Miracle-dz/UniFAR-FTUs huggingface dataset:
A quantum generative model for multi-dimensional time series using Hamiltonian learning

Synthetic data generation has proven to be a promising solution for addressing data availability issues in various domains. Even more challenging is the generation of synthetic time series data, where one has to preserve temporal dynamics, i.e. , the generated time series must respect the original relationships between variables across time. Recently proposed techniques such as generative adversarial networks (GANs) and quantum-GANs lack the ability to attend to the time series speciﬁc temporal correlations adequately. We propose using the inherent nature of quantum computers to simulate quantum dynamics as a technique to encode such features. We start by assuming that a given time series can be generated by a quantum process, after which we proceed to learn that quantum process using quantum machine learning. We then use the learned model to generate out-of-sample time series and show that it captures unique and complex features of the learned time series. We also study the class of time series that can be modeled using this technique. Finally, we experimentally demonstrate the proposed algorithm on an 11-qubit trapped-ion quantum machine.
ASQA: Factoid Questions Meet Long-Form Answers

Recent progress on open domain factoid question answering (QA) does not easily transfer to the task of long-form QA, where the goal is to answer questions that require in-depth explanations. The hurdles include a lack of high-quality data and the absence of a well-defined notion of an answer’s quality. In this work, we address these problems by releasing a novel dataset and a task that we call ASQA (Answer Summaries for Questions which are Ambiguous); and proposing a reliable metric for measuring performance on ASQA. Our task focuses on ambiguous factoid questions which have different correct answers depending on the interpretation. Answers to ambiguous questions should combine factual information from multiple sources into a coherent long-form summary that resolves the ambiguity. In contrast to existing long-form QA tasks (such as ELI5), ASQA admits a clear notion of correctness: a user faced with a good summary should be able to answer different interpretations of the original ambiguous question. Our analysis demonstrates an agreement between this metric and human judgments, and reveals a considerable gap between human performance and strong baselines.
Compact extra dimensions as the source of primordial black holes

This article discusses a model of primordial black hole (PBH) formation at the reheating stage. These small/massive black holes appear due to the specific properties of the compact extra dimensions. The latter gives rise to the low energy model, containing an effective scalar field potential capable of domain wall production. Formed during inflation, these walls are quite dense, meaning they collapse soon after inflation ends. Discussion of the model is framed by the scope of multidimensional f(R)-gravity. We study the possibility of the pure gravitational formation of primordial black holes (PBHs). Interpreting the scalar curvature of compact extra space R n as an effective scalar field in an Einstein framework and consider effective scalar-field theory that might potentially be capable of producing domain walls with a certain choice of parameters. Hence, we demonstrate that f(R)-gravity contains a mechanism for PBH formation. The study assumed that cosmological inflation is an external process, which satisfied all the cosmological constraints on our mechanism.
Emergence of Time from Unitary Equivalence

We discuss the concept of unitary equivalence $\hat{H}\sim\hat{U}^{\dagger}\hat{H}_{\mathrm{mod}}\hat{U}$ between the modular Hamiltonian $\hat{H}_{\mathrm{mod}}$ and the subsystem Hamiltonian $\hat{H}$ in the context of realizing the emergence of time through a unitary operator $\hat{U}$. This concept suggests a duality between the modular flow and time evolution. Additionally, requiring unitary equivalence implies a connection between the"Modular Chaos Bound"and the"Chaos Bound". Furthermore, we demonstrate this duality using quantum chaos diagnostic quantities in the thermofield double state of a fermionic system. Quantum chaos diagnostic quantities are mathematical measures that characterize chaotic behavior in quantum systems. By examining these quantities in the thermofield double state, we illustrate the duality between them and the modular Hamiltonian. We show a specific duality between correlators, the spectral form factor, and the Loschmidt echo with the modular Hamiltonian. The spectral form factor is a quantity that provides information about the energy spectrum of a quantum system, while the Loschmidt echo characterizes the sensitivity of a system's modular time evolution to perturbations. Finally, we demonstrate that a different entanglement spectrum does not impose the same constraint on the subsystem Hamiltonian. The entanglement spectrum is related to entanglement entropy and provides information about the eigenvalues of the reduced density matrix associated with a subsystem. We discuss complex concepts related to the interplay between quantum chaos, time emergence, and the relationship between modular and subsystem Hamiltonians. These ideas are part of ongoing research in quantum information theory and related fields.
Receding Neuron Importances for Structured Pruning

