Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning

Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically. Continual Learning (CL) approaches seek to bridge this gap by facilitating the transfer of knowledge to both previous tasks (backward transfer) and future ones (forward transfer) during training. Recent research has shown that self-supervision can produce versatile models that can generalize well to diverse downstream tasks. However, contrastive self-supervised learning (CSSL), a popular self-supervision technique, has limited effectiveness in online CL (OCL). OCL only permits one iteration of the input dataset, and CSSL's low sample efficiency hinders its use on the input data-stream. In this work, we propose Continual Learning via Equivariant Regularization (CLER), an OCL approach that leverages equivariant tasks for self-supervision, avoiding CSSL's limitations. Our method represents the first attempt at combining equivariant knowledge with CL and can be easily integrated with existing OCL methods. Extensive ablations shed light on how equivariant pretext tasks affect the network's information flow and its impact on CL dynamics.
HSCNet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer

Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between raw pixels and 3D coordinates in the scene, and thus the matching is implicitly performed by the forward pass through the network. However, in a large and ambiguous environment, learning such a regression task directly can be difficult for a single network. In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image. The proposed method, which is an extension of HSCNet, allows us to train compact models which scale robustly to large environments. It sets a new state-of-the-art for single-image localization on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and the combined indoor scenes.
Hybrid Exciton Signatures in ARPES Spectra of van der Waals Materials

Van der Waals heterostructures show fascinating physics including trapped moire exciton states, anomalous moire exciton transport, generalized Wigner crystals, etc. Bilayers of transition metal dichalcogenides (TMDs) are characterized by long-lived spatially separated interlayer excitons. Provided a strong interlayer tunneling, hybrid exciton states consisting of interlayer and intralayer excitons can be formed. Here, electrons and/or holes are in a superposition of both layers. Although crucial for optics, dynamics, and transport, hybrid excitons are usually optically inactive and have therefore not been directly observed yet. Based on a microscopic and material-specific theory, we show that time- and angle-resolved photoemission spectroscopy (tr-ARPES) is the ideal technique to directly visualize these hybrid excitons. Concretely, we predict a characteristic double-peak ARPES signal arising from the hybridized hole in the MoS$_2$ homobilayer. The relative intensity is proportional to the quantum mixture of the two hybrid valence bands at the $\Gamma$ point. Due to the strong hybridization, the peak separation of more than 0.5 eV can be resolved in ARPES experiments. Our study provides a concrete recipe of how to directly visualize hybrid excitons and how to distinguish them from the usually observed regular excitonic signatures.
Evolution under Length Constraints for CNN Architecture design

In recent years, the CNN architectures designed by evolution algorithms have proven to be competitive with handcrafted architectures designed by experts. However, these algorithms need a lot of computational power, which is beyond the capabilities of most researchers and engineers. To overcome this problem, we propose an evolution architecture under length constraints. It consists of two algorithms: a search length strategy to find an optimal space and a search architecture strategy based on genetic algorithm to find the best individual in the optimal space. Our algorithms reduce drastically resource cost and also keep good performance. On the Cifar-10 dataset, our framework presents outstanding performance with an error rate of and only 4.6 GPU a day to converge to the optimal individual -22 GPU a day less than the lowest cost automatic evolutionary algorithm in the peer competition.
Peering into the central region of a nano-quasar: XMM-Newton and Chandra views of the CH Cyg Symbiotic System

We present the analysis of archival XMM-Newton and Chandra observations of CH Cyg, one of the most studied symbiotic stars (SySts). The combination of the high-resolution XMM-Newton RGS and Chandra HETG X-ray spectra allowed us to obtain reliable estimates of the chemical abundances and to corroborate the presence of multi-temperature X-ray-emitting gas. Spectral fitting of the medium-resolution XMM-Newton MOS (MOS1+MOS2) spectrum required the use of an additional component not seen in previous studies in order to fit the 2.0–4.0 keV energy range. Detailed spectral modelling of the XMM-Newton MOS data suggests the presence of a reflection component, very similar to that found in active galactic nuclei. The reflection component is very likely produced by an ionised disk (the accretion disk around the white dwarf) and naturally explains the presence of the fluorescent Fe emission line at 6.4 keV while also contributing to the soft and medium energy ranges. The variability of the global X-ray properties of CH Cyg are discussed as well as the variation of the three Fe lines around the 6–7 keV energy range. We conclude that reflection components are needed to model the hard X-ray emission and may be present in most β/δ-type SySt.
Analyzing Ecological Momentary Assessment Data with State-Space Models: Considerations and Recommendations

