Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense

Deep neural networks, particularly in vision tasks, are notably susceptible to adversarial perturbations. To overcome this challenge, developing a robust classifier is crucial. In light of the recent advancements in the robustness of classifiers, we delve deep into the intricacies of adversarial training and Jacobian regularization, two pivotal defenses. Our work is the first carefully analyzes and characterizes these two schools of approaches, both theoretically and empirically, to demonstrate how each approach impacts the robust learning of a classifier. Next, we propose our novel Optimal Transport with Jacobian regularization method, dubbed OTJR, bridging the input Jacobian regularization with the a output representation alignment by leveraging the optimal transport theory. In particular, we employ the Sliced Wasserstein distance that can efficiently push the adversarial samples' representations closer to those of clean samples, regardless of the number of classes within the dataset. The SW distance provides the adversarial samples' movement directions, which are much more informative and powerful for the Jacobian regularization. Our empirical evaluations set a new standard in the domain, with our method achieving commendable accuracies of 52.57% on CIFAR-10 and 28.3% on CIFAR-100 datasets under the AutoAttack. Further validating our model's practicality, we conducted real-world tests by subjecting internet-sourced images to online adversarial attacks. These demonstrations highlight our model's capability to counteract sophisticated adversarial perturbations, affirming its significance and applicability in real-world scenarios.
Differential Item Functioning Detection With the Mantel-Haenszel Procedure: The Effects of Matching Types and Other Factors

The Mantel-Haenszel (MH) procedure is commonly used to detect items that function differentially for groups of examinees from various demographic and linguistic backgrounds—for example, in international assessments. As in some other DIF methods, the total score is used to match examinees on ability. In thin matching, each of the total score points is used as its own matching category, whereas in thick matching the total score is discretized into several score ranges. Evidence regarding how matching type affects the accuracy of MH procedure is inconclusive. The current study investigated the effects of thin and thick matching in conjunction with sample size, purification, symmetric and asymmetric groups sample sizes, test length, and differences in the ability distributions. Results suggest that whenever feasible, purification should be used in conjunction with thin matching.
Mind meets machine: Unravelling GPT-4's cognitive psychology

Cognitive psychology delves on understanding perception, attention, memory, language, problem-solving, decision-making, and reasoning. Large language models (LLMs) are emerging as potent tools increasingly capable of performing human-level tasks. The recent development in the form of GPT-4 and its demonstrated success in tasks complex to humans exam and complex problems has led to an increased confidence in the LLMs to become perfect instruments of intelligence. Although GPT-4 report has shown performance on some cognitive psychology tasks, a comprehensive assessment of GPT-4, via the existing well-established datasets is required. In this study, we focus on the evaluation of GPT-4's performance on a set of cognitive psychology datasets such as CommonsenseQA, SuperGLUE, MATH and HANS. In doing so, we understand how GPT-4 processes and integrates cognitive psychology with contextual information, providing insight into the underlying cognitive processes that enable its ability to generate the responses. We show that GPT-4 exhibits a high level of accuracy in cognitive psychology tasks relative to the prior state-of-the-art models. Our results strengthen the already available assessments and confidence on GPT-4's cognitive psychology abilities. It has significant potential to revolutionize the field of AI, by enabling machines to bridge the gap between human and machine reasoning.
Being an Influencer is Hard: The Complexity of Influence Maximization in Temporal Graphs with a Fixed Source

We consider the influence maximization problem over a temporal graph, where there is a single fixed source. We deviate from the standard model of influence maximization, where the goal is to choose the set of most influential vertices. Instead, in our model we are given a fixed vertex, or source, and the goal is to find the best time steps to transmit so that the influence of this vertex is maximized. We frame this problem as a spreading process that follows a variant of the susceptible-infected-susceptible (SIS) model and we focus on four objective functions. In the MaxSpread objective, the goal is to maximize the total number of vertices that get infected at least once. In the MaxViral objective, the goal is to maximize the number of vertices that are infected at the same time step. In the MaxViralTstep objective, the goal is to maximize the number of vertices that are infected at a given time step. Finally, in MinNonViralTime, the goal is to maximize the total number of vertices that get infected every $d$ time steps. We perform a thorough complexity theoretic analysis for these four objectives over three different scenarios: (1) the unconstrained setting where the source can transmit whenever it wants; (2) the window-constrained setting where the source has to transmit at either a predetermined, or a shifting window; (3) the periodic setting where the temporal graph has a small period. We prove that all of these problems, with the exception of MaxSpread for periodic graphs, are intractable even for very simple underlying graphs.
Microbial life strategies in relation to Monod curves

