Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial-Information, Longitudinal Cyber Attacks on LiDAR in Autonomous Vehicles

What happens to an autonomous vehicle (AV) if its data are adversarially compromised? Prior security studies have addressed this question through mostly unrealistic threat models, with limited practical relevance, such as white-box adversarial learning or nanometer-scale laser aiming and spoofing. With growing evidence that cyber threats pose real, imminent danger to AVs and cyber-physical systems (CPS) in general, we present and evaluate a novel AV threat model: a cyber-level attacker capable of disrupting sensor data but lacking any situational awareness. We demonstrate that even though the attacker has minimal knowledge and only access to raw data from a single sensor (i.e., LiDAR), she can design several attacks that critically compromise perception and tracking in multi-sensor AVs. To mitigate vulnerabilities and advance secure architectures in AVs, we introduce two improvements for security-aware fusion: a probabilistic data-asymmetry monitor and a scalable track-to-track fusion of 3D LiDAR and monocular detections (T2T-3DLM); we demonstrate that the approaches significantly reduce attack effectiveness. To support objective safety and security evaluations in AVs, we release our security evaluation platform, AVsec, which is built on security-relevant metrics to benchmark AVs on gold-standard longitudinal AV datasets and AV simulators.
Statistical Inference for Large-dimensional Tensor Factor Model by Iterative Projections

Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order large-dimensional tensor time series, and have wide applications in economics, finance and medical imaging. In this paper, we propose a projection estimator for the Tucker-decomposition based TFM, and provide its least-square interpretation which parallels to the least-square interpretation of the Principal Component Analysis (PCA) for the vector factor model. The projection technique simultaneously reduces the dimensionality of the signal component and the magnitudes of the idiosyncratic component tensor, thus leading to an increase of the signal-to-noise ratio. We derive a convergence rate of the projection estimator of the loadings and the common factor tensor which are faster than that of the naive PCA-based estimator. Our results are obtained under mild conditions which allow the idiosyncratic components to be weakly cross- and auto- correlated. We also provide a novel iterative procedure based on the eigenvalue-ratio principle to determine the factor numbers. Extensive numerical studies are conducted to investigate the empirical performance of the proposed projection estimators relative to the state-of-the-art ones.
Hierarchical State Abstraction Based on Structural Information Principles

State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations. Nevertheless, recent approaches focus on adequate representational capacities resulting in essential information loss, affecting their performances on challenging tasks. In this article, we propose a novel mathematical Structural Information principles-based State Abstraction framework, namely SISA, from the information-theoretic perspective. Specifically, an unsupervised, adaptive hierarchical state clustering method without requiring manual assistance is presented, and meanwhile, an optimal encoding tree is generated. On each non-root tree node, a new aggregation function and condition structural entropy are designed to achieve hierarchical state abstraction and compensate for sampling-induced essential information loss in state abstraction. Empirical evaluations on a visual gridworld domain and six continuous control benchmarks demonstrate that, compared with five SOTA state abstraction approaches, SISA significantly improves mean episode reward and sample efficiency up to 18.98 and 44.44%, respectively. Besides, we experimentally show that SISA is a general framework that can be flexibly integrated with different representation-learning objectives to improve their performances further.
Graph Neural Networks for Text Classification: A Survey

Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with complex structured text data and exploit global information. Many real text classification applications can be naturally cast into a graph, which captures words, documents, and corpus global features. In this survey, we bring the coverage of methods up to 2023, including corpus-level and document-level graph neural networks. We discuss each of these methods in detail, dealing with the graph construction mechanisms and the graph-based learning process. As well as the technological survey, we look at issues behind and future directions addressed in text classification using graph neural networks. We also cover datasets, evaluation metrics, and experiment design and present a summary of published performance on the publicly available benchmarks. Note that we present a comprehensive comparison between different techniques and identify the pros and cons of various evaluation metrics in this survey.
Complementarity between decoherence and information retrieval from the environment

We address the problem of fundamental limitations of information extraction from the environment in open quantum systems. We derive a model-independent, hybrid quantum-classical solution of open dynamics in the recoil-less limit, which includes environmental degrees of freedom. Specifying to the celebrated Caldeira-Leggett model of hot thermal environments, ubiquitous in everyday situations, we reveal the existence of a new lengthscale, called distinguishability length, different from the well-known thermal de Broglie wavelength that governs the decoherence. Interestingly, a new integral kernel, called Quantum Fisher Information kernel, appears in the analysis. It complements the well-known dissipation and noise kernels and satisfies disturbance-information gain type of relations, similar to the famous fluctuation-dissipation relation. Our results complement the existing treatments of the Caldeira-Legget model from a non-standard and highly non-trivial perspective of information dynamics in the environment. This leads to a full picture of how the open evolution looks like from both the system and the environment points of view, as well as sets limits on the precision of indirect observations.
Nematic twist-bend phase under external constraints

