear structural constraints, such as positivity or monotonicity. Existing approaches that ensure such structure are norm-dissipative and this can have a deleterious impact when applying these approaches, e.g., when numerical solving partial differential equations. We present a new framework that enforces via optimization such structure on approximations and is simultaneously norm-preserving. This results in a conceptually simple convex optimization problem on the sphere, but the feasible set for such problems can be very complex. We establish well-posedness of the optimization problem through results on spherical convexity and design several spherical-projection-based algorithms to numerically compute the solution. Finally, we demonstrate the effectiveness of this approach through several numerical examples.
Theoretical computations on the efficiency of acetaldehyde formation on interstellar icy grains

Context. Interstellar grains are known to be important actors in the formation of interstellar molecules such as H 2 , water, ammonia, and methanol. It has been suggested that the so-called interstellar complex organic molecules (iCOMs) are also formed on the interstellar grain icy surfaces by the combination of radicals via reactions assumed to have an efﬁciency equal to unity. Aims. In this work, we aim to investigate the robustness or weakness of this assumption. In particular, we consider the case of acetaldehyde (CH 3 CHO), one of the most abundant and commonly identiﬁed iCOMs, as a starting study case. In the literature, it has been postulated that acetaldehyde is formed on the icy surfaces via the combination of HCO and CH 3 . Here we report new theoretical computations on the efﬁciency of its formation. Methods. To this end, we coupled quantum chemical calculations of the energetics and kinetics of the reaction CH 3 +HCO, which can lead to the formation of CH 3 CHO or CO+CH 4 . Speciﬁcally, we combined reaction kinetics computed with the Rice–Ramsperger- –Kassel—Marcus theory (tunneling included) method with diffusion and desorption competitive channels. We provide the results of our computations in the format used by astrochemical models to facilitate their exploitation. Results. Our new computations indicate that the efﬁciency of acetaldehyde formation on the icy surfaces is a complex function of the temperature and, more importantly, of the assumed diffusion over binding energy ratio f of the CH 3 radical. If the ratio f is ≥ 0.4, the efﬁciency is equal to unity in the range where the reaction can occur, namely between 12 and 30 K. However, if f is smaller, the efﬁciency dramatically crashes: with f =0.3, it is at most 0.01. In addition, the formation of acetaldehyde is always in competition with that of CO+CH 4 . Conclusions. Given the poor understanding of the diffusion over binding energy ratio f and the dramatic effect it has on the formation, or not, of acetaldehyde via the combination of HCO and CH 3 on icy surfaces, model predictions based on the formation efﬁciency equal to one should to be taken with precaution. The latest measurements of f suggest f =0.3 and, if conﬁrmed for CH 3 , this would rule out the formation of acetaldehyde on the interstellar icy surfaces. We recall the alternative possibility, which was recently reviewed, that acetaldehyde could be synthesized in the gas phase starting from ethanol. Finally, our computations show the paramount importance played by the micro-physics involved in the interstellar surface chemistry and call for extensive similar studies on different systems believed to form iCOMs on the interstellar icy surfaces.
Geometric Models for (Temporally) Attributed Description Logics

In the search for knowledge graph embeddings that could capture ontological knowledge, geometric models of existential rules have been recently introduced. It has been shown that convex geometric regions capture the so-called quasi-chained rules. Attributed description logics (DL) have been defined to bridge the gap between DL languages and knowledge graphs, whose facts often come with various kinds of annotations that may need to be taken into account for reasoning. In particular, temporally attributed DLs are enriched by specific attributes whose semantics allows for some temporal reasoning. Considering that geometric models and (temporally) attributed DLs are promising tools designed for knowledge graphs, this paper investigates their compatibility, focusing on the attributed version of a Horn dialect of the DL-Lite family. We first adapt the definition of geometric models to attributed DLs and show that every satisfiable ontology has a convex geometric model. Our second contribution is a study of the impact of temporal attributes. We show that a temporally attributed DL may not have a convex geometric model in general but we can recover geometric satisfiability by imposing some restrictions on the use of the temporal attributes.
A Burchnall-Chaundy-Krichever Theory for Fractional Differential Operators

