Miracle-dz/UniFAR-FTUs huggingface dataset:
Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection

Monocular 3D lane detection is a challenging task due to its lack of depth information. A popular solution is to first transform the front-viewed (FV) images or features into the bird-eye-view (BEV) space with inverse perspective mapping (IPM) and detect lanes from BEV features. However, the reliance of IPM on flat ground assumption and loss of context information make it inaccurate to restore 3D information from BEV representations. An attempt has been made to get rid of BEV and predict 3D lanes from FV representations directly, while it still underperforms other BEV-based methods given its lack of structured representation for 3D lanes. In this paper, we define 3D lane anchors in the 3D space and propose a BEV-free method named Anchor3DLane to predict 3D lanes directly from FV representations. 3D lane anchors are projected to the FV features to extract their features which contain both good structural and context information to make accurate predictions. In addition, we also develop a global optimization method that makes use of the equal-width property between lanes to reduce the lateral error of predictions. Extensive experiments on three popular 3D lane detection benchmarks show that our Anchor3DLane outperforms previous BEV-based methods and achieves state-of-the-art performances. The code is available at: https://github.com/tusenai/Anchor3DLane.
Unsupervised Broadcast News Summarization; a Comparative Study on Maximal Marginal Relevance (MMR) and Latent Semantic Analysis (LSA)

The automatic speech summarization methods traditionally are classified into two groups: supervised and unsupervised methods. Supervised methods rely on a set of features, while unsupervised methods perform summarization through a set of rules. Among unsupervised automatic speech summarization methods, Latent Semantic Analysis (LSA) and Maximal Marginal Relevance (MMR) are so famous. This study set out to peruse the overall efficacy of two aforementioned unsupervised methods in summarization of Persian broadcast news transcriptions. The results justify the superiority of LSA to MMR during generic summarization. This is while MMR achieves better results in query-based summarization.
Twisting attosecond pulse trains by amplitude-polarization IR pulses

Natively, atomic and molecular processes develop in a sub-femtosecond time scale. In order to, for instance, track and capture the electron motion in that scale we need suitable `probes'. Attosecond pulses configure the most appropriate tools for such a purpose. These ultrashort bursts of light are generated when a strong laser field interacts with matter and high-order harmonics of the driving source are produced. In this work, we propose a way to twist attosecond pulse trains. In our scheme, each of the attosecond pulses in the train has a well-defined linear polarization, but with a different polarization angle between them. To achieve this goal, we consider an infrared pulse with a particular polarization state, called amplitude polarization. This kind of pulse was experimentally synthesized in previous works. Our twisted attosecond pulse train is then obtained by nonlinear driving an atomic system with that laser source, through the high-order harmonics generation phenomenon. We reach a high degree of control in the polarization of the ultrashort coherent XUV-generated radiation. Through quantum mechanical simulations, supplemented with signal processing tools, we are able to dissect the underlying physics of the generation process. We are confident these polarized-sculpted XUV sources will play an instrumental role in future pump-probe-based experiments.
Delay-Doppler Domain Tomlinson-Harashima Precoding for OTFS-Based Downlink MU-MIMO Transmissions: Linear Complexity Implementation and Scaling Law Analysis

Orthogonal time frequency space (OTFS) modulation is a recently proposed delay-Doppler (DD) domain communication scheme, which has shown promising performance in general wireless communications, especially over high-mobility channels. In this paper, we investigate DD domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output OTFS (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain interference in a symbol-by-symbol manner. We also study the achievable rate performance for the proposed scheme by leveraging the information-theoretical equivalent models. In particular, we show that the proposed scheme can achieve a near optimal performance in the high signal-to-noise ratio (SNR) regime. More importantly, scaling laws for achievable rates with respect to number of antennas and users are derived, which indicate that the achievable rate increases logarithmically with the number of antennas and linearly with the number of users. Our numerical results align well with our findings and also demonstrate a significant improvement compared to existing MU-MIMO schemes on OTFS and orthogonal frequency-division multiplexing (OFDM).
The origin of the optical anisotropy of solvent cast polymeric films

The optical mode structures of solvent cast films of different polymers have been measured with a single prism film coupler. The allowed modes define the index of refraction for light polarized in and perpendicular to the plane of the film. It is shown that the solvent coating process preferentially aligns the polymeric chain backbone in the plane of the film and that this biaxial orientation may produce birefringent films in optically anisotropic resins. The sign of this birefringence depends upon the orientation of the dominating polarizable group relative to the chain backbone in the plasticized melt. The magnitude of this effect reflects both the optical anisotropy of the group and the steric constraints to its free rotation.
Stealthy Backdoor Attack for Code Models

