Miracle-dz/UniFAR-FTUs huggingface dataset:
MetaEmu: An Architecture Agnostic Rehosting Framework for Automotive Firmware

In this paper we present MetaEmu, an architecture-agnostic framework geared towards rehosting and security analysis of automotive firmware. MetaEmu improves over existing rehosting environments in two ways: Firstly, it solves the hitherto open-problem of a lack of generic Virtual Execution Environments (VXEs) by synthesizing processor simulators from Ghidra's language definitions. Secondly, MetaEmu can rehost and analyze multiple targets, each of different architecture, simultaneously, and share analysis facts between each target's analysis environment, a technique we call inter-device analysis. We show that the flexibility afforded by our approach does not lead to a performance trade-off---MetaEmu lifts rehosted firmware to an optimized intermediate representation, and provides performance comparable to existing emulation tools, such as Unicorn. Our evaluation spans five different architectures, bare-metal and RTOS-based firmware, and three kinds of automotive Electronic Control Unit (ECU) from four distinct vendors---none of which can be rehosted or emulated by current tools, due to lack of processor support. Further, we show how MetaEmu enables a diverse set of analyses by implementing a fuzzer, a symbolic executor for solving peripheral access checks, a CAN ID reverse engineering tool, and an inter-device coverage tracker.
Going Beyond Approximation: Encoding Constraints for Explainable Multi-hop Inference via Differentiable Combinatorial Solvers

Integer Linear Programming (ILP) provides a viable mechanism to encode explicit and controllable assumptions about explainable multi-hop inference with natural language. However, an ILP formulation is non-differentiable and cannot be integrated into broader deep learning architectures. Recently, Thayaparan et al. (2021a) proposed a novel methodology to integrate ILP with Transformers to achieve end-to-end differentiability for complex multi-hop inference. While this hybrid framework has been demonstrated to deliver better answer and explanation selection than transformer-based and existing ILP solvers, the neuro-symbolic integration still relies on a convex relaxation of the ILP formulation, which can produce sub-optimal solutions. To improve these limitations, we propose Diff-Comb Explainer, a novel neuro-symbolic architecture based on Differentiable BlackBox Combinatorial solvers (DBCS) (Pogan\v{c}i\'c et al., 2019). Unlike existing differentiable solvers, the presented model does not require the transformation and relaxation of the explicit semantic constraints, allowing for direct and more efficient integration of ILP formulations. Diff-Comb Explainer demonstrates improved accuracy and explainability over non-differentiable solvers, Transformers and existing differentiable constraint-based multi-hop inference frameworks.
A Survey on Visual Map Localization Using LiDARs and Cameras

As the autonomous driving industry is slowly maturing, visual map localization is quickly becoming the standard approach to localize cars as accurately as possible. Owing to the rich data returned by visual sensors such as cameras or LiDARs, researchers are able to build different types of maps with various levels of details, and use them to achieve high levels of vehicle localization accuracy and stability in urban environments. Contrary to the popular SLAM approaches, visual map localization relies on pre-built maps, and is focused solely on improving the localization accuracy by avoiding error accumulation or drift. We define visual map localization as a two-stage process. At the stage of place recognition, the initial position of the vehicle in the map is determined by comparing the visual sensor output with a set of geo-tagged map regions of interest. Subsequently, at the stage of map metric localization, the vehicle is tracked while it moves across the map by continuously aligning the visual sensors' output with the current area of the map that is being traversed. In this paper, we survey, discuss and compare the latest methods for LiDAR based, camera based and cross-modal visual map localization for both stages, in an effort to highlight the strength and weakness of each approach.
Maximal Directional Derivatives in Laakso Space

We investigate the connection between maximal directional deriva- tives and diﬀerentiability for Lipschitz functions deﬁned on Laakso space. We show that maximality of a directional derivative for a Lipschitz function im- plies diﬀerentiability only for a σ -porous set of points. On the other hand, the distance to a ﬁxed point is diﬀerentiable everywhere except for a σ -porous set of points. This behavior is completely diﬀerent to the previously studied settings of Euclidean spaces and Carnot groups.
A Riemannian Optimization Approach to Clustering Problems

This paper considers the optimization problem in the form of $\min_{X \in \mathcal{F}_v} f(x) + \lambda \|X\|_1,$ where $f$ is smooth, $\mathcal{F}_v = \{X \in \mathbb{R}^{n \times q} : X^T X = I_q, v \in \mathrm{span}(X)\}$, and $v$ is a given positive vector. The clustering models including but not limited to the models used by $k$-means, community detection, and normalized cut can be reformulated as such optimization problems. It is proven that the domain $\mathcal{F}_v$ forms a compact embedded submanifold of $\mathbb{R}^{n \times q}$ and optimization-related tools including a family of computationally efficient retractions and an orthonormal basis of any normal space of $\mathcal{F}_v$ are derived. An inexact accelerated Riemannian proximal gradient method that allows adaptive step size is proposed and its global convergence is established. Numerical experiments on community detection in networks and normalized cut for image segmentation are used to demonstrate the performance of the proposed method.
Motivic integration on special rigid varieties and the motivic integral identity conjecture

