Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Synoptic Wind on Coastal Circulation Dynamics

Particularly challenging classes of heterogeneous surfaces are ones where strong secondary circulations are generated, potentially dominating the flow dynamics. In this study, we focus on land-sea breeze circulations (LSBs) resulting from surface thermal contrasts in the presence of increasing synoptic pressure forcing. The relative importance and orientation of the thermal and synoptic forcings are measured through two dimensionless parameters: a bulk Richardson number (Mg is the geostrophic wind magnitude and Wstar a convective buoyant velocity scale), and the angle alpha between the shore and geostrophic wind. Large eddy simulations reveal the emergence of various regimes where the dynamics are shown to be asymmetric with respect to alpha. Along-shore cases result in deep LSBs similar to the quiescent scenario (zero synoptic background), irrespective of the strength of Mg. Across-shore simulations exhibit a circulation cell that decreases in height with increasing synoptic forcing. However, at the highest synoptic winds simulated, the circulation cell is advected away with sea-to-land winds, while a shallow circulation persists for land-to-sea cases. Scaling analysis that relates the internal parameters Qshore (net shore volumetric flux) and qshore (net shore advected heat flux) to the external input parameters Mg and Wstar results in a succinct model of the shore fluxes that also helps explain the physical implications of the identified LSBs. Finally, the vertical profiles of the shore-normal velocity and shore-advected heat flux are used, with the aid of k-means clustering, to independently classify the LSBs in the (Ri, alpha) regime space diagram (canonical LSB, sea-driven LSB, land-driven LSB, and advected LSB), corroborating our visual categorization.
Revealing the Impact of SIC in NOMA-ISAC

The impact of successive interference cancellation (SIC) in non-orthogonal multiple access integrated sensing and communications (NOMA-ISAC) is analyzed. A two-stage SIC-based framework is proposed to deal with the inter-communication user and inter-functionality interferences. The performance of sensing and communications (S&C) is analyzed for two SIC orders, i.e., the communications-centric SIC and the sensing-centric SIC. For each design, diversity orders, high signal-to-noise ratio (SNR) slopes, and high-SNR power offsets of the sensing rate (SR) and communication rate (CR) are derived as insights. Analytical results indicate that i) the main influence of SIC order on the SR and CR lies in the high-SNR power offsets; ii) ISAC provides more degrees of freedom than frequency-division S&C (FDSAC). Numerical results show that the SR-CR region of ISAC entirely covers that of FDSAC.
Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of Sentences

Pre-trained language models have led to a new state-of-the-art in many NLP tasks. However, for topic modeling, statistical generative models such as LDA are still prevalent, which do not easily allow incorporating contextual word vectors. They might yield topics that do not align well with human judgment. In this work, we propose a novel topic modeling and inference algorithm. We suggest a bag of sentences (BoS) approach using sentences as the unit of analysis. We leverage pre-trained sentence embeddings by combining generative process models and clustering. We derive a fast inference algorithm based on expectation maximization, hard assignments, and an annealing process. The evaluation shows that our method yields state-of-the art results with relatively little computational demands. Our method is also more flexible compared to prior works leveraging word embeddings, since it provides the possibility to customize topic-document distributions using priors. Code and data is at \url{https://github.com/JohnTailor/BertSenClu}.
Entity-Aware Dual Co-Attention Network for Fake News Detection

Fake news and misinformation spread rapidly on the Internet. How to identify it and how to interpret the identification results have become important issues. In this paper, we propose a Dual Co-Attention Network (Dual-CAN) for fake news detection, which takes news content, social media replies, and external knowledge into consideration. Our experimental results support that the proposed Dual-CAN outperforms current representative models in two benchmark datasets. We further make in-depth discussions by comparing how models work in both datasets with empirical analysis of attention weights.
Boosting Zero-shot Classification with Synthetic Data Diversity via Stable Diffusion

Recent research has shown it is possible to perform zero-shot classiﬁcation tasks by training a classiﬁer with synthetic data generated by a diffusion model. However, the performance of this approach is still inferior to that of recent vision-language models. It has been suggested that the reason for this is a domain gap between the synthetic and real data. In our work, we show that this domain gap is not the main issue, and that diversity in the synthetic dataset is more important. We propose a bag of tricks to improve diversity and are able to achieve performance on par with one of the vision-language models, CLIP. More importantly, this insight allows us to endow zero-shot classiﬁcation capabilities on any classiﬁcation model.
Phase Transitions in the Detection of Correlated Databases