Code models, such as CodeBERT and CodeT5, offer general-purpose representations of code and play a vital role in supporting downstream automated software engineering tasks. Most recently, code models were revealed to be vulnerable to backdoor attacks. A code model that is backdoor-attacked can behave normally on clean examples but will produce pre-defined malicious outputs on examples injected with triggers that activate the backdoors. Existing backdoor attacks on code models use unstealthy and easy-to-detect triggers. This paper aims to investigate the vulnerability of code models with stealthy backdoor attacks. To this end, we propose AFRAIDOOR (Adversarial Feature as Adaptive Backdoor). AFRAIDOOR achieves stealthiness by leveraging adversarial perturbations to inject adaptive triggers into different inputs. We evaluate AFRAIDOOR on three widely adopted code models (CodeBERT, PLBART and CodeT5) and two downstream tasks (code summarization and method name prediction). We find that around 85% of adaptive triggers in AFRAIDOOR bypass the detection in the defense process. By contrast, only less than 12% of the triggers from previous work bypass the defense. When the defense method is not applied, both AFRAIDOOR and baselines have almost perfect attack success rates. However, once a defense is applied, the success rates of baselines decrease dramatically to 10.47% and 12.06%, while the success rate of AFRAIDOOR are 77.05% and 92.98% on the two tasks. Our finding exposes security weaknesses in code models under stealthy backdoor attacks and shows that the state-of-the-art defense method cannot provide sufficient protection. We call for more research efforts in understanding security threats to code models and developing more effective countermeasures.
Effects of Environmentally Induced Asymmetries on Hurricane Intensity: A Numerical Study

Abstract The influence of uniform large-scale flow, the beta effect, and vertical shear of the environmental flow on hurricane intensity is investigated in the context of the induced convective or potential vorticity asymmetries in the core region with a hydrostatic primitive equation hurricane model. In agreement with previous studies, imposition of one of these environmental effects weakens the simulated tropical cyclones. In response to the environmental influence, significant wavenumber-1 asymmetries develop. Asymmetric and symmetric tendencies of the mean radial and azimuthal winds and temperature associated with the environment-induced convective asymmetries are evaluated. The inhibiting effects of environmental influences are closely associated with the resulting eddy momentum fluxes, which tend to decelerate tangential and radial winds in the inflow and outflow layers. The corresponding changes in the symmetric circulation tend to counteract the deceleration effect. The net effect is a moderate we.
Multi-scale hybrid sensor nodes for acceleration-impedance monitoring for steel structural connections

This paper presents hybrid structural health monitoring of steel girder connections using wireless acceleration and impedance sensor nodes based on Imote2-platform. To achieve the research objective, the feasibility of the sensor nodes is examined about its performance for vibration-based global monitoring and impedance-based local monitoring in the structural systems as the following approaches. First, a damage monitoring scheme is described in parallel with global vibration-based methods and local impedance-based methods. Second, multi-scale sensor nodes that enable combined acceleration-impedance monitoring are described on the design of hardware components and embedded software to operate. Third, the performances of the multi-scale sensor nodes are experimentally evaluated from damage monitoring in a lab-scaled steel girder with bolted connection joints.
Graph Contrastive Learning for Multi-omics Data

Advancements in technologies related to working with omics data require novel computation methods to fully leverage information and help develop a better understanding of human diseases. This paper studies the effects of introducing graph contrastive learning to help leverage graph structure and information to produce better representations for downstream classification tasks for multi-omics datasets. We present a learnining framework named Multi-Omics Graph Contrastive Learner(MOGCL) which outperforms several aproaches for integrating multi-omics data for supervised learning tasks. We show that pre-training graph models with a contrastive methodology along with fine-tuning it in a supervised manner is an efficient strategy for multi-omics data classification.
Fuzzers for stateful systems: Survey and Research Directions

Fuzzing is a very effective testing methodology to find bugs. In a nutshell, a fuzzer sends many slightly malformed messages to the software under test, hoping for crashes or incorrect system behaviour. The methodology is relatively simple, although applications that keep internal states are challenging to fuzz. The research community has responded to this challenge by developing fuzzers tailored to stateful systems, but a clear understanding of the variety of strategies is still missing. In this paper, we present the first taxonomy of fuzzers for stateful systems and provide a systematic comparison and classification of these fuzzers.
The one step fixed-lag particle smoother as a strategy to improve the prediction step of particle filtering

