Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Attack and Defense in Deep Ranking

Deep Neural Network classifiers are vulnerable to adversarial attacks, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can raise or lower the rank of chosen candidates by adversarial perturbations. Specifically, the expected ranking order is first represented as a set of inequalities. Then a triplet-like objective function is designed to obtain the optimal perturbation. Conversely, an anti-collapse triplet defense is proposed to improve the ranking model robustness against all proposed attacks, where the model learns to prevent the adversarial attack from pulling the positive and negative samples close to each other. To comprehensively measure the empirical adversarial robustness of a ranking model with our defense, we propose an empirical robustness score, which involves a set of representative attacks against ranking models. Our adversarial ranking attacks and defenses are evaluated on MNIST, Fashion-MNIST, CUB200-2011, CARS196, and Stanford Online Products datasets. Experimental results demonstrate that our attacks can effectively compromise a typical deep ranking system. Nevertheless, our defense can significantly improve the ranking system's robustness and simultaneously mitigate a wide range of attacks.
Spherical adjunctions of stable $\infty$-categories and the relative S-construction

We develop the theory of semi-orthogonal decompositions and spherical functors in the framework of stable 8-categories. Building on this, we study the relative Waldhausen S-construction S‚pF q of a spherical functor F and equip it with a natural paracyclic structure (“rotational symmetry”). This fulfills a part of the general program to provide a rigorous account of perverse schobers which are (thus far conjectural) categorifications of perverse sheaves. Namely, in terms of our previous identification of perverse sheaves on Riemann surfaces with Milnor sheaves, the relative S-construction with its paracyclic symmetry amounts to a categorification of the stalks of a Milnor sheaf at a singularity of the corresponding perverse sheaf. The action of the paracyclic rotation is a categorical analog of the monodromy on the vanishing cycles of a perverse sheaf. Having this local categorification in mind, we may view the S-construction of a spherical functor as defining a schober locally at a singularity. Each component SnpF q can be interpreted as a partially wrapped Fukaya category of the disk with coefficients in the schober and with n ` 1 stops at the boundary.
Recurring Planetary Debris Transits and Circumstellar Gas around White Dwarf ZTF J0328–1219

We present follow-up photometry and spectroscopy of ZTF J0328−1219, strengthening its status as a white dwarf exhibiting transiting planetary debris. Using TESS and Zwicky Transient Facility photometry, along with follow-up high-speed photometry from various observatories, we find evidence for two significant periods of variability at 9.937 and 11.2 hr. We interpret these as most likely the orbital periods of different debris clumps. Changes in the detailed dip structures within the light curves are observed on nightly, weekly, and monthly timescales, reminiscent of the dynamic behavior observed in the first white dwarf discovered to harbor a disintegrating asteroid, WD 1145+017. We fit previously published spectroscopy along with broadband photometry to obtain new atmospheric parameters for the white dwarf, with M ⋆ = 0.731 ± 0.023 M ⊙, T eff = 7630 ± 140 K, and [Ca/He] = − 9.55 ± 0.12. With new high-resolution spectroscopy, we detect prominent and narrow Na D absorption features likely of circumstellar origin, with velocities 21.4 ± 1.0 km s−1 blueshifted relative to atmospheric lines. We attribute the periodically modulated photometric signal to dusty effluents from small orbiting bodies such as asteroids or comets, but we are unable to identify the most likely material that is being sublimated, or otherwise ejected, as the environmental temperatures range from roughly 400 to 700 K.
A continued learning approach for model‐informed precision dosing: Updating models in clinical practice

Model‐informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug‐disease‐patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real‐world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, because only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil‐guided dosing of paclitaxel. The present study constitutes an important step toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.
Effect of Adaptive and Fixed Shared Steering Control on Distracted Driver Behavior

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the physiological status of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane changes. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.
Asymmetric Loss Functions for Learning with Noisy Labels