We construct an equivariant motivic integration on rigid varieties, which is a continuation of our recent work on the theory of equivariant motivic integration on special formal schemes. By showing that two formal models of a special smooth rigid variety can be dominated by a third formal model we are able to deﬁne a notion of motivic volume on special smooth rigid varieties. We then prove that this motivic volume can be extended to a homomorphism from a certain Grothendieck ring of special smooth rigid varieties to the classical Grothendieck ring of varieties. Applying this theory we prove the motivic integral identity conjecture by Kontsevich and Soibelman.
Aperiodic Structures in Condensed Matter: Fundamentals and Applications

Orderings of Matter Periodic thinking in physical sciences The realm of periodic crystals Superimposed periods Order at different scales Scale invariance and fractal geometry Hierarchical architecture of biomolecules The role of correlated disorder Long-range correlations: the DNA case The Notion of Aperiodic Crystal Order without periodicity Bringing in icosahedral thought Beautiful forbidden symmetries Unveiling Pythagorean dreams Crystallography in six dimensions IUCr: A new definition of crystal Aperiodic crystals classification schemes Some milestones in the aperiodic crystal route Quasiperiodic Crystals Quasicrystalline alloys Quasicrystals as a hierarchy of clusters Electronic structure of icosahedral quasicrystals Phenomenological transport models Aperiodically Layered Materials A novel design: Fibonacci superlattices General aperiodic heterostructures Signatures of quasiperiodicity One-Dimensional Quasiperiodic Models Effective one-dimensional systems Classification schemes based on spectral properties Remarkable properties of singular continuous spectra Frequency spectra of general Fibonacci lattices Nature of critical states in aperiodic systems The role of critical states in transport properties Transport properties of Fibonacci superlattices Beyond one-dimensional models The Aperiodic Crystal of Life The double helix Electronic structure of nucleic acids Charge transfer in DNA: what experiments say Modeling charge migration in DNA Long-range correlations: the biophysicist viewpoint Exploiting Aperiodic Order in Technological Devices Periodic versus aperiodic Layered systems Photonic and phononic quasicrystals Complex metallic alloys DNA-based nanoelectronics Novel Designs Based on the Aperiodic Order Order and design: the technological viewpoint One-dimensional designs Two- and three-dimensional designs Beyond quasiperiodic order: aperiodicity by design Some open questions Mathematical Tools Almost periodic and quasiperiodic functions Transfer matrix technique Trace map formalism Renormalization matrix methods Diagnostic tools
Enhancing the Convergence Properties of the BLP (1995) Contraction Mapping

In the wake of Dub´e, Fox and Su (2012), this paper (i) analyzes the consequences for the BLP Contraction Mapping (Berry, Levinsohn and Pakes, 1995) of setting the inner-loop convergence tolerance at the required level of in D 10 14 to avoid propagation of approximation error from inner to outer loop, and (ii) proposes acceleration as a viable alternative within the confines of the Nested-Fixed Point paradigm (Rust, 1987) to enhance the convergence properties of the inner-loop BLP Contraction Mapping. Drawing upon the equivalence between nonlinear rootfinding and fixed-point iter- ation, we introduce and compare two alternative methods specifically designed for handling large-scale nonlinear problems, in particular the derivative-free spectral al- gorithm for nonlinear equations (La Cruz et al., 2006, DF-SANE), and the squared polynomial extrapolation method for fixed-point acceleration (Varadhan and Roland, 2008, SQUAREM). Running a Monte Carlo study with specific scenarios and with Newton-Raphson as a benchmark, we study the characteristics of these algorithms in terms of (i) speed, (ii) robustness, and (iii) quality of approximation. Under the worst of circumstances we find that (i) SQUAREM is faster (up to more than five times as fast) and more robust (up to 14 percentage points better suc- cess rate) than BLP while attaining a comparable quality of approximation, and (ii) also outperforms DF-SANE in nearly all scenarios. Eliminating averaging bias against more robust algorithms, a performance profile subsequently shows that (iii) SQUAREM is both faster, delivers the best quality approximation, and is more robust than BLP, DF-SANE and Newton-Raphson.
Real-time Egyptian License Plate Detection and Recognition using YOLO