We study the problem of detecting the correlation between two Gaussian databases $\mathsf{X}\in\mathbb{R}^{n\times d}$ and $\mathsf{Y}^{n\times d}$, each composed of $n$ users with $d$ features. This problem is relevant in the analysis of social media, computational biology, etc. We formulate this as a hypothesis testing problem: under the null hypothesis, these two databases are statistically independent. Under the alternative, however, there exists an unknown permutation $\sigma$ over the set of $n$ users (or, row permutation), such that $\mathsf{X}$ is $\rho$-correlated with $\mathsf{Y}^\sigma$, a permuted version of $\mathsf{Y}$. We determine sharp thresholds at which optimal testing exhibits a phase transition, depending on the asymptotic regime of $n$ and $d$. Specifically, we prove that if $\rho^2d\to0$, as $d\to\infty$, then weak detection (performing slightly better than random guessing) is statistically impossible, irrespectively of the value of $n$. This compliments the performance of a simple test that thresholds the sum all entries of $\mathsf{X}^T\mathsf{Y}$. Furthermore, when $d$ is fixed, we prove that strong detection (vanishing error probability) is impossible for any $\rho<\rho^\star$, where $\rho^\star$ is an explicit function of $d$, while weak detection is again impossible as long as $\rho^2d\to0$. These results close significant gaps in current recent related studies.
State-wise Safe Reinforcement Learning: A Survey

Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges. A major concern is safety, in another word, constraint satisfaction. State-wise constraints are one of the most common constraints in real-world applications and one of the most challenging constraints in Safe RL. Enforcing state-wise constraints is necessary and essential to many challenging tasks such as autonomous driving, robot manipulation. This paper provides a comprehensive review of existing approaches that address state-wise constraints in RL. Under the framework of State-wise Constrained Markov Decision Process (SCMDP), we will discuss the connections, differences, and trade-offs of existing approaches in terms of (i) safety guarantee and scalability, (ii) safety and reward performance, and (iii) safety after convergence and during training. We also summarize limitations of current methods and discuss potential future directions.
Nonlinear causal discovery with confounders*

This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a deconfounding adjustment to remove the confounding effects and a sequential procedure to estimate the causal order of variables. We implement DeFuSE via feedforward neural networks for scalable computation. Moreover, we establish the consistency of DeFuSE under an assumption called the strong causal minimality. In simulations, DeFuSE compares favorably against state-of-the-art competitors that ignore confounding or nonlinearity. Finally, we demonstrate the utility and effectiveness of the proposed approach with an application to gene regulatory network analysis. The Python implementation is available at https://github.com/chunlinli/defuse.
SimCon Loss with Multiple Views for Text Supervised Semantic Segmentation

Learning to segment images purely by relying on the image-text alignment from web data can lead to sub-optimal performance due to noise in the data. The noise comes from the samples where the associated text does not correlate with the image's visual content. Instead of purely relying on the alignment from the noisy data, this paper proposes a novel loss function termed SimCon, which accounts for intra-modal similarities to determine the appropriate set of positive samples to align. Further, using multiple views of the image (created synthetically) for training and combining the SimCon loss with it makes the training more robust. This version of the loss is termed MV-SimCon. The empirical results demonstrate that using the proposed loss function leads to consistent improvements on zero-shot, text supervised semantic segmentation and outperforms state-of-the-art by $+3.0\%$, $+3.3\%$ and $+6.9\%$ on PASCAL VOC, PASCAL Context and MSCOCO, respectively. With test time augmentations, we set a new record by improving these results further to $58.7\%$, $26.6\%$, and $33.3\%$ on PASCAL VOC, PASCAL Context, and MSCOCO, respectively. In addition, using the proposed loss function leads to robust training and faster convergence.
Classifying log del Pezzo surfaces with torus action

We consider log del Pezzo surfaces coming with a non-trivial torus action. Such a surface is 1/k-log canonical if it allows a resolution of singularities with discrepanies all greater or equal to 1/k-1. We provide a concrete classification algorithm for fixed k and give explicit results for k=1, 2 and 3. This comprises in particular the cases of Gorenstein index 1, 2 and 3.
Bath-limited dynamics of nuclear spins in solid-state spin platforms

Nuclear spins in the proximity of electronic spin defects in solids are promising platforms for quantum information processing due to their ability to preserve quantum states for a remarkably long time. Here we report a comprehensive study of the nuclear decoherence processes in the vicinity of the nitrogen-vacancy (NV) center in diamond. We simulate from first principles the change in the dynamics of nuclear spins as a function of distance and state of the NV center and validate our results with experimental data. Our simulations reveal nontrivial oscillations in the Hahn echo signal, pointing to a new sensing modality of dynamical-decoupling spectroscopy, and show how hybridization of the electronic states suppresses the coherence time of strongly coupled nuclear spins. The computational framework developed in our work is general and can be broadly applied to predict the dynamical properties of nuclear spins.
Deep Class-Incremental Learning: A Survey

Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually. For example, a robot needs to understand new instructions, and an opinion monitoring system should analyze emerging topics every day. Class-Incremental Learning (CIL) enables the learner to incorporate the knowledge of new classes incrementally and build a universal classifier among all seen classes. Correspondingly, when directly training the model with new class instances, a fatal problem occurs -- the model tends to catastrophically forget the characteristics of former ones, and its performance drastically degrades. There have been numerous efforts to tackle catastrophic forgetting in the machine learning community. In this paper, we survey comprehensively recent advances in deep class-incremental learning and summarize these methods from three aspects, i.e., data-centric, model-centric, and algorithm-centric. We also provide a rigorous and unified evaluation of 16 methods in benchmark image classification tasks to find out the characteristics of different algorithms empirically. Furthermore, we notice that the current comparison protocol ignores the influence of memory budget in model storage, which may result in unfair comparison and biased results. Hence, we advocate fair comparison by aligning the memory budget in evaluation, as well as several memory-agnostic performance measures. The source code to reproduce these evaluations is available at https://github.com/zhoudw-zdw/CIL_Survey/
Argon-Milling-Induced Decoherence Mechanisms in Superconducting Quantum Circuits

The fabrication of superconducting circuits requires multiple deposition, etch and cleaning steps, each possibly introducing material property changes and microscopic defects. In this work, we specifically investigate the process of argon milling, a potentially coherence limiting step, using niobium and aluminum superconducting resonators as a proxy for surface-limited behavior of qubits. We find that niobium microwave resonators exhibit an order of magnitude decrease in quality-factors after surface argon milling, while aluminum resonators are resilient to the same process. Extensive analysis of the niobium surface shows no change in the suboxide composition due to argon milling, while two-tone spectroscopy measurements reveal an increase in two-level system electrical dipole moments, indicating a structurally altered niobium oxide hosting larger two-level system defects. However, a short dry etch can fully recover the argon milling induced losses on niobium, offering a potential route towards state-of-the-art overlap Josephson junction qubits with niobium circuitry.
Efficiently Upgrading Multilingual Machine Translation Models to Support More Languages

With multilingual machine translation (MMT) models continuing to grow in size and number of supported languages, it is natural to reuse and upgrade existing models to save computation as data becomes available in more languages. However, adding new languages requires updating the vocabulary, which complicates the reuse of embeddings. The question of how to reuse existing models while also making architectural changes to provide capacity for both old and new languages has also not been closely studied. In this work, we introduce three techniques that help speed up the effective learning of new languages and alleviate catastrophic forgetting despite vocabulary and architecture mismatches. Our results show that by (1) carefully initializing the network, (2) applying learning rate scaling, and (3) performing data up-sampling, it is possible to exceed the performance of a same-sized baseline model with 30{% computation and recover the performance of a larger model trained from scratch with over 50{% reduction in computation. Furthermore, our analysis reveals that the introduced techniques help learn new directions more effectively and alleviate catastrophic forgetting at the same time. We hope our work will guide research into more efficient approaches to growing languages for these MMT models and ultimately maximize the reuse of existing models.
The Evolution of Protostellar Outflow Cavities, Kinematics, and Angular Distribution of Momentum and Energy in Orion A: Evidence for Dynamical Cores

We present Atacama Large Millimeter/submillimeter Array observations of the ∼10,000 au environment surrounding 21 protostars in the Orion A molecular cloud tracing outflows. Our sample is composed of Class 0 to flat-spectrum protostars, spanning the full ∼1 Myr lifetime. We derive the angular distribution of outflow momentum and energy profiles and obtain the first two-dimensional instantaneous mass, momentum, and energy ejection rate maps using our new approach: the pixel flux-tracing technique. Our results indicate that by the end of the protostellar phase, outflows will remove ∼2–4 M ⊙ from the surrounding ∼1 M ⊙ low-mass core. These high values indicate that outflows remove a significant amount of gas from their parent cores and continuous core accretion from larger scales is needed to replenish core material for star formation. This poses serious challenges to the concept of cores as well-defined mass reservoirs, and hence to the simplified core-to-star conversion prescriptions. Furthermore, we show that cavity opening angles, and momentum and energy distributions all increase with protostar evolutionary stage. This is clear evidence that even garden-variety protostellar outflows: (a) effectively inject energy and momentum into their environments on 10,000 au scales, and (b) significantly disrupt their natal cores, ejecting a large fraction of the mass that would have otherwise fed the nascent star. Our results support the conclusion that protostellar outflows have a direct impact on how stars get their mass, and that the natal sites of individual low-mass star formation are far more dynamic than commonly accepted theoretical paradigms.
DITTO: Offline Imitation Learning with World Models