Microbial diversity is often discussed in terms of dichotomies such as copiotrophic/oligotrophic and fast/slow-growing microbes. We put these dichotomies in an ecological context by discussing their connections with Monod curves of specific growth rate versus limiting nutrient concentration. We find correspondences between Monod curves for laboratory cultures of isolated microbes and key ecological parameters in McArthur and Wilson’s r/K selection theory, Tilman’s resource competition and community structure theory and Grime’s triangle of life strategies. These parallels allow us in the first place to compare copiotrophic/oligotrophic and fast/slow-growing microbes with well-studied life strategies. Second, standard laboratory experiments can yield quantitative parameters useful for microbial ecology studies. Third, we can partly explain the microbial diversity observed in metagenomic experiments by considering viable carbon sources for heterotrophic microbes in the framework of community structure theory. Fourth, we can reconcile the r/K,
On two-dimensional filter stability test

In this paper, a new procedure of 2-D filter stability test is proposed based on linking and further development of some newly published contributions of the authors. The modified polynomial array given in this paper eliminates redundant information introduced in the ordinary table technique and leads to a great reduction in the order of polynomial entries of the array from n/sub 2/2n/sup (1/-1) to n/sub 1/n/sub 2/, where n/sub 1/ and n/sub 2/ are the orders of F(z1, z/sub 2/), the characteristic polynomial of the filter under test, in z/sub 1/ and z/sub 2/ respectively. The properties of the array are studied and a systematic construction procedure given. The new test procedure applies twice to the array. The first application involves only numerical entries, but provides a simple way for a primary checking of some necessary conditions before any further complex computations are required. The second application involves constructing of the polynomial array and the positivity testing of only the last entry of the array for all |z/sub 1/|=1. The simplicity of the method may allow it to be applied for filter designs. >
Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving

In recent times, there has been a growing focus on end-to-end autonomous driving technologies. This technology involves the replacement of the entire driving pipeline with a single neural network, which has a simpler structure and faster inference time. However, while this approach reduces the number of components in the driving pipeline, it also presents challenges related to interpretability and safety. For instance, the trained policy may not always comply with traffic rules, and it is difficult to determine the reason for such misbehavior due to the lack of intermediate outputs. Additionally, the successful implementation of autonomous driving technology heavily depends on the reliable and expedient processing of sensory data to accurately perceive the surrounding environment. In this paper, we provide penalty-based imitation learning approach combined with cross semantics generation sensor fusion technologies (P-CSG) to efficiently integrate multiple modalities of information and enable the autonomous agent to effectively adhere to traffic regulations. Our model undergoes evaluation within the Town 05 Long benchmark, where we observe a remarkable increase in the driving score by more than 12% when compared to the state-of-the-art (SOTA) model, InterFuser. Notably, our model achieves this performance en-hancement while achieving a 7- fold increase in inference speed and reducing the model size by approximately 30%. For more detailed information, including code-based resources, they can be found at https://hk-zh.github. io/p-csg/
cTBLS: Augmenting Large Language Models with Conversational Tables

Optimizing accuracy and performance while eliminating hallucinations of open-domain conversational large language models (LLMs) is an open research challenge. A particularly promising direction is to augment and ground LLMs with information from structured sources. This paper introduces Conversational Tables cTBLS, a three-step architecture to retrieve and generate dialogue responses grounded on retrieved tabular information. cTBLS uses Transformer encoder embeddings for Dense Table Retrieval and obtains up to 125% relative improvement over the retriever in the previous state-of-the-art system on the HyrbiDialogue dataset. cTBLS then uses a shared process between encoder and decoder models to perform a coarse+fine tabular knowledge (e.g., cell) ranking combined with a GPT-3.5 LLM response generator to yield a 2x relative improvement in ROUGE scores. Finally, human evaluators prefer cTBLs +80% of the time (coherency, fluency) and judge informativeness to be 4x better than the previous state-of-the-art.
Particle acceleration at magnetized, relativistic, turbulent shock fronts

The efficiency of particle acceleration at shock waves in relativistic, magnetized astrophysical outflows is a debated topic with far-reaching implications. Here, for the first time, we study the impact of turbulence in the pre-shock plasma. Our simulations demonstrate that, for a mildly relativistic, magnetized pair shock (Lorentz factor $\gamma_{\rm sh} \simeq 2.7$, magnetization level $\sigma \simeq 0.01$), strong turbulence can revive particle acceleration in a superluminal configuration that otherwise prohibits it. Depending on the initial plasma temperature and magnetization, stochastic-shock-drift or diffusive-type acceleration governs particle energization, producing powerlaw spectra $\mathrm{d}N/\mathrm{d}\gamma \propto \gamma^{-s}$ with $s \sim 2.5-3.5$. At larger magnetization levels, stochastic acceleration within the pre-shock turbulence becomes competitive and can even take over shock acceleration.
Notions of enriched purity