—Automatic License Plate Detection and Recognition (ALPR) is one of the most significant technologies in intelligent transportation and surveillance across the world. It has many challenges because it affects by many parameters such as the country’s layout, colors, language, fonts, and several environmen- tal conditions so, there isn’t a consolidated ALPR system for all countries. Many ALPR methods have been proposed based on traditional image processing and machine learning algorithms since there aren’t enough datasets, particularly in the Arabic language. In this paper, we proposed a real-time ALPR system for the Egyptian license plate (LP) detection and recognition using Tiny-YOLOV3. It consists of two deep convolutional neural networks. The experimental results in the first available publicly Egyptian Automatic License Plate (EALPR) dataset show the proposed system is more robust in detecting and recognizing the Egyptian license plates and gives mean average precision values of 97.89% and 92.46% for LP detection and character recognition, respectively.
Complex network model for railway timetable stability optimisation

Recent studies on train timetable are paying more and more attention to the dynamic characteristics. Among the dynamic characteristics, stability is a most important one, which determines the capacity of the train timetable to tolerate the disturbance in the train operation process. In this study, the authors build a complex network model to describe the train timetable, making it possible to utilise the complex network theory to study the train timetable optimisation problem. Then, they design the solving algorithm to solve the problem. Finally, they present a computing case to prove the approach to improve the train timetable stability is practical. The approach proposed in this study can generate referential advice for the railway operators design the train timetable.
Data Collection for Empirically Determining the Necessary Information for Smooth Handover in Dialogue

Despite recent advances, dialogue systems still struggle to achieve fully autonomous transactions. Therefore, when a system encounters a problem, human operators need to take over the dialogue to complete the transaction. However, it is unclear what information should be presented to the operator when this handover takes place. In this study, we conducted a data collection experiment in which one of two operators talked to a user and switched with the other operator periodically while exchanging notes when the handovers took place. By examining these notes, it is possible to identify the information necessary for handing over the dialogue. We collected 60 dialogues in which two operators switched periodically while performing chat, consultation, and sales tasks in dialogue. We found that adjacency pairs are a useful representation for recording conversation history. In addition, we found that key-value-pair representation is also useful when there are underlying tasks, such as consultation and sales.
Interpretable Models Capture the Complex Relationship Between Climate Indices and Fire Season Intensity in Maritime Southeast Asia

There have been many extreme fire seasons in Maritime Southeast Asia (MSEA) over the last two decades. Fires, in turn, are a major driver of atmospheric carbon monoxide (CO) variability, especially in the Southern Hemisphere. Here we attempt to maximize the amount of CO variability that can be explained during fire season in MSEA (defined as September through December) via human‐interpretable statistical models that use only climate mode indices as predictor variables and are trained on data from 2001 to 2019. We expand upon previous work through the complexity at which we study the connections between climate mode indices and atmospheric CO (an indicator of fire intensity). Specifically, we present three modeling advancements. First, we analyze five different climate modes at a weekly timescale, increasing explained variability by 15% over models a monthly timescale. Second, we accommodate multiple lead times for each climate mode index, finding that some indices have very different effects on CO at different lead times. Finally, we model the interactions between climate mode indices at a weekly timescale, providing a framework for studying more complex interactions than previous work. Furthermore, we perform a stability analysis and show that our model for the MSEA region is robust, adding weight to the scientific interpretation of selected model terms. We believe the relationships quantified here provide new understanding of a significant mode of variability in MSEA, specific lead times for use in forecasts, and a method for evaluating climate mode‐CO relationships in climate model output.
MMDAG: Multimodal Directed Acyclic Graph Network for Emotion Recognition in Conversation

Emotion recognition in conversation is important for an empathetic dialogue system to understand the user’s emotion and then generate appropriate emotional responses. However, most previous researches focus on modeling conversational contexts primarily based on the textual modality or simply utilizing multimodal information through feature concatenation. In order to exploit multimodal information and contextual information more effectively, we propose a multimodal directed acyclic graph (MMDAG) network by injecting information flows inside modality and across modalities into the DAG architecture. Experiments on IEMOCAP and MELD show that our model outperforms other state-of-the-art models. Comparative studies validate the effectiveness of the proposed modality fusion method.
Understandings of Vietnamese EFL Students’ Preferences on Written Corrective Feedback: A Case Study

— Written corrective feedback (WCF) is significantly important for students at all levels as it can help them enhance their second language writing ability after making errors in writing. However, without urgent understandings of students’ preferences, the effectiveness of WCF definitely weakens. This study, thus, aimed at investigating students’ preferences for WCF. To achieve this aim, relevant literature on WCF in terms of value, timing, sources, strategies, amount, and focus of WCF and students’ preferences were reviewed. Based upon this conceptual framework, the survey study with a questionnaire was conducted at Binh Thuan Vocational College in Vietnam with the participation of 35 IT-majored students. The findings of this study showed that these students had positive perceptions and preferences of WCF in their writing learning. Based on the research findings, the paper concluded with pedagogical implications and a recommendation for further study in the line of research on WCF.
Do the dynamics of the city environment influence us, and how? Answer through the Machine Learning Approaches