Structured pruning efficiently compresses networks by identifying and removing unimportant neurons. While this can be elegantly achieved by applying sparsity-inducing regularisation on BatchNorm parameters, an L1 penalty would shrink all scaling factors rather than just those of superfluous neurons. To tackle this issue, we introduce a simple BatchNorm variation with bounded scaling parameters, based on which we design a novel regularisation term that suppresses only neurons with low importance. Under our method, the weights of unnecessary neurons effectively recede, producing a polarised bimodal distribution of importances. We show that neural networks trained this way can be pruned to a larger extent and with less deterioration. We one-shot prune VGG and ResNet architectures at different ratios on CIFAR and ImagenNet datasets. In the case of VGG-style networks, our method significantly outperforms existing approaches particularly under a severe pruning regime.
Data-heterogeneity-aware Mixing for Decentralized Learning

Decentralized learning provides an effective framework to train machine learning models with data distributed over arbitrary communication graphs. However, most existing approaches towards decentralized learning disregard the interaction between data heterogeneity and graph topology. In this paper, we characterize the dependence of convergence on the relationship between the mixing weights of the graph and the data heterogeneity across nodes. We propose a metric that quantiﬁes the ability of a graph to mix the current gradients. We further prove that the metric controls the convergence rate, particularly in settings where the heterogeneity across nodes dominates the stochasticity between updates for a given node. Motivated by our analysis, we propose an approach that periodically and efﬁciently optimizes the metric using standard convex constrained optimization and sketching techniques. Through comprehensive experiments on standard computer vision and NLP benchmarks, we show that our approach leads to improvement in test performance for a wide range of tasks. We heterogeneity data. This to signiﬁcantly and to a higher accuracy than methods. Our evaluations show that the additional beneﬁt provided by the dynamic and data-adaptive design of the mixing matrix consistently outweighs other baselines on deep learning benchmarks, unlike other approaches based
A Unified Cascaded Encoder ASR Model for Dynamic Model Sizes

In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios. Moreover, the model can significantly reduce model size and power consumption without loss of quality. Namely, with the dynamic cascaded encoder model, we explore three techniques to maximally boost the performance of each model size: 1) Use separate decoders for each sub-model while sharing the encoders; 2) Use funnel-pooling to improve the encoder efficiency; 3) Balance the size of causal and non-causal encoders to improve quality and fit deployment constraints. Overall, the proposed large-medium model has 30% smaller size and reduces power consumption by 33%, compared to the baseline cascaded encoder model. The triple-size model that unifies the large, medium, and small models achieves 37% total size reduction with minimal quality loss, while substantially reducing the engineering efforts of having separate models.
Approximation of Lipschitz Functions using Deep Spline Neural Networks

Lipschitz-constrained neural networks have many applications in machine learning. Since designing and training expressive Lipschitz-constrained networks is very challenging, there is a need for improved methods and a better theoretical understanding. Unfortunately, it turns out that ReLU networks have provable disadvantages in this setting. Hence, we propose to use learnable spline activation functions with at least 3 linear regions instead. We prove that this choice is optimal among all component-wise $1$-Lipschitz activation functions in the sense that no other weight constrained architecture can approximate a larger class of functions. Additionally, this choice is at least as expressive as the recently introduced non component-wise Groupsort activation function for spectral-norm-constrained weights. Previously published numerical results support our theoretical findings.
Out-of-distribution Detection with Deep Nearest Neighbors

Out-of-distribution (OOD) detection is a critical task for deploying machine learning models in the open world. Distance-based methods have demonstrated promise, where testing samples are detected as OOD if they are relatively far away from in-distribution (ID) data. However, prior methods impose a strong distributional assumption of the underlying feature space, which may not always hold. In this paper, we explore the efficacy of non-parametric nearest-neighbor distance for OOD detection, which has been largely overlooked in the literature. Unlike prior works, our method does not impose any distributional assumption, hence providing stronger flexibility and generality. We demonstrate the effectiveness of nearest-neighbor-based OOD detection on several benchmarks and establish superior performance. Under the same model trained on ImageNet-1k, our method substantially reduces the false positive rate (FPR@TPR95) by 24.77% compared to a strong baseline SSD+, which uses a parametric approach Mahalanobis distance in detection. Code is available: https://github.com/deeplearning-wisc/knn-ood.
BEHM-GAN: Bandwidth Extension of Historical Music Using Generative Adversarial Networks