Ecological momentary assessment (EMA) data have a broad base of application in the study of time trends and relations. In EMA studies, there are a number of design considerations which influence the analysis of the data. One general modeling framework is particularly well-suited for these analyses: state-space modeling. Here, we present the state-space modeling framework with recommendations for the considerations that go into modeling EMA data. These recommendations can account for the issues that come up in EMA data analysis such as idiographic versus nomothetic modeling, missing data, and stationary versus non-stationary data. In addition, we suggest R packages in order to implement these recommendations in practice. Overall, well-designed EMA studies offer opportunities for researchers to handle the momentary minutiae in their assessment of psychological phenomena.
Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Due to the flexibility of prompting, foundation models have become the dominant force in the domains of natural language processing and image generation. With the recent introduction of the Segment Anything Model (SAM), the prompt-driven paradigm has entered the realm of image segmentation, bringing with a range of previously unexplored capabilities. However, it remains unclear whether it can be applicable to medical image segmentation due to the significant differences between natural images and medical images.In this work, we summarize recent efforts to extend the success of SAM to medical image segmentation tasks, including both empirical benchmarking and methodological adaptations, and discuss potential future directions for SAM in medical image segmentation. Although directly applying SAM to medical image segmentation cannot obtain satisfying performance on multi-modal and multi-target medical datasets, many insights are drawn to guide future research to develop foundation models for medical image analysis. To facilitate future research, we maintain an active repository that contains up-to-date paper list and open-source project summary at https://github.com/YichiZhang98/SAM4MIS.
Demystifying Softmax Gating Function in Gaussian Mixture of Experts

Understanding the parameter estimation of softmax gating Gaussian mixture of experts has remained a long-standing open problem in the literature. It is mainly due to three fundamental theoretical challenges associated with the softmax gating function: (i) the identifiability only up to the translation of parameters; (ii) the intrinsic interaction via partial differential equations between the softmax gating and the expert functions in the Gaussian density; (iii) the complex dependence between the numerator and denominator of the conditional density of softmax gating Gaussian mixture of experts. We resolve these challenges by proposing novel Voronoi loss functions among parameters and establishing the convergence rates of maximum likelihood estimator (MLE) for solving parameter estimation in these models. When the true number of experts is unknown and over-specified, our findings show a connection between the convergence rate of the MLE and a solvability problem of a system of polynomial equations.
Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization

Training a dialogue policy using deep reinforcement learning requires a lot of exploration of the environment. The amount of wasted invalid exploration makes their learning inefficient. In this paper, we find and define an important reason for the invalid exploration: dead-ends. When a conversation enters a dead-end state, regardless of the actions taken afterward, it will continue in a dead-end trajectory until the agent reaches a termination state or maximum turn. We propose a dead-end resurrection (DDR) algorithm that detects the initial dead-end state in a timely and efficient manner and provides a rescue action to guide and correct the exploration direction. To prevent dialogue policies from repeatedly making the same mistake, DDR also performs dialogue data augmentation by adding relevant experiences containing dead-end states. We first validate the dead-end detection reliability and then demonstrate the effectiveness and generality of the method by reporting experimental results on several dialogue datasets from different domains.
A technical note on bilinear layers for interpretability

The ability of neural networks to represent more features than neurons makes interpreting them challenging. This phenomenon, known as superposition, has spurred efforts to find architectures that are more interpretable than standard multilayer perceptrons (MLPs) with elementwise activation functions. In this note, I examine bilinear layers, which are a type of MLP layer that are mathematically much easier to analyze while simultaneously performing better than standard MLPs. Although they are nonlinear functions of their input, I demonstrate that bilinear layers can be expressed using only linear operations and third order tensors. We can integrate this expression for bilinear layers into a mathematical framework for transformer circuits, which was previously limited to attention-only transformers. These results suggest that bilinear layers are easier to analyze mathematically than current architectures and thus may lend themselves to deeper safety insights by allowing us to talk more formally about circuits in neural networks. Additionally, bilinear layers may offer an alternative path for mechanistic interpretability through understanding the mechanisms of feature construction instead of enumerating a (potentially exponentially) large number of features in large models.
Employing Hybrid Deep Neural Networks on Dari Speech