ABSTRACT The recently discovered twist-bend nematic phase, NTB, has short-pitched heliconical structure with doubly degenerate handedness. In contrast to the classic nematic, in the NTB phase the director is spontaneously distorted, resulting in unusual elastic properties. The response of the NTB phase to external stimuli, like chiral doping or applied fields might provide further information about its structure and can find utilisation in practical applications. Here, the NTB behaviour is theoretically investigated under chiral doping and strong electric fields. We show that the chiral doping removes the NTB degeneration and modifies the conical tilt angle, leaving the pitch unchanged. Thus, the NTB helical twisting power is very high and strongly non-linear. Under electric field, we consider separately the ferroelectric, flexoelectric and dielectric couplings. We show that the experiments reported so far disagree with the ferroelectric behaviour, indicating that the NTB phase is not spontaneously polarised. On the contrary, the observed polar effects fit well with the flexoelectric coupling, confirming the degenerated heliconical structure of the phase. Under very strong fields, we predict a second-order twist-bend nematic – nematic phase transition due to the dielectric torque on the director. Graphical Abstract
Optically-triggered deterministic spiking regimes in nanostructure resonant tunnelling diode-photodetectors

This work reports a nanostructure resonant tunnelling diode-photodetector (RTD-PD) device and demonstrates its operation as a controllable, optically-triggered excitable spike generator. The top contact layer of the device is designed with a nanopillar structure (500 nm in diameter) to restrain the injection current, yielding therefore lower energy operation for spike generation. We demonstrate experimentally the deterministic optical triggering of controllable and repeatable neuron-like spike patterns in the nanostructure RTD-PDs. Moreover, we show the device’s ability to deliver spiking responses when biased in either of the two regions adjacent to the negative differential conductance region, the so-called ‘peak’ and ‘valley’ points of the current–voltage (I–V) characteristic. This work also demonstrates experimentally key neuron-like dynamical features in the nanostructure RTD-PD, such as a well-defined threshold (in input optical intensity) for spike firing, as well as the presence of spike firing refractory time. The optoelectronic and chip-scale character of the proposed system together with the deterministic, repeatable and well controllable nature of the optically-elicited spiking responses render this nanostructure RTD-PD element as a highly promising solution for high-speed, energy-efficient optoelectronic artificial spiking neurons for novel light-enabled neuromorphic computing hardware.
UTSGAN: Unseen Transition Suss GAN for Transition-Aware Image-to-image Translation

In the field of Image-to-Image (I2I) translation, ensuring consistency between input images and their translated results is a key requirement for producing high-quality and desirable outputs. Previous I2I methods have relied on result consistency, which enforces consistency between the translated results and the ground truth output, to achieve this goal. However, result consistency is limited in its ability to handle complex and unseen attribute changes in translation tasks. To address this issue, we introduce a transition-aware approach to I2I translation, where the data translation mapping is explicitly parameterized with a transition variable, allowing for the modelling of unobserved translations triggered by unseen transitions. Furthermore, we propose the use of transition consistency, defined on the transition variable, to enable regularization of consistency on unobserved translations, which is omitted in previous works. Based on these insights, we present Unseen Transition Suss GAN (UTSGAN), a generative framework that constructs a manifold for the transition with a stochastic transition encoder and coherently regularizes and generalizes result consistency and transition consistency on both training and unobserved translations with tailor-designed constraints. Extensive experiments on four different I2I tasks performed on five different datasets demonstrate the efficacy of our proposed UTSGAN in performing consistent translations.
Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study

