Miracle-dz/UniFAR-FTUs huggingface dataset:
Registration based Few-Shot Anomaly Detection

This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided for each category at training. So far, existing FSAD studies follow the one-model-per-category learning paradigm used for standard AD, and the inter-category commonality has not been explored. Inspired by how humans detect anomalies, i.e., comparing an image in question to normal images, we here leverage registration, an image alignment task that is inherently generalizable across categories, as the proxy task, to train a category-agnostic anomaly detection model. During testing, the anomalies are identified by comparing the registered features of the test image and its corresponding support (normal) images. As far as we know, this is the first FSAD method that trains a single generalizable model and requires no re-training or parameter fine-tuning for new categories. Experimental results have shown that the proposed method outperforms the state-of-the-art FSAD methods by 3%-8% in AUC on the MVTec and MPDD benchmarks.
A tight quasi-polynomial bound for Global Label Min-Cut

We study a generalization of the classic Global Min-Cut problem, called Global Label Min-Cut (or sometimes Global Hedge Min-Cut): the edges of the input (multi)graph are labeled (or partitioned into color classes or hedges), and removing all edges of the same label (color or from the same hedge) costs one. The problem asks to disconnect the graph at minimum cost. While the $st$-cut version of the problem is known to be NP-hard, the above global cut version is known to admit a quasi-polynomial randomized $n^{O(\log \mathrm{OPT})}$-time algorithm due to Ghaffari, Karger, and Panigrahi [SODA 2017]. They consider this as ``strong evidence that this problem is in P''. We show that this is actually not the case. We complete the study of the complexity of the Global Label Min-Cut problem by showing that the quasi-polynomial running time is probably optimal: We show that the existence of an algorithm with running time $(np)^{o(\log n/ (\log \log n)^2)}$ would contradict the Exponential Time Hypothesis, where $n$ is the number of vertices, and $p$ is the number of labels in the input. The key step for the lower bound is a proof that Global Label Min-Cut is W[1]-hard when parameterized by the number of uncut labels. In other words, the problem is difficult in the regime where almost all labels need to be cut to disconnect the graph. To turn this lower bound into a quasi-polynomial-time lower bound, we also needed to revisit the framework due to Marx [Theory Comput. 2010] of proving lower bounds assuming Exponential Time Hypothesis through the Subgraph Isomorphism problem parameterized by the number of edges of the pattern. Here, we provide an alternative simplified proof of the hardness of this problem that is more versatile with respect to the choice of the regimes of the parameters.
Strains induced around cutoff walls of earth dam under full reservoir and drawdown conditions

This paper presents the study pertaining to the strains that are developed near the cutoff walls employed in the foundation medium of the dam section. A total of sixteen finite element simulations are considered in the study, where the combination and placement of the cutoff walls in the foundation medium are varied. A cutoff wall is made to act as a seepage barrier with assigned linear elastic properties. A hypothetical earth dam of height 35 m is provided with a slope of 1V: 2.5H for both the downstream and the upstream sides. The finite element analyses are carried out using PLAXIS – 3D to obtain the principal strains and their variation with respect to time for two different conditions viz. Full reservoir steady seepage and Drawdown conditions. The variations in strains in the dam section and in the foundation medium are found to be more predominant for the two barriers with increased Young’s modulus having a spacing of 15 m, first one being located at 0 m and second at 15 m from the center of the dam. The excessive strains would initiate cracks in the cutoff walls. This fact has to be taken to advantage while finalizing the sections for the cutoff walls.
A category-theoretic proof of the ergodic decomposition theorem

