Miracle-dz/UniFAR-FTUs huggingface dataset:
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization

Unsupervised anomaly detection in medical imaging aims to detect and localize arbitrary anomalies without requiring annotated anomalous data during training. Often, this is achieved by learning a data distribution of normal samples and detecting anomalies as regions in the image which deviate from this distribution. Most current state-of-the-art methods use latent variable generative models operating directly on the images. However, generative models have been shown to mostly capture low-level features, s.a. pixel-intensities, instead of rich semantic features, which also applies to their representations. We circumvent this problem by proposing CRADL whose core idea is to model the distribution of normal samples directly in the low-dimensional representation space of an encoder trained with a contrastive pretext-task. By utilizing the representations of contrastive learning, we aim to fix the over-fixation on low-level features and learn more semantic-rich representations. Our experiments on anomaly detection and localization tasks using three distinct evaluation datasets show that 1) contrastive representations are superior to representations of generative latent variable models and 2) the CRADL framework shows competitive or superior performance to state-of-the-art.
Families of bosonic suppression laws beyond the permutation symmetry principle

Exact cancellation of quantum amplitudes in multiphoton interferences with Fock states at the input, the so-called suppression or zero transmission laws generalizing the Hong–Ou–Mandel dip, are useful tools in quantum information and computation. It was recently suggested that all bosonic suppression laws follow from a common permutation symmetry both in the input quantum state and in the unitary matrix of the interferometer. By using the recurrence relations for the interference of Fock states, we find a wealth of suppression laws on the beamsplitter and tritter interferometers that do not follow from the permutation symmetry principle. In general, our results point on the existence of whole families of suppression laws with only a fraction of them being accounted for by the permutation symmetry principle, suggested as the general principle behind all the suppression laws.
Critical Perspectives: A Benchmark Revealing Pitfalls in PerspectiveAPI

Detecting “toxic” language in internet content is a pressing social and technical challenge. In this work, we focus on Perspective API from Jigsaw, a state-of-the-art tool that promises to score the “toxicity” of text, with a recent model update that claims impressive results (Lees et al., 2022). We seek to challenge certain normative claims about toxic language by proposing a new benchmark, Selected Adversarial SemanticS, or SASS. We evaluate Perspective on SASS, and compare to low-effort alternatives, like zero-shot and few-shot GPT-3 prompt models, in binary classification settings. We find that Perspective exhibits troubling shortcomings across a number of our toxicity categories. SASS provides a new tool for evaluating performance on previously undetected toxic language that avoids common normative pitfalls. Our work leads us to emphasize the importance of questioning assumptions made by tools already in deployment for toxicity detection in order to anticipate and prevent disparate harms.
Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms

Diversity maximization aims to select a diverse and representative subset of items from a large dataset. It is a fundamental optimization task that finds applications in data summarization, feature selection, web search, recommender systems, and elsewhere. However, in a setting where data items are associated with different groups according to sensitive attributes like sex or race, it is possible that algorithmic solutions for this task, if left unchecked, will under- or over-represent some of the groups. Therefore, we are motivated to address the problem of \emph{max-min diversification with fairness constraints}, aiming to select $k$ items to maximize the minimum distance between any pair of selected items while ensuring that the number of items selected from each group falls within predefined lower and upper bounds. In this work, we propose an exact algorithm based on integer linear programming that is suitable for small datasets as well as a $\frac{1-\varepsilon}{5}$-approximation algorithm for any $\varepsilon \in (0, 1)$ that scales to large datasets. Extensive experiments on real-world datasets demonstrate the superior performance of our proposed algorithms over existing ones.
Exact and Heuristic Approaches to Speeding Up the MSM Time Series Distance Computation

The computation of the distance of two time series is time-consuming for any elastic distance function that accounts for misalignments. Among those functions, DTW is the most prominent. However, a recent extensive evaluation has shown that the move-split merge (MSM) metric is superior to DTW regarding the analytical accuracy of the 1-NN classifier. Unfortunately, the running time of the standard dynamic programming algorithm for MSM distance computation is $\Omega(n^2)$, where $n$ is the length of the longest time series. In this paper, we provide approaches to reducing the cost of MSM distance computations by using lower and upper bounds for early pruning paths in the underlying dynamic programming table. For the case of one time series being a constant, we present a linear-time algorithm. In addition, we propose new linear-time heuristics and adapt heuristics known from DTW to computing the MSM distance. One heuristic employs the metric property of MSM and the previously introduced linear-time algorithm. Our experimental studies demonstrate substantial speed-ups in our approaches compared to previous MSM algorithms. In particular, the running time for MSM is faster than a state-of-the-art DTW distance computation for a majority of the popular UCR data sets.
Comment on"Biological modeling of gold nanoparticle enhanced radiotherapy for proton therapy"by Lin et al. [Phys. Med. Biol. 60 (2015) 4149-4168]

In their article published in Phys. Med. Biol. 60 (2015) 4149-4168, Lin et al studied the radiosensitizing effect of gold nanoparticles (GNPs) using radiation transport simulations and a biological model for the survival of irradiated cells. This comment points out several caveats to the methodlogy used by Lin et al. that may not be evident to readers and may contribute to confusion in the literature about the radiation effects of gold nanoparticles. The two main caveats are the high mass fraction of gold considered and a potential problem with the modified local effect model used to predict cell survival.
Can the Schrödinger dynamics explain measurement?