We introduce the enriched notions of purity depending on the left class $\mathcal E$ of a factorization system on the base $\mathcal V$ of enrichment. The ordinary purity is given by the class of surjective mappings in the category of sets. Under specific assumptions, covering enrichment over quantale-valued metric spaces, $\omega$-complete posets, and quasivarieties, we characterize the $(\lambda,\mathcal E)$-injectivity classes of locally presentable $\mathcal V$-categories in terms of closure under a class of limits, $\lambda$-filtered colimits, and $(\lambda,\mathcal E)$-pure subobjects.
Here comes the SU(N): multivariate quantum gates and gradients

Variational quantum algorithms use non-convex optimization methods to find the optimal parameters for a parametrized quantum circuit in order to solve a computational problem. The choice of the circuit ansatz, which consists of parameterized gates, is crucial to the success of these algorithms. Here, we propose a gate which fully parameterizes the special unitary group SU(N). This gate is generated by a sum of non-commuting operators, and we provide a method for calculating its gradient on quantum hardware. In addition, we provide a theorem for the computational complexity of calculating these gradients by using results from Lie algebra theory. In doing so, we further generalize previous parameter-shift methods. We show that the proposed gate and its optimization satisfy the quantum speed limit, resulting in geodesics on the unitary group. Finally, we give numerical evidence to support the feasibility of our approach and show the advantage of our gate over a standard gate decomposition scheme. In doing so, we show that not only the expressibility of an ansatz matters, but also how it's explicitly parameterized.
Data-Efficient Large Scale Place Recognition with Graded Similarity Supervision

Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or not. Such a binary indication does not consider continuous relations of similarity between images of the same place taken from different positions, determined by the continuous nature of camera pose. The binary similarity induces a noisy supervision signal into the training of VPR methods, which stall in local minima and require expensive hard mining algorithms to guarantee convergence. Motivated by the fact that two images of the same place only partially share visual cues due to camera pose differences, we deploy an automatic re-annotation strategy to re-label VPR datasets. We compute graded similarity labels for image pairs based on available localization metadata. Furthermore, we propose a new Generalized Contrastive Loss (GCL) that uses graded similarity labels for training contrastive networks. We demonstrate that the use of the new labels and GCL allow to dispense from hard-pair mining, and to train image descriptors that perform better in VPR by nearest neighbor search, obtaining superior or comparable results than methods that require expensive hard-pair mining and re-ranking techniques.
ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders

We propose ViC-MAE, a model that combines both Masked AutoEncoders (MAE) and contrastive learning. ViC-MAE is trained using a global featured obtained by pooling the local representations learned under an MAE reconstruction loss and leveraging this representation under a contrastive objective across images and video frames. We show that visual representations learned under ViC-MAE generalize well to both video and image classification tasks. Particularly, ViC-MAE obtains state-of-the-art transfer learning performance from video to images on Imagenet-1k compared to the recently proposed OmniMAE by achieving a top-1 accuracy of 86% (+1.3% absolute improvement) when trained on the same data and 87.1% (+2.4% absolute improvement) when training on extra data. At the same time ViC-MAE outperforms most other methods on video benchmarks by obtaining 75.9% top-1 accuracy on the challenging Something something-v2 video benchmark . When training on videos and images from a diverse combination of datasets, our method maintains a balanced transfer-learning performance between video and image classification benchmarks, coming only as a close second to the best supervised method.
MODEFORMER: Modality-Preserving Embedding For Audio-Video Synchronization Using Transformers

Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience. A widely accepted paradigm is to create an error detection mechanism that identifies the cases when audio is leading or lagging. We propose ModEFormer, which independently extracts audio and video embeddings using modality-specific transformers. Different from the other transformer-based approaches, ModEFormer preserves the modality of the input streams which allows us to use a larger batch size with more negative audio samples for contrastive learning. Further, we propose a tradeoff between the number of negative samples and number of unique samples in a batch to significantly exceed the performance of previous methods. Experimental results show that ModEFormer achieves state-of-the-art performance, 94.5% for LRS2 and 90.9% for LRS3. Finally, we demonstrate how ModEFormer can be used for offset detection for test clips.
Dynamics-Aware Loss for Learning with Label Noise

Label noise poses a serious threat to deep neural networks (DNNs). Employing robust loss functions which reconcile fitting ability with robustness is a simple but effective strategy to handle this problem. However, the widely-used static trade-off between these two factors contradicts the dynamics of DNNs learning with label noise, leading to inferior performance. Therefore, we propose a dynamics-aware loss (DAL) to solve this problem. Considering that DNNs tend to first learn beneficial patterns, then gradually overfit harmful label noise, DAL strengthens the fitting ability initially, then gradually improves robustness. Moreover, at the later stage, to further reduce the negative impact of label noise and combat underfitting simultaneously, we let DNNs put more emphasis on easy examples than hard ones and introduce a bootstrapping term. Both the detailed theoretical analyses and extensive experimental results demonstrate the superiority of our method. Our source code can be found in https://github.com/XiuchuanLi/DAL.
X-Ray properties of optically selected QSOs