This paper is an extension of our previous conference paper. In recent years, there has been a growing interest among researchers in developing and improving speech recognition systems to facilitate and enhance human-computer interaction. Today, Automatic Speech Recognition (ASR) systems have become ubiquitous, used in everything from games to translation systems, robots, and more. However, much research is still needed on speech recognition systems for low-resource languages. This article focuses on the recognition of individual words in the Dari language using the Mel-frequency cepstral coefficients (MFCCs) feature extraction method and three different deep neural network models: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multilayer Perceptron (MLP), as well as two hybrid models combining CNN and RNN. We evaluate these models using an isolated Dari word corpus that we have created, consisting of 1000 utterances for 20 short Dari terms. Our study achieved an impressive average accuracy of 98.365%.
A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding

Webpages have been a rich, scalable resource for vision-language and language only tasks. Yet only pieces of webpages are kept in existing datasets: image-caption pairs, long text articles, or raw HTML, never all in one place. Webpage tasks have resultingly received little attention and structured image-text data left underused. To study multimodal webpage understanding, we introduce the Wikipedia Webpage suite (WikiWeb2M) containing 2M pages with all of the associated image, text, and structure data. We verify its utility on three generative tasks: page description generation, section summarization, and contextual image captioning. We design a novel attention mechanism Prefix Global, which selects the most relevant image and text content as global tokens to attend to the rest of the webpage for context. By using page structure to separate such tokens, it performs better than full attention with lower computational complexity. Extensive experiments show that the new data in WikiWeb2M improves task performance compared to prior work.
Flash: An Asynchronous Payment System with Good-Case Linear Communication Complexity

While the original purpose of blockchains was to realize a payment system, it has been shown that, in fact, such systems do not require consensus and can be implemented deterministically in asynchronous networks. State-of-the-art payment systems employ Reliable Broadcast to disseminate payments and prevent double spending, which entails O(n^2) communication complexity per payment even if Byzantine behavior is scarce or non-existent. Here we present Flash, the first payment system to achieve $O(n)$ communication complexity per payment in the good case and $O(n^2)$ complexity in the worst-case, matching the lower bound. This is made possible by sidestepping Reliable Broadcast and instead using the blocklace -- a DAG-like partially-ordered generalization of the blockchain -- for the tasks of recording transaction dependencies, block dissemination, and equivocation exclusion, which in turn prevents doublespending. Flash has two variants: for high congestion when multiple blocks that contain multiple payments are issued concurrently; and for low congestion when payments are infrequent.
LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER Using XLM-RoBERTa

Named Entity Recognition(NER) is a task ofrecognizing entities at a token level in a sen-tence. This paper focuses on solving NER tasksin a multilingual setting for complex named en-tities.Our team, LLM-RM participated in therecently organized SemEval 2023 task, Task 2:MultiCoNER II,Multilingual Complex NamedEntity Recognition. We approach the problemby leveraging cross-lingual representation pro-vided by fine-tuning XLM-Roberta base modelon datasets of all of the 12 languages provided - Bangla, Chinese, English, Farsi, French,German, Hindi, Italian, Portuguese, Spanish,Swedish and Ukrainian.
Adjoint and direct characteristic equations for two-dimensional compressible Euler flows

The method of characteristics is a classical method for gaining understanding in the solution of a partial differential equation. It has recently been applied to the adjoint equations of the 2D steady-state Euler equations and the first goal of this paper is to present a linear algebra analysis that greatly simplifies the discussion of the number of independent characteristic equations satisfied along a family of characteristic curves. This method may be applied for both the direct and the adjoint problem. Our second goal is to directly derive in conservative variables the characteristic equations of 2D compressible inviscid flows. Finally, the theoretical results are assessed for a nozzle flow with a classical scheme and its dual consistent discrete adjoint.
Application of Matrix Pencil Algorithm in Synchronous Machine Parameters Identification

The accuracy of synchronous machine parameters is extremely important to power system operation and control.Based on matrix pencil algorithm,a new method is proposed to identify synchronous machine parameters,with the basic principles and processes of identification provided.The method can structure two Hankel matrices from collected three-phase short-circuit current.The noise effect is restrained based on singular value decomposition(SVD) and low rank approximation.The computational costs are reduced.DC component and fundamental component are extracted as well using specific relationship between matrices,consequently accurate identification of synchronous machine parameters can be realized.The simulation results show that this method has higher precision,computational efficiency and stronger anti-noise capacity.
Experimental Validation of Safe MPC for Autonomous Driving in Uncertain Environments