The motion of a ball through an appropriate lattice of round obstacles models the behavior of a Brownian particle and can be used to describe measurement on a macro system. On another hand, such motion is chaotic and a known conjecture asserts that the Hamiltonian of the corresponding quantum system must follow the random matrix statistics of an appropriate ensemble. We use the Hamiltonian represented by a random matrix in the Gaussian unitary ensemble to study the Schrödinger evolution of non-stationary states. For Gaussian states representing a classical system, the Brownian motion that describes the behavior of the system under measurement is obtained. For general quantum states, the Born rule for the probability of transition between states is derived. It is then shown that the Schrödinger evolution with such a Hamiltonian models measurement on macroscopic and microscopic systems, provides an explanation for the classical behavior of macroscopic bodies and for irreversibility of a measurement, and identifies the boundary between micro and macro worlds.
Saturation of ﬁshbone modes by self-generated zonal ﬂows in tokamak plasmas

Gyrokinetic and kinetic-MHD simulations of n=1 ﬁshbone modes in DIII-D plasmas ﬁnd that self-generated zonal ﬂows can dominate the ﬁshbone saturation. The saturation mechanism is identiﬁed in phase space, where the zonal ﬂows prevent holes and clumps from persisting or drifting in phase space with mode down-chirping, reducing the wave-particle resonant drive. This saturation is conﬁrmed by quantitative agreement with experimental measurements for both mode saturation amplitude and neutron emissivity. Zonal ﬂows shearing rate exceeds the drift-wave growth rate, consistent with the ITB observed in DIII-D plasmas. The deliberate destabilization of ﬁshbones for the development of high performance scenarios in ITER is then proposed.
EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation

We present a novel yet simple deep learning approach, dubbed EPR-Net, for constructing the potential landscape of high-dimensional non-equilibrium steady state (NESS) systems. The key idea of our approach is to utilize the fact that the negative potential gradient is the orthogonal projection of the driving force in a weighted Hilbert space with respect to the steady-state distribution. The constructed loss function also coincides with the entropy production rate (EPR) formula in NESS theory. This approach can be extended to dealing with dimensionality reduction and state-dependent diffusion coefficients in a unified fashion. The robustness and effectiveness of the proposed approach are demonstrated by numerical studies of several high-dimensional biophysical models with multi-stability, limit cycle, or strange attractor with non-vanishing noise.
Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information

A flying bird object detection algorithm based on motion information (FBOD-BMI) is proposed to solve the problem that the features of the object are not obvious in a single frame, and the size of the object is small [low signal-to-noise ratio (SNR)] in surveillance video. First, a ConvLSTM-PAN model structure is designed to capture suspicious flying bird objects, in which the convolutional long and short time memory (ConvLSTM) network aggregates the spatio-temporal features of the flying bird object in adjacent multiframe before the input of the general model [path aggregation network (PAN)], and the PAN model locates the suspicious flying bird objects. Then, an object tracking algorithm is used to track suspicious flying bird objects and calculate their motion range (MR). At the same time, the size of the MR of the suspicious flying bird object is adjusted adaptively according to its speed of movement (specifically, if the bird moves slowly, its MR will be expanded according to the speed of the bird to ensure the environmental information needed to detect the flying bird object). Adaptive spatio-temporal cubes (ASt-Cubes) of the flying bird objects are generated to ensure that the SNR of the flying bird objects is improved, and the necessary environmental information is retained adaptively. Finally, a lightweight U-shape net (LW-USN) based on ASt-Cubes is designed to detect flying bird objects, which rejects the false detections of the suspicious flying bird objects and returns the position of the real flying bird objects. The monitoring video including the flying birds is collected in the unattended traction substation as the experimental dataset to verify the performance of the algorithm. The experimental results show that the flying bird object detection method based on motion information proposed in this article can effectively detect the flying bird object in surveillance video.
Privacy-Protecting Techniques for Behavioral Biometric Data: A Survey

Our behavior (the way we talk, walk, act or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions and health conditions. Hence, techniques to protect individuals privacy against unwanted inferences are required, if such data is planned to be processed. To consolidate knowledge in this area, we systematically review applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. We review anonymization techniques for the behavioral biometric traits of voice, gait, hand motions, eye-gaze, heartbeat (ECG), and brain activity (EEG). Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brain activity) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved.
Detecting Neighborhood Gentrification at Scale via Street-level Visual Data