Audio bandwidth extension aims to expand the spectrum of bandlimited audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes a method for the bandwidth extension of historical music using generative adversarial networks (BEHM-GAN) as a practical solution to this problem. The proposed method works with the complex spectrogram representation of audio and, thanks to a dedicated regularization strategy, can effectively extend the bandwidth of out-of-distribution real historical recordings. The BEHM-GAN is designed to be applied as a second step after denoising the recording to suppress any additive disturbances, such as clicks and background noise. We train and evaluate the method using solo piano classical music. The proposed method outperforms the compared baselines in both objective and subjective experiments. The results of a formal blind listening test show that BEHM-GAN significantly increases the perceptual sound quality in early-20th-century gramophone recordings. For several items, there is a substantial improvement in the mean opinion score after enhancing historical recordings with the proposed bandwidth-extension algorithm. This study represents a relevant step toward data-driven music restoration in real-world scenarios.
Communication Efﬁcient Curvature Aided Primal-dual Algorithms for Distributed Optimization

This paper presents a family of primal-dual algorithms for distributed convex composite problems. We consider the setting of a network of agents that cooperatively minimize a global objective function composed of a sum of local functions plus a regularizer. Through use of intermediate consensus variables, we remove the need for inner-communication loops between agents when computing curvature guided updates. A general scheme is presented which uniﬁes the analysis for a plethora of computing choices, including gradient descent, Newton updates, and BFGS updates. Our analysis establishes sublinear convergence to the optimal solution under convex objective functions with Lipschitz continuous gradient, as well as linear convergence rate when the local functions are assumed to be strongly convex. Moreover, we explicitly characterize the acceleration due to curvature information. Last but not least, we present an asynchronous implementation for the proposed algorithms, which removes the need for a central clock, with linear convergence rate established in expectation under strongly convex objectives. We ascertain the effectiveness of the proposed methods with numerical experiments on benchmark datasets.
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding

In the age of large transformer language models, linguistic evaluation play an important role in diagnosing models’ abilities and limitations on natural language understanding. However, current evaluation methods show some significant shortcomings. In particular, they do not provide insight into how well a language model captures distinct linguistic skills essential for language understanding and reasoning. Thus they fail to effectively map out the aspects of language understanding that remain challenging to existing models, which makes it hard to discover potential limitations in models and datasets. In this paper, we introduce Curriculum as a new format of NLI benchmark for evaluation of broad-coverage linguistic phenomena. Curriculum contains a collection of datasets that covers 36 types of major linguistic phenomena and an evaluation procedure for diagnosing how well a language model captures reasoning skills for distinct types of linguistic phenomena. We show that this linguistic-phenomena-driven benchmark can serve as an effective tool for diagnosing model behavior and verifying model learning quality. In addition, our experiments provide insight into the limitation of existing benchmark datasets and state-of-the-art models that may encourage future research on re-designing datasets, model architectures, and learning objectives.
Modified Atmosphere Packaging Of Sapota

The present project work was undertaken to study the performance of the developed Modified Atmosphere Packaging Technology to increase the shelf life of sapota. The effect of various thicknesses of polyethylene bags, gas proportion and storage temperatures were observed and data were recorded at seven days interval for various physical, biochemical and organeleptic parameters. The study revealed that the shelf life of sapota was increased for 49 days with packed them in 25 µ polyethylene bag under 5% O2 + 10% CO2 mixture and stored at 6 oC temperature without much changes in physical, biochemical and organeleptic parameters. Sapota can be stored for 35 days for fruits packed in 25 µ polyethylene bag in all gas concentration and stored at 11 oC and fruits ripened properly during storage. The shelf life was increased for 56 days for fruits packed in 40 & 60 µ polyethylene bags in all gas concentration and stored at 6 oC but, fruits did not ripen properly. Thus, it is recommended from the study that sapota can be stored up to 49 days during transportation and export with specified storage conditions without much change in its physical, biochemical and organeleptic parameters.
Building extraction with vision transformer