Background Large language models, such as ChatGPT, are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the internet. However, medical texts, such as clinical notes and diagnoses, require rigorous validation, and erroneous medical content generated by ChatGPT could potentially lead to disinformation that poses significant harm to health care and the general public. Objective This study is among the first on responsible artificial intelligence–generated content in medicine. We focus on analyzing the differences between medical texts written by human experts and those generated by ChatGPT and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT. Methods We first constructed a suite of data sets containing medical texts written by human experts and generated by ChatGPT. We analyzed the linguistic features of these 2 types of content and uncovered differences in vocabulary, parts-of-speech, dependency, sentiment, perplexity, and other aspects. Finally, we designed and implemented machine learning methods to detect medical text generated by ChatGPT. The data and code used in this paper are published on GitHub. Results Medical texts written by humans were more concrete, more diverse, and typically contained more useful information, while medical texts generated by ChatGPT paid more attention to fluency and logic and usually expressed general terminologies rather than effective information specific to the context of the problem. A bidirectional encoder representations from transformers–based model effectively detected medical texts generated by ChatGPT, and the F1 score exceeded 95%. Conclusions Although text generated by ChatGPT is grammatically perfect and human-like, the linguistic characteristics of generated medical texts were different from those written by human experts. Medical text generated by ChatGPT could be effectively detected by the proposed machine learning algorithms. This study provides a pathway toward trustworthy and accountable use of large language models in medicine.
Matching markets with farsighted couples

We adopt the notion of the farsighted stable set to determine which matchings are stable when agents are farsighted in matching markets with couples. We show that a singleton matching is a farsighted stable set if and only if the matching is stable. Thus, matchings that are stable with myopic agents remain stable when agents become farsighted. Examples of farsighted stable sets containing multiple non-stable matchings are provided for markets with and without stable matchings. For couples markets where the farsighted stable set does not exist, we propose the DEM farsighted stable set to predict the matchings that are stable when agents are farsighted.
D ELTA S CORE : Story Evaluation with Perturbations

Various evaluation metrics exist for natural language generation tasks, but they have limited utility for story generation since they generally do not correlate well with human judgments and are not designed to evaluate ﬁne-grained story aspects, such as ﬂuency and relatedness. In this paper, we propose D ELTA SCORE , an approach that utilizes perturbation to evaluate ﬁne-grained story aspects. Our core idea is based on the hypothesis that the better the story performs in a speciﬁc aspect (e.g., ﬂuency), the more it will be affected by a particular perturbation (e.g., introducing typos). To measure the impact, we calculate the likelihood difference between the pre- and post-perturbation stories using large pre-trained language models. We evaluate D ELTA S CORE against state-of-the-art model-based and traditional similarity-based metrics across two story domains, and investigate its correlation with human judgments on ﬁve ﬁne-grained story aspects: ﬂuency, coherence, relatedness, logicality, and interestingness. The ﬁndings of our study indicate that the D ELTA S CORE approach exhibits exceptional performance in evaluating intricate story aspects. An unex-pected discovery was made in our experiment, where a single perturbation method was found to effectively capture a majority of these aspects.
Large Powers asymptotics, Khinchin families and Lagrangian distributions

This paper delves on the versatility of the theory of Khinchin families for asymptotic estimation. We show that in combination with Local Central Limit theorems for lattice variables, Khinchin families furnish a convenient and unified framework to deal with asymptotic results of the coefficients of large powers of power series. We revisit in the present paper this classical theme from that point of view, obtaining clean new proofs and a number of new results. Asymptotic results for the coefficients of solutions of Lagrange's equation fall naturally into this combined framework. We provide a direct proof of an extension of the Otter and Meir-Moon asymptotic formula as well as asymptotic results for families of Lagrangian probability distributions.
The spherical interpolation method for closed-form passive source localization using range difference measurements

The problem of estimating the location of a radiating source from range difference measurements taken from a passive, stationary array is discussed. A new closed-form source location estimator, termed the Spherical Interpolation Estimator, is presented and analyzed. The location estimates produced by the Spherical Interpolation Estimator are approximate minimizers of a weighted equation error norm, and are shown to approach the maximum likelihood source location estimator.
A Uniform- and Variable-Resolution Stretched-Grid GCM Dynamical Core with Realistic Orography

The impact of introducing a realistic orographic forcing into a uniform- and variable-resolution stretched-grid GCM dynamical core is investigated by performing long-term and medium-range integrations. Comparisons are made between various stretched-grid simulations and a control that consists of a uniform grid integration at high resolution. These comparisons include those where the orography has and has not been filtered to eliminate small-scale noise. Results from the region of interest with highest resolution show that 1) the stretched-grid GCM provides an efficient downscaling over the area of interest, that is, it properly simulates not only largescale but also mesoscale features; and 2) the introduction of orography has a greater impact than the effect of stretching. Results presented here suggest that dynamical core integrations with both uniform and stretched grids should consider orographic forcing as an integral part of the model dynamics.
Exponential decay of the volume for Bernoulli percolation: a proof via stochastic comparison