Abstract The ergodic decomposition theorem is a cornerstone result of dynamical systems and ergodic theory. It states that every invariant measure on a dynamical system is a mixture of ergodic ones. Here, we formulate and prove the theorem in terms of string diagrams, using the formalism of Markov categories. We recover the usual measure-theoretic statement by instantiating our result in the category of stochastic kernels. Along the way, we give a conceptual treatment of several concepts in the theory of deterministic and stochastic dynamical systems. In particular, ergodic measures appear very naturally as particular cones of deterministic morphisms (in the sense of Markov categories), and the invariant $\sigma $ -algebra of a dynamical system can be seen as a colimit in the category of Markov kernels. In line with other uses of category theory, once the necessary structures are in place, our proof of the main theorem is much more intuitive than traditional approaches. In particular, it does not use any quantitative limiting arguments, and it does not rely on the cardinality of the group or monoid indexing the dynamics. We hope that this result paves the way for further applications of category theory to dynamical systems, ergodic theory, and information theory.
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning

We study the multi-step off-policy learning approach to distributional RL. Despite the apparent similarity between value-based RL and distributional RL, our study reveals intriguing and fundamental differences between the two cases in the multi-step setting. We identify a novel notion of path-dependent distributional TD error, which is indispensable for principled multi-step distributional RL. The distinction from the value-based case bears important implications on concepts such as backward-view algorithms. Our work provides the first theoretical guarantees on multi-step off-policy distributional RL algorithms, including results that apply to the small number of existing approaches to multi-step distributional RL. In addition, we derive a novel algorithm, Quantile Regression-Retrace, which leads to a deep RL agent QR-DQN-Retrace that shows empirical improvements over QR-DQN on the Atari-57 benchmark. Collectively, we shed light on how unique challenges in multi-step distributional RL can be addressed both in theory and practice.
Flow-augmentation III: Complexity dichotomy for Boolean CSPs parameterized by the number of unsatisfied constraints

We study the parameterized problem of satisfying ``almost all'' constraints of a given formula $F$ over a fixed, finite Boolean constraint language $\Gamma$, with or without weights. More precisely, for each finite Boolean constraint language $\Gamma$, we consider the following two problems. In Min SAT$(\Gamma)$, the input is a formula $F$ over $\Gamma$ and an integer $k$, and the task is to find an assignment $\alpha \colon V(F) \to \{0,1\}$ that satisfies all but at most $k$ constraints of $F$, or determine that no such assignment exists. In Weighted Min SAT$(\Gamma$), the input additionally contains a weight function $w \colon F \to \mathbb{Z}_+$ and an integer $W$, and the task is to find an assignment $\alpha$ such that (1) $\alpha$ satisfies all but at most $k$ constraints of $F$, and (2) the total weight of the violated constraints is at most $W$. We give a complete dichotomy for the fixed-parameter tractability of these problems: We show that for every Boolean constraint language $\Gamma$, either Weighted Min SAT$(\Gamma)$ is FPT; or Weighted Min SAT$(\Gamma)$ is W[1]-hard but Min SAT$(\Gamma)$ is FPT; or Min SAT$(\Gamma)$ is W[1]-hard. This generalizes recent work of Kim et al. (SODA 2021) which did not consider weighted problems, and only considered languages $\Gamma$ that cannot express implications $(u \to v)$ (as is used to, e.g., model digraph cut problems). Our result generalizes and subsumes multiple previous results, including the FPT algorithms for Weighted Almost 2-SAT, weighted and unweighted $\ell$-Chain SAT, and Coupled Min-Cut, as well as weighted and directed versions of the latter. The main tool used in our algorithms is the recently developed method of directed flow-augmentation (Kim et al., STOC 2022).
Cooperative Marine Operations via Ad Hoc Teams

While research in ad hoc teamwork has great potential for solving real-world robotic applications, most developments so far have been focusing on environments with simple dynamics. In this article, we discuss how the problem of ad hoc teamwork can be of special interest for marine robotics and how it can aid marine operations. Particularly, we present a set of challenges that need to be addressed for achieving ad hoc teamwork in underwater environments and we discuss possible solutions based on current state-of-the-art developments in the ad hoc teamwork literature.
Impulsively generated kink wave trains in solar coronal slabs