We propose DITTO, an offline imitation learning algorithm which uses world models and on-policy reinforcement learning to addresses the problem of covariate shift, without access to an oracle or any additional online interactions. We discuss how world models enable offline, on-policy imitation learning, and propose a simple intrinsic reward defined in the world model latent space that induces imitation learning by reinforcement learning. Theoretically, we show that our formulation induces a divergence bound between expert and learner, in turn bounding the difference in reward. We test our method on difficult Atari environments from pixels alone, and achieve state-of-the-art performance in the offline setting.
Design of an Energy-Aware Cartesian Impedance Controller for Collaborative Disassembly

Human-robot collaborative disassembly is an emerging trend in the sustainable recycling process of electronic and mechanical products. It requires the use of advanced technologies to assist workers in repetitive physical tasks and deal with creaky and potentially damaged components. Nevertheless, when disassembling worn-out or damaged components, unexpected robot behaviors may emerge, so harmless and symbiotic physical interaction with humans and the environment becomes paramount. This work addresses this challenge at the control level by ensuring safe and passive behaviors in unplanned interactions and contact losses. The proposed algorithm capitalizes on an energy-aware Cartesian impedance controller, which features energy scaling and damping injection, and an augmented energy tank, which limits the power flow from the controller to the robot. The controller is evaluated in a real-world flawed unscrewing task with a Franka Emika Panda and is compared to a standard impedance controller and a hybrid force-impedance controller. The results demonstrate the high potential of the algorithm in human-robot collaborative disassembly tasks.
Moving Averages

We consider the convergence of moving averages in the general setting of ergodic theory or stationary ergodic processes. We characterize when there is universal convergence of moving averages based on complete convergence to zero of the standard ergodic averages. Using a theorem of Hsu-Robbins (1947) for independent, identically distributed processes, we prove for any bounded measurable function $f$ on a standard probability space $(X,\mathcal{B},\mu)$, there exists a Bernoulli shift $T$, such that all moving averages $M(v_n, L_n)^T f = \frac{1}{L_n} \sum_{i=v_n+1}^{v_n+L_n} f \circ T^i$ with $L_n\geq n$ converge a.e. to $\int_X f d\mu$. We refresh the reader about the cone condition established by Bellow, Jones, Rosenblatt (1990) which guarantees convergence of certain moving averages for all $f \in L^1(\mu)$ and ergodic measure preserving maps $T$. We show given $f \in L^1(\mu)$ and ergodic measure preserving $T$, there exists a moving average $M(v_n,L_n)^T f$ with $L_n$ strictly increasing such that $(v_n,L_n)$ does not satisfy the cone condition, but pointwise convergence holds a.e. We show for any non-zero $f\in L^1(\mu)$, there is a generic class of ergodic maps $T$ such that each map has an associated moving average $M(v_n, L_n)^T f$ which does not converge pointwise. It is known if $f\in L^2(\mu)$ is mean-zero, then there exist solutions $T$ and $g\in L^1(\mu)$ to the coboundary equation: $f = g - g\circ T$. This implies $f$ and $T$ produce universal moving averages. We show this does not generalize to $L^p(\mu)$ for $p<2$ by explicitly defining functions $f\in \cap_{p<2}L^p(\mu)$ such that for each ergodic measure preserving $T$, there exists a moving average $M(v_n, L_n)^T f$ with $L_n\geq n$ such that these moving averages do not converge pointwise. Several of the results are generalized to the case of moving averages with polynomial growth.
Traffic Shaping and Hysteresis Mitigation Using Deep Reinforcement Learning in a Connected Driving Environment

A multi-agent deep reinforcement learning-based framework for traffic shaping. The proposed framework offers a key advantage over existing congestion management strategies which is the ability to mitigate hysteresis phenomena. Unlike existing congestion management strategies that focus on breakdown prevention, the proposed framework is extremely effective after breakdown formation. The proposed framework assumes partial connectivity between the automated vehicles which share information. The framework requires a basic level of autonomy defined by one-dimensional longitudinal control. This framework is primarily built using a centralized training, centralized execution multi-agent deep reinforcement learning approach, where longitudinal control is defined by signals of acceleration or deceleration commands which are then executed by all agents uniformly. The model undertaken for training and testing of the framework is based on the well-known Double Deep Q-Learning algorithm which takes the average state of flow within the traffic stream as the model input and outputs actions in the form of acceleration or deceleration values. We demonstrate the ability of the model to shape the state of traffic, mitigate the negative effects of hysteresis, and even improve traffic flow beyond its original level. This paper also identifies the minimum percentage of CAVs required to successfully shape the traffic under an assumption of uniformly distributed CAVs within the loop system. The framework illustrated in this work doesnt just show the theoretical applicability of reinforcement learning to tackle such challenges but also proposes a realistic solution that only requires partial connectivity and continuous monitoring of the average speed of the system, which can be achieved using readily available sensors that measure the speeds of vehicles in reasonable proximity to the CAVs.