Robust loss functions are essential for training deep neural networks with better generalization power in the presence of noisy labels. Symmetric loss functions are confirmed to be robust to label noise. However, the symmetric condition is overly restrictive. In this work, we propose a new class of loss functions, namely \textit{asymmetric loss functions}, which are robust to learning with noisy labels for various types of noise. We investigate general theoretical properties of asymmetric loss functions, including classification calibration, excess risk bound, and noise tolerance. Meanwhile, we introduce the asymmetry ratio to measure the asymmetry of a loss function. The empirical results show that a higher ratio would provide better noise tolerance. Moreover, we modify several commonly-used loss functions and establish the necessary and sufficient conditions for them to be asymmetric. Experimental results on benchmark datasets demonstrate that asymmetric loss functions can outperform state-of-the-art methods. The code is available at \href{https://github.com/hitcszx/ALFs}{https://github.com/hitcszx/ALFs}
ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias

Transformers have shown great potential in various computer vision tasks owing to their strong capability in modeling long-range dependency using the self-attention mechanism. Nevertheless, vision transformers treat an image as 1D sequence of visual tokens, lacking an intrinsic inductive bias (IB) in modeling local visual structures and dealing with scale variance. Alternatively, they require large-scale training data and longer training schedules to learn the IB implicitly. In this paper, we propose a novel Vision Transformer Advanced by Exploring intrinsic IB from convolutions, ie, ViTAE. Technically, ViTAE has several spatial pyramid reduction modules to downsample and embed the input image into tokens with rich multi-scale context by using multiple convolutions with different dilation rates. In this way, it acquires an intrinsic scale invariance IB and is able to learn robust feature representation for objects at various scales. Moreover, in each transformer layer, ViTAE has a convolution block in parallel to the multi-head self-attention module, whose features are fused and fed into the feed-forward network. Consequently, it has the intrinsic locality IB and is able to learn local features and global dependencies collaboratively. Experiments on ImageNet as well as downstream tasks prove the superiority of ViTAE over the baseline transformer and concurrent works. Source code and pretrained models will be available at GitHub.
Let’s be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction

In implicit discourse relation classification, we want to predict the relation between adjacent sentences in the absence of any overt discourse connectives. This is challenging even for humans, leading to shortage of annotated data, a fact that makes the task even more difficult for supervised machine learning approaches. In the current study, we perform implicit discourse relation classification without relying on any labeled implicit relation. We sidestep the lack of data through explicitation of implicit relations to reduce the task to two sub-problems: language modeling and explicit discourse relation classification, a much easier problem. Our experimental results show that this method can even marginally outperform the state-of-the-art, in spite of being much simpler than alternative models of comparable performance. Moreover, we show that the achieved performance is robust across domains as suggested by the zero-shot experiments on a completely different domain. This indicates that recent advances in language modeling have made language models sufficiently good at capturing inter-sentence relations without the help of explicit discourse markers.
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation

A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses. This inspired recent research on few-shot WSD using meta-learning. While such work has successfully applied meta-learning to learn new word senses from very few examples, its performance still lags behind its fully-supervised counterpart. Aiming to further close this gap, we propose a model of semantic memory for WSD in a meta-learning setting. Semantic memory encapsulates prior experiences seen throughout the lifetime of the model, which aids better generalization in limited data settings. Our model is based on hierarchical variational inference and incorporates an adaptive memory update rule via a hypernetwork. We show our model advances the state of the art in few-shot WSD, supports effective learning in extremely data scarce (e.g. one-shot) scenarios and produces meaning prototypes that capture similar senses of distinct words.
Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping

Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data. Despite their success, GNNs suffer from sub-optimal generalization performance given limited training data, referred to as over-fitting. This paper proposes Topology Adaptive Edge Dropping (TADropEdge) method as an adaptive data augmentation technique to improve generalization performance and learn robust GNN models. We start by explicitly analyzing how random edge dropping increases the data diversity during training, while indicating i.i. d. edge dropping does not account for graph structural information and could result in noisy augmented data degrading performance. To overcome this issue, we consider graph connectivity as the key property that captures graph topology. TADropEdge incorporates this factor into random edge dropping such that the edge-dropped subgraphs maintain similar topology as the underlying graph, yielding more satisfactory data augmentation. In particular, TADropEdge first leverages the graph spectrum to assign proper weights to graph edges, which represent their criticality for establishing the graph connectivity. It then normalizes the edge weights and drops graph edges adaptively based on their normalized weights. Besides improving generalization performance, TADropEdge reduces variance for efficient training and can be applied as a generic method modular to different GNN models. Intensive experiments on real-life and synthetic datasets corroborate theory and verify the effectiveness of the proposed method.
Right-Most Position of a Last Progeny Modified Branching Random Walk