We numerically follow the response of density-enhanced slabs to impulsive, localized, transverse velocity perturbations by working in the framework of ideal magnetohydrodynamics (MHD). Both linear and nonlinear regimes are addressed. Kink wave trains are seen to develop along the examined slabs, sharing the characteristics that more oscillatory patterns emerge with time and that the apparent wavelength increases with distance at a given instant. Two features none the less arise due to nonlinearity, one being a density cavity close to the exciter and the other being the appearance of shocks both outside and inside the nominal slab. These features may be relevant for understanding the interaction between magnetic structures and such explosive events as coronal mass ejections. Our numerical findings on kink wave trains in solar coronal slabs are discussed in connection with typical measurements of streamer waves.
A phase transition driven by subtle distortion without broken symmetry on spin, charge and lattice in Layered LnCu4-{\delta}P2(Ln=Eu, Sr)

In the scenario of Landau phase transition theory in condensed matter physics, any thermal dynamic phase transition must be subject to some kind of broken symmetries, that are relative to its spin, charge, orbital and lattice. Here we report a rare phase transition at T p ~120 K or 140 K in layered materials Ln Cu 4-δ P 2 ( Ln =Eu, Sr) driven by a subtle structural-distortion without any broken symmetry on charge, spin and lattice. The variations of the lattice parameters, ( ΔL c / L c ) ~ 0.013% or 0.062%, verified by thermal expansion, is much less than that for a typical crystalline phase transition (~0.5-1%), but the significant anomaly in heat capacity provides clear evidence of its intrinsic nature of thermodynamic transition.
Pantheon+
 constraints on dark energy and modified gravity: An evidence of dynamical dark energy

We use the latest Type Ia supernovae sample Pantheon+ to explore new physics on cosmic scales. Specifically, in light of this new sample, we constrain the interacting dark energy and Hu-Sawicki $f(R)$ gravity models and employ the model-independent Gaussian processes to investigate whether there is an evidence of dark energy evolution. We find that Pantheon+ alone just gives weak constraints. However, a data combination of Pantheon+ with cosmic microwave background, baryon acoustic oscillations and cosmic chronometers gives a strong constraint on the modified matter expansion rate $\epsilon=0.048\pm0.026$, which indicates that the momentum may transfer from dark energy to dark matter in the dark sector of the universe at the $1.85\,\sigma$ confidence level. In the meantime, we obtain a very tight constraint on the deviation from general relativity $\log_{10} f_{R0}<-6.32$ at the $2\,\sigma$ confidence level. Interestingly, when combining Pantheon+ with cosmic chronometers and cosmic microwave background data, we find a quintessence-like dark energy signal beyond the $2\,\sigma$ confidence level in the redshift range $z\in[0.70,1.05]$. This implies the nature of dark energy may actually be dynamical.
An Approach for Link Prediction in Directed Complex Networks based on Asymmetric Similarity-Popularity

Complex networks are graphs representing real-life systems that exhibit unique characteristics not found in purely regular or completely random graphs. The study of such systems is vital but challenging due to the complexity of the underlying processes. This task has nevertheless been made easier in recent decades thanks to the availability of large amounts of networked data. Link prediction in complex networks aims to estimate the likelihood that a link between two nodes is missing from the network. Links can be missing due to imperfections in data collection or simply because they are yet to appear. Discovering new relationships between entities in networked data has attracted researchers' attention in various domains such as sociology, computer science, physics, and biology. Most existing research focuses on link prediction in undirected complex networks. However, not all real-life systems can be faithfully represented as undirected networks. This simplifying assumption is often made when using link prediction algorithms but inevitably leads to loss of information about relations among nodes and degradation in prediction performance. This paper introduces a link prediction method designed explicitly for directed networks. It is based on the similarity-popularity paradigm, which has recently proven successful in undirected networks. The presented algorithms handle the asymmetry in node relationships by modeling it as asymmetry in similarity and popularity. Given the observed network topology, the algorithms approximate the hidden similarities as shortest path distances using edge weights that capture and factor out the links' asymmetry and nodes' popularity. The proposed approach is evaluated on real-life networks, and the experimental results demonstrate its effectiveness in predicting missing links across a broad spectrum of networked data types and sizes.
Catastrogenesis: DM, GWs, and PBHs from ALP string-wall networks