The full deployment of autonomous driving systems on a worldwide scale requires that the self-driving vehicle can be operated in a provably safe manner, i.e., the vehicle must be able to avoid collisions in any possible traffic situation. In this article, we propose a framework based on model predictive control (MPC) that endows the self-driving vehicle with the necessary safety guarantees. In particular, our framework ensures constraint satisfaction at all times while tracking the reference trajectory as close as obstacles allow, resulting in a safe and comfortable driving behavior. To discuss the performance and real-time capability of our framework, we provide first an illustrative simulation example, and then, we demonstrate the effectiveness of our framework in experiments with a real test vehicle.
Can Large Language Models Transform Computational Social Science?

Large Language Models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the Computational Social Science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompting best practices and an extensive evaluation pipeline to measure the zero-shot performance of 13 language models on 25 representative English CSS benchmarks. On taxonomic labeling tasks (classification), LLMs fail to outperform the best fine-tuned models but still achieve fair levels of agreement with humans. On free-form coding tasks (generation), LLMs produce explanations that often exceed the quality of crowdworkers' gold references.We conclude that the performance of today’s LLMs can augment the CSS research pipeline in two ways: (1) serving as zeroshot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e.g., explaining the underlying attributes of a text). In summary, LLMs are posed to meaningfully participate in social science analysis in partnership with humans.
Fermionization of a few-body Bose system immersed into a Bose-Einstein condensate

We study the recently introduced self-pinning transition [Phys. Rev. Lett. 128, 053401 (2022)] in a quasi-one-dimensional two-component quantum gas in the case where the component immersed into the Bose-Einstein condensate has a finite intraspecies interaction strength. As a result of the matter-wave backaction, the fermionization in the limit of infinite intraspecies repulsion occurs via a first-order phase transition to the self-pinned state, which is in contrast to the asymptotic behavior in static trapping potentials. The system also exhibits an additional superfluid state for the immersed component if the interspecies interaction is able to overcome the intraspecies repulsion. We approximate the superfluid state in an analytical model and derive an expression for the phase transition line that coincides with well-known phase separation criteria in binary Bose systems. The full phase diagram of the system is mapped out numerically for the case of two and three atoms in the immersed component.
Intentional and serendipitous diffusion of ideas: Evidence from academic conferences

This paper investigates the effects of seeing ideas presented in-person when they are easily accessible online. Presentations may increase the diffusion of ideas intentionally (when one attends the presentation of an idea of interest) and serendipitously (when one sees other ideas presented in the same session). We measure these effects in the context of 25 computer science conferences using data from the scheduling application Confer, which lets users browse papers, Like those of interest, and receive schedules of their presentations. We address endogeneity concerns in presentation attendance by exploiting scheduling conflicts: when a user Likes multiple papers that are presented at the same time, she cannot see them both, potentially affecting their diffusion. Estimates show that being able to see presentations increases citing of Liked papers within two years by 1.5 percentage points (62.5% boost over the baseline citation rate). Attention to Liked papers also spills over to non-Liked papers in the same session, increasing their citing by 0.5 percentage points (125% boost), and this serendipitous diffusion represents 30.5% of the total effect. Both diffusion types were concentrated among papers semantically close to an attendee's prior work, suggesting that there are inefficiencies in finding related research that conferences help overcome. Overall, even when ideas are easily accessible online, in-person presentations substantially increase diffusion, much of it serendipitous.
Finding Outliers in Gaussian Model-Based Clustering

Unsupervised classification, or clustering, is a problem often plagued by outliers, yet there is a paucity of work on handling outliers in unsupervised classification. Outlier algorithms tend to fall into two broad categories: outlier inclusion methods and trimming methods, which often require pre-specification of the number of points to remove. The fact that sample Mahalanobis distance is beta-distributed is used to derive an approximate distribution for the log-likelihoods of subset finite Gaussian mixture models. An algorithm is proposed that removes the least likely points, which are deemed outliers, until the log-likelihoods adhere to the reference distribution. This results in a trimming method which inherently estimates the number of outliers present.