The dependence of the X-ray-to-optical luminosity ratio on optical luminosity and redshift for optically selected QSOs is studied, largely on the basis of two, complete, magnitude-limited samples (Bright Quasar Survey /1983/ and Braccesi Faint /1984/) which were observed with the Einstein Observatory. Heterogeneous samples are established as adequate for the study of that dependence. Optimal choices for increasing the size of the data set for such a study are pointed out. The previous results of Avni and Tananbaum for alpha sub 0, x(z, L sub opt) are confirmed and strengthened, and the numerical sensitivity to changes in the values of q sub 0 and of the optical spectral index is evaluated. It is shown that the large majority, probably all, of optically selected QSOs are X-ray loud; no more than a few percent can be X-ray quiet. Thus X-ray emission appears to be a universal property of QSOs. It is shown that comparisons of optically selected QSOs with X-ray selected QSOs are numerically sensitive to the details of the input ingredients. A residual discrepancy of about a factor of 2 between calculated and observed X-ray number counts is found. Directions for further research that are important for understanding the fullmore » bivariate optical-X-ray evolution and luminosity function for QSOs are discussed. 36 references. « less
Tailored inference for finite populations: conditional validity and transfer across distributions

Parameters of sub-populations can be more relevant than those of super-populations. For example, a healthcare provider may be interested in the effect of a treatment plan for a specific subset of their patients; policymakers may be concerned with the impact of a policy in a particular state within a given population. In these cases, the focus is on a specific finite population, as opposed to an infinite super-population. Such a population can be characterized by fixing some attributes that are intrinsic to them, leaving unexplained variations like measurement error as random. Inference for a population with fixed attributes can then be modelled as inferring parameters of a conditional distribution. Accordingly, it is desirable that confidence intervals are conditionally valid for the realized population, instead of marginalizing over many possible draws of populations. We provide a statistical inference framework for parameters of finite populations with known attributes. Leveraging the attribute information, our estimators and confidence intervals closely target a specific finite population. When the data is from the population of interest, our confidence intervals attain asymptotic conditional validity given the attributes, and are shorter than those for super-population inference. In addition, we develop procedures to infer parameters of new populations with differing covariate distributions; the confidence intervals are also conditionally valid for the new populations under mild conditions. Our methods extend to situations where the fixed information has a weaker structure or is only partially observed. We demonstrate the validity and applicability of our methods using simulated and real-world data.
Asymmetric Di-Higgs Signals of the N2HDM-$U(1)$

The two-Higgs-doublet model with a $U(1)_H$ gauge symmetry (N2HDM-$U(1)$) has several advantages compared to the $Z_2$ version (N2HDM-$Z_2$): It is purely based on gauge symmetries, involves only spontaneous symmetry breaking and is more predictive because it contains one less free parameter in its Higgs potential which ensures $CP$ conservation at the same time. However, the phenomenology of its Higgs sector has been barely studied. After pointing out that a second, so far unknown version of the N2HDM-$U(1)$ exists, we examine the phenomenological consequences of the differences in the scalar potentials. In particular, we find that while the N2HDM-$Z_2$ predicts suppressed branching ratios for decays into different Higgses for small scalar mixing (as suggested by Higgs coupling measurements), both versions of the N2HDM-$U(1)$ allow for sizable rates. This is particularly important in light of the CMS excess in Higgs pair production at around $650\,$GeV decaying a Standard Model Higgs decaying to photons and a new scalar with a mass of $\approx90\,$GeV decaying to bottom quarks (i.e., compatible with the CMS $\gamma\gamma$ excess at $95\,$GeV), which, as we will show, can be explained within the N2HDM-$U(1)$, predicting an interesting and unavoidable $Z+ b\bar b$ signature.
Gauge invariance on the light-cone: curvature perturbations and radiative degrees of freedom

We derive the expressions on the observed light-cone for some relevant cosmological gauge invariant variables, such as the Mukhanov-Sasaki variable and E- and B- modes of the tensor perturbations. Since the structure of the light-cone does not reflect in a direct way the FLRW symmetries, we develop a formalism which is coordinate independent and classifies the perturbations according to their helicities. Even though we work with linear perturbations, our formalism can be readily extended to non-linear theory and put the basis to study the evolution of cosmological perturbations, since the early- until the late-time Universe, directly along the observed light-cone.