Axion-like particles (ALPs), a compelling candidate for dark matter (DM), are the pseudo Nambu-Goldstone bosons of a spontaneously and explicitly broken global U(1) symmetry. When the symmetry breaking happens after inflation, the ALP cosmology predicts the formation of a string-wall network which must annihilate early enough, producing gravitational waves (GWs) and primordial black holes (PBHs), as well as non-relativistic ALPs. We call this process catastrogenesis. We show that, under the generic assumption that the potential has several degenerate minima, GWs from string-wall annihilation at temperatures below 100 eV could be detected by future CMB and astrometry probes, for ALPs with mass from 10-16 to 106 eV. In this case, structure formation could limit ALPs to constitute a fraction of the DM and the annihilation would produce mostly “stupendously large” PBHs. For larger annihilation temperatures, ALPs can constitute 100% of DM, and the annihilation could produce supermassive black holes with a mass of up to 109 M ⊙ as found at the center of large galaxies. Therefore our model can solve two mysteries, the nature of the DM and the origin of these black holes.
Feed-Forward Latent Domain Adaptation

We study a new highly-practical problem setting that enables resource-constrained edge devices to adapt a pre-trained model to their local data distributions. Recognizing that device's data are likely to come from multiple latent domains that include a mixture of unlabelled domain-relevant and domain-irrelevant examples, we focus on the comparatively under-studied problem of latent domain adaptation. Considering limitations of edge devices, we aim to only use a pre-trained model and adapt it in a feed-forward way, without using back-propagation and without access to the source data. Modelling these realistic constraints bring us to the novel and practically important problem setting of feed-forward latent domain adaptation. Our solution is to meta-learn a network capable of embedding the mixed-relevance target dataset and dynamically adapting inference for target examples using cross-attention. The resulting framework leads to consistent improvements over strong ERM baselines. We also show that our framework sometimes even improves on the upper bound of domain-supervised adaptation, where only domain-relevant instances are provided for adaptation. This suggests that human annotated domain labels may not always be optimal, and raises the possibility of doing better through automated instance selection.
Plex: Towards Reliability using Pretrained Large Model Extensions

A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. Probing these models' abilities in diverse ways is therefore critical to the field. In this paper, we explore the reliability of models, where we define a reliable model as one that not only achieves strong predictive performance but also performs well consistently over many decision-making tasks involving uncertainty (e.g., selective prediction, open set recognition), robust generalization (e.g., accuracy and proper scoring rules such as log-likelihood on in- and out-of-distribution datasets), and adaptation (e.g., active learning, few-shot uncertainty). We devise 10 types of tasks over 40 datasets in order to evaluate different aspects of reliability on both vision and language domains. To improve reliability, we developed ViT-Plex and T5-Plex, pretrained large model extensions for vision and language modalities, respectively. Plex greatly improves the state-of-the-art across reliability tasks, and simplifies the traditional protocol as it improves the out-of-the-box performance and does not require designing scores or tuning the model for each task. We demonstrate scaling effects over model sizes up to 1B parameters and pretraining dataset sizes up to 4B examples. We also demonstrate Plex's capabilities on challenging tasks including zero-shot open set recognition, active learning, and uncertainty in conversational language understanding.
Alterações na aptidão física de atletas de futebol infantil ao longo de um macrociclo de treinamento Changes in physical fitness of u- 15 soccer players over macrocycles of training

The aim of this study was to evaluate children's ph ysical fitness of footballers over a macro training. The sample consisted of 16 athletes from children's football, with average height of 1.74 m (SD = 0.07) and average body weight equal to 63 kg (SD = 6). We analyzed the aerobic power (VO2max), speed (T30m), explosive power of vertical jump (SV) in 4 different times of macro (P1, P2, P3, P4). Data analysis was used descriptive statistics and one-wa y ANOVA with Bonferroni adjustment. The main results were as follows: VO2max = 13% (P1xP3) and -3.8% (P3xP4); T30m = 4.1% (P1xP2), SV = 5.26% (P2xP3); It was possible to observe the consistency of results with the proposed methodology adopted, although the results have been shown below expectations.
Effects of the long-range neutrino-mediated force in atomic phenomena