As an important carrier of human productive activities, the extraction of buildings is not only essential for urban dynamic monitoring but also necessary for suburban construction inspection. Nowadays, accurate building extraction from remote sensing images remains a challenge due to the complex background and diverse appearances of buildings. The convolutional neural network (CNN) based building extraction methods, although increased the accuracy significantly, are criticized for their inability for modelling global dependencies. Thus, this paper applies the Vision Transformer for building extraction. However, the actual utilization of the Vision Transformer often comes with two limitations. First, the Vision Transformer requires more GPU memory and computational costs compared to CNNs. This limitation is further magnified when encountering large-sized inputs like fine-resolution remote sensing images. Second, spatial details are not sufficiently preserved during the feature extraction of the Vision Transformer, resulting in the inability for fine-grained building segmentation. To handle these issues, we propose a novel Vision Transformer (BuildFormer), with a dual-path structure. Specifically, we design a spatial-detailed context path to encode rich spatial details and a global context path to capture global dependencies. Besides, we develop a window-based linear multi-head self-attention to make the complexity of the multi-head self-attention linear with the window size, which strengthens the global context extraction by using large windows and greatly improves the potential of the Vision Transformer in processing large-sized remote sensing images. The proposed method yields state-of-the-art performance (75.74% IoU) on the Massachusetts building dataset. Code will be available at https://github.com/WangLibo1995/BuildFormer.
Do research universities boost regional economic development? -A case study of University of Science and Technology of China

ABSTRACT This study examines the interactions between the presence of research universities and regional economic development, drawing on the event of the forced relocation of the University of Science and Technology of China. We analyze the development in the treated regions compared with a set of control regions that are created using the synthetic control method and find that research universities may slow regional economic development. We then perform a series of robustness checks, and the main results carry through. By using more exogenous shocks and a more rational identification strategy, our study provides new evidence that research universities do not necessarily boost regional economic development.
Neuronal Cell Adhesion Molecules May Mediate Neuroinflammation in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by restrictive and repetitive behaviors, alongside deficits in social interaction and communication. The etiology of ASD is largely unknown but is strongly linked to genetic variants in neuronal cell adhesion molecules (CAMs), cell-surface proteins that have important roles in neurodevelopment. A combination of environmental and genetic factors are believed to contribute to ASD pathogenesis. Inflammation in ASD has been identified as one of these factors, demonstrated through the presence of proinflammatory cytokines, maternal immune activation, and activation of glial cells in ASD brains. Glial cells are the main source of cytokines within the brain and, therefore, their activity is vital in mediating inflammation in the central nervous system. However, it is unclear whether the aforementioned neuronal CAMs are involved in modulating neuroimmune signaling or glial behavior. This review aims to address the largely unexplored role that neuronal CAMs may play in mediating inflammatory cascades that underpin neuroinflammation in ASD, primarily focusing on the Notch, nuclear factor-κB (NF-κB), and mitogen-activated protein kinase (MAPK) cascades. We will also evaluate the available evidence on how neuronal CAMs may influence glial activity associated with inflammation. This is important when considering the impact of environmental factors and inflammatory responses on ASD development. In particular, neural CAM1 (NCAM1) can regulate NF-κB transcription in neurons, directly altering proinflammatory signaling. Additionally, NCAM1 and contactin-1 appear to mediate astrocyte and oligodendrocyte precursor proliferation which can alter the neuroimmune response. Importantly, although this review highlights the limited information available, there is evidence of a neuronal CAM regulatory role in inflammatory signaling. This warrants further investigation into the role other neuronal CAM family members may have in mediating inflammatory cascades and would advance our understanding of how neuroinflammation can contribute to ASD pathology.
Enhancing Learner Participation in Online Discussion Forums in Massive Open Online Courses: The Role of Mandatory Participation

Online discussion forums are an essential and standard setup in online courses to facilitate interactions among learners. However, learners’ inadequate participation in online discussion forums is a long-standing challenge, which necessitates instructor intervention and the design consideration of online learning platforms. This research proposes and studies the role of mandatory participation, i.e., learners’ participation in online course forums by instructors’ requirements, in fostering their voluntary participation and boosting their learning performance. This novel effect link between mandatory participation and voluntary participation has not been assessed in previous research. An empirical study is conducted using a large-scale dataset of 27,767 learners from a leading massive open online course (MOOC) platform in China. The findings indicate that besides its direct effect on learning performance, learners’ mandatory participation has a significant positive effect on their voluntary participation in online course forums, enhancing learning performance. Moreover, the effect of mandatory participation on voluntary participation varies across learner groups, being more prominent for early registrants than late registrants and part-time learners than full-time learners. This research contributes to the online learning literature by introducing mandatory participation as a viable approach to foster voluntary participation and boost learning performance through enhanced voluntary participation. It provides evidence on the effectiveness of the novel design feature of MOOC platforms that enables and facilitates the mandatory participation mechanism in online learning.