Sequential Monte Carlo methods have been a major breakthrough in the field of numerical signal processing for stochastic dynamical state-space systems with partial and noisy observations. However, these methods still present certain weaknesses. One of the most fundamental is the degeneracy of the filter due to the impoverishment of the particles: the prediction step allows the particles to explore the state-space and can lead to the impoverishment of the particles if this exploration is poorly conducted or when it conflicts with the following observation that will be used in the evaluation of the likelihood of each particle. In this article, in order to improve this last step within the framework of the classic bootstrap particle filter, we propose a simple approximation of the one step fixed- lag smoother. At each time iteration, we propose to perform additional simulations during the prediction step in order to improve the likelihood of the selected particles. Les méthodes de Monte Carlo séquentielles ont constitué une percée majeure dans le domaine du traitement numérique du signal pour les systèmes dynamiques stochastiques à espace d'état avec observations partielles et bruitées. Cependant, ces méthodes présentent encore certaines faiblesses. L'une des plus fondamentales est la dégénérescence du filtre due à l'appauvrissement des particules : l'étape de prédiction permet aux particules d'explorer l'espace d'état et peut conduire à l'appauvrissement des particules si cette exploration est mal menée ou lorsqu'elle entre en conflit avec l'observation suivante qui sera utilisée dans l'évaluation de la vraisemblance de chaque particule. Dans cet article, afin d'améliorer cette dernière étape dans le cadre du filtre particulaire bootstrap classique, nous proposons une approximation simple du lisseur à retard fixe à un pas. A chaque itération temporelle, nous proposons d'effectuer des simulations supplémentaires pendant l'étape de prédiction afin d'améliorer la vraisemblance des particules sélectionnées.
Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods

Sharpness is an almost generic assumption in continuous optimization that bounds the distance from minima by objective function suboptimality. It leads to the acceleration of first-order methods via restarts. However, sharpness involves problem-specific constants that are typically unknown, and previous restart schemes reduce convergence rates. Moreover, such schemes are challenging to apply in the presence of noise or approximate model classes (e.g., in compressive imaging or learning problems), and typically assume that the first-order method used produces feasible iterates. We consider the assumption of approximate sharpness, a generalization of sharpness that incorporates an unknown constant perturbation to the objective function error. This constant offers greater robustness (e.g., with respect to noise or relaxation of model classes) for finding approximate minimizers. By employing a new type of search over the unknown constants, we design a restart scheme that applies to general first-order methods and does not require the first-order method to produce feasible iterates. Our scheme maintains the same convergence rate as when assuming knowledge of the constants. The rates of convergence we obtain for various first-order methods either match the optimal rates or improve on previously established rates for a wide range of problems. We showcase our restart scheme on several examples and point to future applications and developments of our framework and theory.
BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor

Random forests are machine learning methods characterised by high performance and robustness to overfitting. However, since multiple learners are combined, they are not as interpretable as a single decision tree. In this work we propose a novel method that is Building Explanations through a LocalLy AccuraTe Rule EXtractor (Bellatrex), which is able to explain the forest prediction for a given test instance with only a few diverse rules. Starting from the decision trees generated by a random forest, our method: 1) pre-selects a subset of the rules used to make the prediction; 2) creates a vector representation of such rules; 3) projects them to a low-dimensional space; 4) clusters such representations to pick a rule from each cluster to explain the instance prediction. We test the effectiveness of Bellatrex on 89 real-world datasets and we demonstrate the validity of our method for binary classification, regression, multi-label classification and time-to-event tasks. To the best of our knowledge, it is the first time that an interpretability toolbox can handle all these tasks within the same framework. We also show that Bellatrex is able to approximate the performance of the corresponding ensemble model in all considered tasks, and it does so while selecting at most three rules from the whole forest. Finally, a comparison with similar methods in literature also shows that our proposed approach substantially outperforms other explainable toolboxes in terms of predictive performance.
CHARM: Composing Heterogeneous AcceleRators for Matrix Multiply on Versal ACAP Architecture

Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning applications. To cope with the high computation demands of these applications, heterogeneous architectures featuring both FPGA and dedicated ASIC accelerators have emerged as promising platforms. For example, the AMD/Xilinx Versal ACAP architecture combines general-purpose CPU cores and programmable logic (PL) with AI Engine processors (AIE) optimized for AI/ML. An array of 400 AI Engine processors executing at 1 GHz can theoretically provide up to 6.4 TFLOPs performance for 32-bit floating-point (fp32) data. However, machine learning models often contain both large and small MM operations. While large MM operations can be parallelized efficiently across many cores, small MM operations typically cannot. In our investigation, we observe that executing some small MM layers from the BERT natural language processing model on a large, monolithic MM accelerator in Versal ACAP achieved less than 5% of the theoretical peak performance. Therefore, one key question arises: How can we design accelerators to fully use the abundant computation resources under limited communication bandwidth for end-to-end applications with multiple MM layers of diverse sizes? We identify the biggest system throughput bottleneck resulting from the mismatch of massive computation resources of one monolithic accelerator and the various MM layers of small sizes in the application. To resolve this problem, we propose the CHARM framework to compose multiple diverse MM accelerator architectures working concurrently towards different layers within one application. CHARM includes analytical models which guide design space exploration to determine accelerator partitions and layer scheduling. To facilitate the system designs, CHARM automatically generates code, enabling thorough onboard design verification. We deploy the CHARM framework for four different deep learning applications, including BERT, ViT, NCF, MLP, on the AMD/Xilinx Versal ACAP VCK190 evaluation board. Our experiments show that we achieve 1.46 TFLOPs, 1.61 TFLOPs, 1.74 TFLOPs, and 2.94 TFLOPs inference throughput for BERT, ViT, NCF, MLP, respectively, which obtain 5.40x, 32.51x, 1.00x and 1.00x throughput gains compared to one monolithic accelerator.
Piecewise deterministic sampling with splitting schemes