As known, electron vacuum polarisation by nuclear Coulomb field produces the Uehling potential with the range . Similarly, neutrino vacuum polarisation by the Z boson field produces a long-range potential with a very large range . Measurements of macroscopic effects produced by the potential give limits on the effective interaction constant which exceed Fermi constant by many orders of magnitude, while limits from the spectroscopy of simple atomic systems are approaching the Standard Model predictions. In this paper, we consider limits on from hydrogen, muonium, positronium, deuteron and molecular hydrogen. Constraints are also obtained on fifth force parameterised by Yukawa-type potential mediated by a scalar particle. GRAPHICAL ABSTRACT
University of Birmingham Interactions between identity and emotional expression in face processing across the lifespan

We tested how aging affects the integration of visual information from faces. Three groups of participants aged 20–30, 40–50, and 60–70 performed a divided attention task in which they had to detect the presence of a target facial identity or a target facial expression. Three target stimuli were used: (1) with the target identity but not the target expression, (2) with the target expression but not the target identity, and (3) with both the target identity and target expression (the redundant target condition). On nontarget trials the faces contained neither the target identity nor expression. All groups were faster in responding to a face containing both the target identity and emotion compared to faces containing either single target. Furthermore the redundancy gains for combined targets exceeded performance limits predicted by the independent processing of facial identity and emotion. These results are held across the age range. The results suggest that there is interactive processing of facial identity and emotion which is independent of the effects of cognitive aging. Older participants demonstrated reliably larger size of the redundancy gains compared to the young group that reflect a greater experience with faces. Alternative explanations are discussed.
PRIMARY STUDY ON INFLUENCE OF FOOD PACKAGING DESIGN BENEFITS ON BRAND ASSOCIATION FROM CONSUMERS’ VIEWS

Product packaging design is the approach with the most cost benefit for establishing brand equity. In severely competitive sales market, it will influence consumers’ cognition, attitude, and preference toward brands and become the key factor of establishing of brand equity. As exploratory research, this study focuses on major dimensions (brand association) of brand equity to probe into the packaging design benefits that most influence brand association, as well as the composition categories of brand association that are easily influenced by packaging design benefits. Focus groups are conducted on consumers and analysis is based on qualitative data. Finally, the researcher indicates the most representative research propositions on influence of packaging design benefits on brand association as the criteria for development of research hypotheses.
Effects of Chronic Internal :-Radiation from Photoassimilated 14C02 on the Retention and Distribution of '4C in Young White Pine Plants1

Eastern white pines (Pinus strobus L.) in their 3rd year of growth photoassimilated 35, 110, 220, or 400 microcuries of 4CO2 on a single occasion in the spring when needle expansion was occurring. Once assimilated into organic products and translocated to various sinks, the retained 14C represented an internal chronic source of ionizing radiation to the plant. About 2.5 months later, the pines were harvested, and the distribution of "C activity was determined. In addition, new needle lengths, fresh weights, and rates of processes involving CO. exchange were measured. All parameters measured were affected in those pines which initially assimilated 220 or 400 microcuries of "CO2, whereas no significant differences were observed between control plants and those initially assimilating 35 microcuries. Plants incor-porating 110 microcuries were intermediate in their responses. The percentage of assimilated "C retained by the plants decreased as the initial activity of assimilated "C increased, with the losses of "C occurring through both respiration and the shedding of needles. The pines which assimilated 35 microcuries of "CO2 retained about 70% of the "C; those which assimilated 400 microcuries retained only about 28%. The distribution of the "lC activity recovered from all the plants was essentially the same; about 65% of the total 14C recovered was in the new needles and 13% in the roots.