In this work, we consider a modification of the usual Branching Random Walk (BRW), where we give certain independent and identically distributed (i.i.d.) displacements to all the particles at the $n$-th generation, which may be different from the driving increment distribution. We call this process \emph{last progeny modified branching random walk (LPM-BRW)}. Depending on the value of a parameter, $\theta$, we classify the model in three distinct cases, namely, the boundary case, below the boundary case, and above the boundary case. Under very minimal assumptions on the underlying point process of the increments, we show that at the \emph{boundary case}, $\theta=\theta_0$, where $\theta_0$ is a parameter value associated with the displacement point process, the maximum displacement converges to a limit after only an appropriate centering, which is of the form $c_1 n - c_2 \log n$. We give an explicit formula for the constants $c_1$ and $c_2$ and show that $c_1$ is exactly the same, while $c_2$ is $1/3$ of the corresponding constants of the usual BRW Aidekon (2013). We also characterize the limiting distribution. We further show that below the boundary, $\theta<\theta_0$, the logarithmic correction term is absent. For above the boundary case, $\theta>\theta_0$, the logarithmic correction term is exactly the same as that of the classical BRW. For $\theta \leq \theta_0$, we further derive Brunet-Derrida -type results of point process convergence of our LPM-BRW to a Poisson point process. Our proofs are based on a novel method of coupling the maximum displacement with a \emph{linear statistic} associated with a more well-studied process in statistics, known as the \emph{smoothing transformation}.
PROST: Physical Reasoning about Objects through Space and Time

We present a new probing dataset named PROST: Physical Reasoning about Objects Through Space and Time. This dataset contains 18,736 multiple-choice questions made from 14 manually curated templates, covering 10 physical reasoning concepts. All questions are designed to probe both causal and masked language models in a zero-shot setting. We conduct an extensive analysis which demonstrates that state-of-the-art pretrained models are inadequate at physical reasoning: they are influenced by the order in which answer options are presented to them, they struggle when the superlative in a question is inverted (e.g., most<->least), and increasing the amount of pretraining data and parameters only yields minimal improvements. These results provide support for the hypothesis that current pretrained models' ability to reason about physical interactions is inherently limited by a lack of real world experience. By highlighting these limitations, we hope to motivate the development of models with a human-like understanding of the physical world.
Self-Damaging Contrastive Learning

The recent breakthrough achieved by contrastive learning accelerates the pace for deploying unsupervised training on real-world data applications. However, unlabeled data in reality is commonly imbalanced and shows a long-tail distribution, and it is unclear how robustly the latest contrastive learning methods could perform in the practical scenario. This paper proposes to explicitly tackle this challenge, via a principled framework called Self-Damaging Contrastive Learning (SDCLR), to automatically balance the representation learning without knowing the classes. Our main inspiration is drawn from the recent finding that deep models have difficult-to-memorize samples, and those may be exposed through network pruning. It is further natural to hypothesize that long-tail samples are also tougher for the model to learn well due to insufficient examples. Hence, the key innovation in SDCLR is to create a dynamic self-competitor model to contrast with the target model, which is a pruned version of the latter. During training, contrasting the two models will lead to adaptive online mining of the most easily forgotten samples for the current target model, and implicitly emphasize them more in the contrastive loss. Extensive experiments across multiple datasets and imbalance settings show that SDCLR significantly improves not only overall accuracies but also balancedness, in terms of linear evaluation on the full-shot and few-shot settings. Our code is available at: https://github.com/VITA-Group/SDCLR.
PAIO: A Software-Defined Storage Data Plane Framework