We introduce novel Markov chain Monte Carlo (MCMC) algorithms based on numerical approximations of piecewise-deterministic Markov processes obtained with the framework of splitting schemes. We present unadjusted as well as adjusted algorithms, for which the asymptotic bias due to the discretisation error is removed applying a non-reversible Metropolis-Hastings filter. In a general framework we demonstrate that the unadjusted schemes have weak error of second order in the step size, while typically maintaining a computational cost of only one gradient evaluation of the negative log-target function per iteration. Focusing then on unadjusted schemes based on the Bouncy Particle and Zig-Zag samplers, we provide conditions ensuring geometric ergodicity and consider the expansion of the invariant measure in terms of the step size. We analyse the dependence of the leading term in this expansion on the refreshment rate and on the structure of the splitting scheme, giving a guideline on which structure is best. Finally, we illustrate the competitiveness of our samplers with numerical experiments on a Bayesian imaging inverse problem and a system of interacting particles.
PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images

Touch plays a fundamental role in manipulation for humans; however, machine perception of contact and pressure typically requires invasive sensors. Recent research has shown that deep models can estimate hand pressure based on a single RGB image. However, evaluations have been limited to controlled settings since collecting diverse data with ground-truth pressure measurements is difficult. We present a novel approach that enables diverse data to be captured with only an RGB camera and a cooperative participant. Our key insight is that people can be prompted to apply pressure in a certain way, and this prompt can serve as a weak label to supervise models to perform well under varied conditions. We collect a novel dataset with 51 participants making fingertip contact with diverse objects. Our network, PressureVision++, outperforms human annotators and prior work. We also demonstrate an application of PressureVision++ to mixed reality where pressure estimation allows everyday surfaces to be used as arbitrary touch-sensitive interfaces. Code, data, and models are available online.
Spectrum Monitoring and Analysis in Urban and Rural Environments at Different Altitudes

Due to the scarcity of spectrum resources, the emergence of new technologies and ever-increasing number of wireless devices operating in the radio frequency spectrum lead to data congestion and interference. In this work, we study the effect of altitude on sub-6 GHz spectrum measurement results obtained at a Helikite flying over two distinct scenarios; i.e., urban and rural environments. Specifically, we aim at investigating the spectrum occupancy of various long-term evolution (LTE), 5th generation (5G) and citizens broadband radio service (CBRS) bands utilized in the United States for both uplink and downlink at altitudes up to 180 meters. Our results reveal that generally the mean value of the measured power increases as the altitude increases where the line-of-sight links with nearby base stations is more available. SigMF-compliant spectrum measurement datasets used in this paper covering all the bands between 100 MHz to 6 GHz are also provided.
Many-body hybrid Excitons in Organic-Inorganic van der Waals Heterostructures

The coherent many-body interaction at the organic-inorganic interface can give rise to intriguing hybrid excitons that combine the advantages of the Wannier-Mott and Frenkel excitons simultaneously. Unlike the 2D inorganic heterostructures that suffer from moment mismatch, the hybrid excitons formed at the organic-inorganic interface have a momentum-direct nature, which have yet to be explored. Here, we report hybrid excitons at the copper phthalocyanine/molybdenum diselenide (CuPc/MoSe2) interface with strong molecular orientation dependence using low-temperature photoluminescence spectroscopy. The new emission peaks observed in the CuPc/MoSe2 heterostructure indicate the formation of interfacial hybrid excitons. The density functional theory (DFT) calculation confirms the strong hybridization between the lowest unoccupied molecular orbital (LUMO) of CuPc and the conduction band minimum (CBM) of MoSe2, suggesting that the hybrid excitons consist of electrons extended in both layers and holes confined in individual layers. The temperature-dependent measurements show that the hybrid excitons can gain the signatures of the Frenkel excitons of CuPc and the Wannier-Mott excitons of MoSe2 simultaneously. The out-of-plane molecular orientation is used to tailor the interfacial hybrid exciton states. Our results reveal the hybrid excitons at the CuPc/MoSe2 interface with tunability by molecular orientation, which suggests that the emerging organic-inorganic heterostructure can be a promising platform for many-body exciton physics.