Fractional differential (and difference) operators play a role in a number of diverse settings: integrable systems, mirror symmetry, Hurwitz numbers, the Bethe ansatz equations. We prove extensions of the three major results on algebras of commuting (ordinary) differentials operators to the setting of fractional differential operators: (1) the Burchnall-Chaundy theorem that a pair of commuting differential operators is algebraically dependent, (2) the classification of maximal commutative algebras of differential operators in terms of Sato's theory and (3) the Krichever correspondence constructing those of rank 1 in an algebro-geometric way. Unlike the available proofs of the Burchnall-Chaundy theorem which use the action of one differential operator on the kernel of the other, our extension to the fractional case uses bounds on orders of fractional differential operators and growth of algebras, which also presents a new and much shorter proof of the original result. The second main theorem is achieved by developing a new tool of the spectral field of a point in Sato's Grassmannian, which carries more information than the widely used notion of spectral curve of a KP solution. Our Krichever type correspondence for fractional differential operators is based on infinite jet bundles.
Dual-arm Coordinated Manipulation for Object Twisting with Human Intelligence

Robotic dual-arm twisting is a common but very challenging task in both industrial production and daily services, as it often requires dexterous collaboration, a large scale of end-effector rotating, and good adaptivity for object manipulation. Meanwhile, safety and efficiency are primary concerns for robotic dual-arm coordinated manipulation. Thus, the normally adopted fully automated task execution approaches based on environmental perception and motion planning techniques are still inadequate and problematic for the arduous twisting tasks. To this end, this paper presents a novel strategy of the dual-arm coordinated control for twisting manipulation based on the combination of optimized motion planning for one arm and real-time telecontrol with human intelligence for the other. The analysis and simulation results showed it can achieve collision and singularity free for dual arms with enhanced dexterity, safety, and efficiency.
Dealing with Typos for BERT-based Passage Retrieval and Ranking

Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers. These methods have been shown to effectively model the semantic matching between queries and passages, also in presence of keyword mismatch, i.e. passages that are relevant to a query but do not contain important query keywords. In this paper we consider the Dense Retriever (DR), a passage retrieval method, and the BERT re-ranker, a popular passage re-ranking method. In this context, we formally investigate how these models respond and adapt to a specific type of keyword mismatch – that caused by keyword typos occurring in queries. Through empirical investigation, we find that typos can lead to a significant drop in retrieval and ranking effectiveness. We then propose a simple typos-aware training framework for DR and BERT re-ranker to address this issue. Our experimental results on the MS MARCO passage ranking dataset show that, with our proposed typos-aware training, DR and BERT re-ranker can become robust to typos in queries, resulting in significantly improved effectiveness compared to models trained without appropriately accounting for typos.
Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line Systems

Maintenance scheduling is a complex decision-making problem in the production domain, where a number of maintenance tasks and resources has to be assigned and scheduled to production entities in order to prevent unplanned production downtime. Intelligent maintenance strategies are required that are able to adapt to the dynamics and different conditions of production systems. The paper introduces a deep reinforcement learning approach for condition-oriented maintenance scheduling in flow line systems. Different policies are learned, analyzed and evaluated against a benchmark scheduling heuristic based on reward modelling. The evaluation of the learned policies shows that reinforcement learning based maintenance strategies meet the requirements of the presented use case and are suitable for maintenance scheduling in the shop floor.
Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition

This study investigates an application of a new probabilistic interpretation of a softmax output to Open-Set Recognition (OSR). Softmax is a mechanism wildly used in classiﬁcation and object recognition. However, a softmax mechanism forces a model to operate under a closed-set paradigm, i.e., to predict an object class out of a set of pre-deﬁned labels. This characteristic contributes to eﬃcacy in classiﬁcation, but poses a risk of non-sense prediction in object recognition. Object recognition is often operated under a dynamic and diverse condition. A foreign object—an object of any unprepared class—can be encountered at any time. OSR is intended to address an issue of identifying a foreign object in object recognition. Based on Bayes theorem and the emphasis of conditioning on the context, softmax inference has been re-interpreted. This re-interpretation has led to a new approach to OSR, called Latent Cognizance (LC). Our investigation employs various scenarios, using Imagenet 2012 dataset as well as fooling and open-set images. The ﬁndings support LC hypothesis and show its eﬀectiveness on OSR.
Quantum Sub-Gaussian Mean Estimator