We propose PAIO, the first general-purpose framework that enables system designers to build custom-made Software-Defined Storage (SDS) data plane stages. It provides the means to implement storage optimizations adaptable to different workflows and user-defined policies, and allows straightforward integration with existing applications and I/O layers. PAIO allows stages to be integrated with modern SDS control planes to ensure holistic control and system-wide optimal performance. We demonstrate the performance and applicability of PAIO with two use cases. The first improves 99th percentile latency by 4x in industry-standard LSM-based key-value stores. The second ensures dynamic per-application bandwidth guarantees under shared storage environments.
Preservation of the Global Knowledge by Not-True Self Knowledge Distillation in Federated Learning

In federated learning, a strong global model is collaboratively learned by aggregating clients’ locally trained models. Although this precludes the need to access clients’ data directly, the global model’s convergence often suffers from data heterogeneity. This study starts from an analogy to continual learning and suggests that forgetting could be the bottleneck of federated learning. We observe that the global model forgets the knowledge from previous rounds, and the local training induces forgetting the knowledge outside of the local distribution. Based on our ﬁndings, we hypothesize that tackling down forgetting will relieve the data heterogeneity problem. To this end, we propose a novel and effective algorithm, Federated Not-True Distillation (FedNTD), which preserves the global perspective on locally available data only for the not-true classes. In the experiments, FedNTD shows state-of-the-art performance on various setups without compromising data privacy or incurring additional communication costs.
CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings

Without positional information, attention-based Transformer neural networks are permutation-invariant. Absolute or relative positional embeddings are the most popular ways to feed Transformer models with positional information. Absolute positional embeddings are simple to implement, but suffer from generalization issues when evaluating on sequences longer than seen at training time. Relative positions are more robust to input length change, but are more complex to implement and yield inferior model throughput due to extra computational and memory costs. In this paper, we propose an augmentation-based approach (CAPE) for absolute positional embeddings, which keeps the advantages of both absolute (simplicity and speed) and relative positional embeddings (better generalization). In addition, our empirical evaluation on state-of-the-art models in machine translation, image and speech recognition demonstrates that CAPE leads to better generalization performance as well as increased stability with respect to training hyper-parameters.
Study of the bottom-hole rock stress field under water jet impact

ABSTRACT On the basis of analysis of the bottom-hole rock stress field under water jet impact, there are three types of coupling, which include the coupling of pore fluid and water jet, the coupling of pore fluid and rock matrix, and the coupling of water jet and rock matrix. The fluid-solid coupling model with the four main factors of three-dimensional in-situ stress, fluid column pressure, pore pressure, and water jet velocity is established and calculated by the finite element method and the finite volume method. The results show that the maximum principal stress of the bottom-hole increases with the increase of bottom-hole differential pressure. The maximum jet impact force is proportional to the square of the jet velocity; the pore pressures on the impact surface and along impact axis decrease in the form of cubic parabola with the increase of the distance. The local effect is obvious under water jet impact; the main affected area of the water jet is 2 times jet radius on the impact surface and 2.5 to 3.5 times jet radius along the impact axis when water jet velocity increases from 50 to 250 m/s, which is consistent with the stress wave attenuation theory. Study of the bottom-hole rock stress field under water jet impact provides a numerical research method for study of the water jet rock breaking mechanism under actual drilling conditions and the theoretical basis for faster and more efficient drilling.
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction

Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed which rely on a model of chemical reactivity. In this study, we model single-step retrosynthesis in a template-based approach using modern Hopfield networks (MHNs). We adapt MHNs to associate different modalities, reaction templates and molecules, which allows the model to leverage structural information about reaction templates. This approach significantly improves the performance of template relevance prediction, especially for templates with few or zero training examples. With inference speed several times faster than that of baseline methods, we improve predictive performance for top-k exact match accuracy for $\mathrm{k}\geq5$ in the retrosynthesis benchmark USPTO-50k.