Let us consider subcritical Bernoulli percolation on a connected, transitive, infinite and locally finite graph. In this paper, we propose a new (and short) proof of the exponential decay property for the volume of clusters. We neither rely on differential inequalities nor on the BK inequality. We rather use stochastic comparison techniques, which are inspired by (and simpler than) those proposed in [Van22] to study the diameter of clusters. We are also very inspired by the adaptation of these techniques in [Eas22].
Local Bounded Commuting Projection Operators for Discrete Gradgrad Complexes

This paper discusses the construction of local bounded commuting projections for discrete subcomplexes of the gradgrad complexes in two and three dimensions, which play an important role in the finite element theory of elasticity (2D) and general relativity (3D). The construction first extends the local bounded commuting projections to the discrete de Rham complexes to other discrete complexes. Moreover, the argument also provides a guidance in the design of new discrete gradgrad complexes.
Birational rigidity and alpha invariants of Fano varieties

We prove that for every $\epsilon>0$, there is a birationally super-rigid Fano variety $X$ such that $\frac{1}{2}\leqslant\alpha(X)\leqslant \frac{1}{2}+\epsilon$. Also we show that for every $\epsilon>0$, there is a Fano variety $X$ and a finite subgroup $G\subset\mathrm{Aut}(X)$ such that $X$ is $G$-birationally super-rigid, and $\alpha_G(X)<\epsilon$.
Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture

Medical image segmentation is crucial for the development of computer-aided diagnostic and therapeutic systems, but still faces numerous difficulties. In recent years, the commonly used encoder-decoder architecture based on CNNs has been applied effectively in medical image segmentation, but has limitations in terms of learning global context and spatial relationships. Some researchers have attempted to incorporate transformers into both the decoder and encoder components, with promising results, but this approach still requires further improvement due to its high computational complexity. This paper introduces Dilated-UNet, which combines a Dilated Transformer block with the U-Net architecture for accurate and fast medical image segmentation. Image patches are transformed into tokens and fed into the U-shaped encoder-decoder architecture, with skip-connections for local-global semantic feature learning. The encoder uses a hierarchical Dilated Transformer with a combination of Neighborhood Attention and Dilated Neighborhood Attention Transformer to extract local and sparse global attention. The results of our experiments show that Dilated-UNet outperforms other models on several challenging medical image segmentation datasets, such as ISIC and Synapse.
Learn What NOT to Learn: Towards Generative Safety in Chatbots

Conversational models that are generative and open-domain are particularly susceptible to generating unsafe content since they are trained on web-based social data. Prior approaches to mitigating this issue have drawbacks, such as disrupting the flow of conversation, limited generalization to unseen toxic input contexts, and sacrificing the quality of the dialogue for the sake of safety. In this paper, we present a novel framework, named"LOT"(Learn NOT to), that employs a contrastive loss to enhance generalization by learning from both positive and negative training signals. Our approach differs from the standard contrastive learning framework in that it automatically obtains positive and negative signals from the safe and unsafe language distributions that have been learned beforehand. The LOT framework utilizes divergence to steer the generations away from the unsafe subspace and towards the safe subspace while sustaining the flow of conversation. Our approach is memory and time-efficient during decoding and effectively reduces toxicity while preserving engagingness and fluency. Empirical results indicate that LOT reduces toxicity by up to four-fold while achieving four to six-fold higher rates of engagingness and fluency compared to baseline models. Our findings are further corroborated by human evaluation.
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies

Unrolled computation graphs are prevalent throughout machine learning but present challenges to automatic differentiation (AD) gradient estimation methods when their loss functions exhibit extreme local sensitivtiy, discontinuity, or blackbox characteristics. In such scenarios, online evolution strategies methods are a more capable alternative, while being more parallelizable than vanilla evolution strategies (ES) by interleaving partial unrolls and gradient updates. In this work, we propose a general class of unbiased online evolution strategies methods. We analytically and empirically characterize the variance of this class of gradient estimators and identify the one with the least variance, which we term Noise-Reuse Evolution Strategies (NRES). Experimentally, we show NRES results in faster convergence than existing AD and ES methods in terms of wall-clock time and number of unroll steps across a variety of applications, including learning dynamical systems, meta-training learned optimizers, and reinforcement learning.