As a part of research with a team at the ETH Zurich, Switzerland, we investigated a detailed answer to the question “Do the dynamics of the city environment influence us and how? ” We adopted Data Science and Machine Learning approaches for investigating such relationship between the dynamics of the city environment and the human perception. A complex and messy dataset was analyzed and resolved for this purpose. The machine learning analysis and its results will focus on answering the following questions: (1) What are the factors that need to be accounted for from the city environment? (2) How can we collect and process messy and complex datasets? (3) Can we predict the citizen's perception based on an environmental condition? (4) Are we able to infer a detailed relationship between a citizen's perception and the city environment? (5) What are the significant factors that influence a citizen's perception? (6) Does the citizen exhibit positive or negative emotion towards a certain environment? Our investigation through Data Science and Machine Learning approaches answers these questions in detail using data preprocessing techniques such as stationary wavelet transforms; physiological signals processing; and quantification and information fusion. We discover knowledge (answers to the questions) through the use of data science tools like decision tree, fuzzy unor-dered rule induction algorithm, feature analysis, self-organizing map, deep learning, spatial and non-spatial statistics, and plotting correlation maps. Our results reveal qualitative answers to the above questions such as highly sensitive of human’s perception of the environmental conditions, the essential drivers such as temperature and illuminance, and field of view.
RUSAVIC Corpus: Russian Audio-Visual Speech in Cars

We present a new audio-visual speech corpus (RUSAVIC) recorded in a car environment and designed for noise-robust speech recognition. Our goal was to produce a speech corpus which is natural (recorded in real driving conditions), controlled (providing different SNR levels by windows open/closed, moving/parked vehicle, etc.), and adequate size (the amount of data is enough to train state-of-the-art NN approaches). We focus on the problem of audio-visual speech recognition: with the use of automated lip-reading to improve the performance of audio-based speech recognition in the presence of severe acoustic noise caused by road traffic. We also describe the equipment and procedures used to create RUSAVIC corpus. Data are collected in a synchronous way through several smartphones located at different angles and equipped with FullHD video camera and microphone. The corpus includes the recordings of 20 drivers with minimum of 10 recording sessions for each. Besides providing a detailed description of the dataset and its collection pipeline, we evaluate several popular audio and visual speech recognition methods and present a set of baseline recognition results. At the moment RUSAVIC is a unique audio-visual corpus for the Russian language that is recorded in-the-wild condition and we make it publicly available.
A Comparison of Praising Skills in Face-to-Face and Remote Dialogues

Praising behavior is considered to an important method of communication in daily life and social activities. An engineering analysis of praising behavior is therefore valuable. However, a dialogue corpus for this analysis has not yet been developed. Therefore, we develop corpuses for face-to-face and remote two-party dialogues with ratings of praising skills. The corpuses enable us to clarify how to use verbal and nonverbal behaviors for successfully praise. In this paper, we analyze the differences between the face-to-face and remote corpuses, in particular the expressions in adjudged praising scenes in both corpuses, and also evaluated praising skills. We also compare differences in head motion, gaze behavior, facial expression in high-rated praising scenes in both corpuses. The results showed that the distribution of praising scores was similar in face-to-face and remote dialogues, although the ratio of the number of praising scenes to the number of utterances was different. In addition, we confirmed differences in praising behavior in face-to-face and remote dialogues.
MBHrelation between SMBHs and the velocity dispersion of globular cluster systems

ABSTRACT We ﬁnd evidence that the mass M BH of central supermassive black holes (SMBHs)correlates with the velocity dispersion σ GC of globular cluster systems of their hostgalaxies. This extends the well-known M BH −σ sph relation between black hole massand velocity dispersion of the host spheroidal component. We compile published mea-surements of both M BH and σ GC for a sample of 13 systems and ﬁnd the relationlog(M BH ) = α + βlog(σ GC /200) with α = 8.63 ±0.09 and β = 3.76 ±0.52. We alsoconsider blue (metal-poor) and red (metal-rich) globular clusters sub-populations sep-arately and obtain a surprisingly tight correlation using only the velocity dispersionσ redGC of the red clusters with α = 8.73 ±0.09 and β = 3.84 ±0.52 and an intrinsicscatter e 0 = 0.22 dex compared to e 0 = 0.27 dex for the M BH −σ sph of our sample. Weuse this M BH −σ redGC relation to estimate the central black hole mass in ﬁve galaxiesfor which σ redGC is measured.Key words: black hole physics – galaxies: evolution – galaxies: nuclei – galaxies:star clusters: general – galaxies: fundamental parameters – globular clusters: general