We present a new quantum algorithm for estimating the mean of a real-valued random variable obtained as the output of a quantum computation. Our estimator achieves a nearly-optimal quadratic speedup over the number of classical i.i. d. samples needed to estimate the mean of a heavy-tailed distribution with a sub-Gaussian error rate. This result subsumes (up to logarithmic factors) earlier works on the mean estimation problem that were not optimal for heavy-tailed distributions [Brassard et al., 2002; Brassard et al., 2011], or that require prior information on the variance [Heinrich, 2002; Montanaro, 2015; Hamoudi and Magniez, 2019]. As an application, we obtain new quantum algorithms for the (e,δ)-approximation problem with an optimal dependence on the coefficient of variation of the input random variable.
The history of metal enrichment traced by X-ray observations of high-redshift galaxy clusters

We present the analysis of deep X-ray observations of 10 massive galaxy clusters at redshifts 1.05 < z < 1.71, with the primary goal of measuring the metallicity of the intracluster medium (ICM) at intermediate radii, to better constrain models of the metal enrichment of the intergalactic medium. The targets were selected from X-ray and Sunyaev–Zel’dovich effect surveys, and observed with both the XMM–Newton and Chandra satellites. For each cluster, a precise gas mass profile was extracted, from which the value of r500 could be estimated. This allows us to define consistent radial ranges over which the metallicity measurements can be compared. In general, the data are of sufficient quality to extract meaningful metallicity measurements in two radial bins, r < 0.3r500 and 0.3 < r/r500 < 1.0. For the outer bin, the combined measurement for all 10 clusters, Z/Z⊙ = 0.21 ± 0.09, represents a substantial improvement in precision over previous results. This measurement is consistent with, but slightly lower than, the average metallicity of 0.315 solar measured at intermediate-to-large radii in low-redshift clusters. Combining our new high-redshift data with the previous low-redshift results allows us to place the tightest constraints to date on models of the evolution of cluster metallicity at intermediate radii. Adopting a power-law model of the form Z ∝ (1 + z)γ, we measure a slope $\gamma = -0.5^{+0.4}_{-0.3}$, consistent with the majority of the enrichment of the ICM having occurred at very early times and before massive clusters formed, but leaving open the possibility that some additional enrichment in these regions may have occurred since a redshift of 2.
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings

Rule-based IDS (intrusion detection systems) are being replaced by more robust neural IDS, which demonstrate great potential in the field of Cybersecurity. However, these ML approaches continue to rely on ad-hoc feature engineering techniques, which lack the capacity to vectorize inputs in ways that are fully relevant to the discovery of anomalous cyber activity. We propose a deep end-to-end framework with NLP-inspired components for identifying potentially malicious behaviors on enterprise computer networks. We also demonstrate the efficacy of this technique on the recently released DARPA OpTC data set.
Manipulation of spin transport in graphene/transition metal dichalcogenide heterobilayers upon twisting

Proximity effects between layered materials trigger a plethora of novel and exotic quantum transport phenomena. Besides, the capability to modulate the nature and strength of proximity effects by changing crystalline and interfacial symmetries offers a vast playground to optimize physical properties of relevance for innovative applications. In this work, we use large-scale first principles calculations to demonstrate that strain and twist-angle strongly vary the spin–orbit coupling (SOC) in graphene/transition metal dichalcogenide heterobilayers. Such a change results in a modulation of the spin relaxation times by up to two orders of magnitude. Additionally, the relative strengths of valley-Zeeman and Rashba SOC can be tailored upon twisting, which can turn the system into an ideal Dirac–Rashba regime or generate transitions between topological states of matter. These results shed new light on the debated variability of SOC and clarify how lattice deformations can be used as a knob to control spin transport. Our outcomes also suggest complex spin transport in polycrystalline materials, due to the random variation of grain orientation, which could reflect in large spatial fluctuations of SOC fields.