Neighborhood gentrification plays a significant role in shaping the social and economic well-being of both individuals and communities at large. While some efforts have been made to detect gentrification in cities, existing approaches rely mainly on estimated measures from survey data, require substantial work of human labeling, and are limited in characterizing the neighborhood as a whole. We propose a novel approach to detecting neighborhood gentrification at a large-scale based on the physical appearance of neighborhoods by incorporating historical street-level visual data. We show the effectiveness of the proposed method by comparing results from our approach with gentrification measures from previous literature and case studies. Our approach has the potential to supplement existing indicators of gentrification and become a valid resource for urban researchers and policy makers.
Theories of gravity with nonminimal matter-curvature coupling and the de Sitter swampland conjectures

We discuss, in the context of alternative theories of gravity with nonminimal coupling between matter and curvature, if inflationary solutions driven by a single scalar field can be reconciled with the swampland conjectures about the emergence of de Sitter solutions in string theory. We find that the slow-roll conditions are incompatible with the swampland conjectures for a fairly generic inflationary solution in such alternative theories of gravity.
Recurrent and (strongly) resolvable graphs

We develop a new approach to recurrence and the existence of non-constant harmonic functions on infinite weighted graphs. The approach is based on the capacity of subsets of metric boundaries with respect to intrinsic metrics. The main tool is a connection between polar sets in such boundaries and null sets of paths. This connection relies on suitably diverging functions of finite energy.
On the hierarchical product of graphs and the generalized binomial tree

In this article we follow the study of the hierarchical product of graphs, an operation recently introduced in the context of networks. A well-known example of such a product is the binomial tree which is the (hierarchical) power of the complete graph on two vertices. An appealing property of this structure is that all the eigenvalues are distinct. Here we show how to obtain a graph with this property by applying the hierarchical product. In particular, we propose a generalization of the binomial tree and study some of its main properties.
Limit theorems for iid products of positive matrices

We study stochastic properties of the norm cocycle associated with iid products of positive matrices. We obtain the almost sure invariance principle (ASIP) with rate o ( n 1 /p ) under the optimal condition of a moment or order p > 2 and the Berry-Esseen theorem with rate O (1 / √ n ) under the optimal condition of a moment of order 3. The results are also valid for the matrix norm. For the matrix coeﬃcients, we also have the ASIP but we obtain only partial results for the Berry-Esseen theorem. The proofs make use of coupling coeﬃcients that surprisingly decay exponentially fast to 0 while there is only a polynomial decay in the case of invertible matrices. All the results are actually valid in the context of iid products of matrices leaving invariant a suitable cone.
MetaSecure: A Passwordless Authentication for the Metaverse

Metaverse in general holds a potential future for cyberspace. At the beginning of Web 2.0, it was witnessed that people were signing in with various pseudonyms or 'nyms', risking their online identities by increasing presence of fake accounts leading to difficulty in unique identification for different roles. However, in Web 3.0, the metaverse, a user's identity is tied to their original identity, where risking one poses a significant risk to the other. Therefore, this paper proposes a novel authentication system for securing digital assets, online identity, avatars, and accounts called Metasecure where a unique id for every entity or user to develop a human establishment is essential on a digital platform. The proposed passwordless system provides three layers of security using device attestation, facial recognition and use of physical security keys, security keys, or smartcards in accordance to Fast IDentity Online (FIDO2) specifications. It provides SDKs for authentication on any system including VR/XR glasses, thus ensuring seamlessness in accessing services in the Metaverse.
Defining Logical Systems via Algebraic Constraints on Proofs

We present a comprehensive programme analysing the decomposition of proof systems for non-classical logics into proof systems for other logics, especially classical logic, using an algebra of constraints. That is, one recovers a proof system for a target logic by enriching a proof system for another, typically simpler, logic with an algebra of constraints that act as correctness conditions on the latter to capture the former; e.g. one may use Boolean algebra to give constraints in a sequent calculus for classical propositional logic to produce a sequent calculus for intuitionistic propositional logic. The idea behind such forms of decomposition is to obtain a tool for uniform and modular treatment of proof theory and to provide a bridge between semantics logics and their proof theory. The paper discusses the theoretical background of the project and provides several illustrations of its work in the field of intuitionistic and modal logics: including, a uniform treatment of modular and cut-free proof systems for a large class of propositional logics; a general criterion for a novel approach to soundness and completeness of a logic with respect to a model-theoretic semantics; and a case study deriving a model-theoretic semantics from a proof-theoretic specification of a logic.
Thomsen-type parameters and attenuation coefficients for constant-Q transverse isotropy

Transversely isotropic (TI) media with the frequency-independent quality-factor elements (also called “constant- Q” transverse isotropy) are often used to describe attenuation anisotropy in sedimentary rocks. The attenuation coefficients in constant- Q TI models can be conveniently defined in terms of the Thomsen-type attenuation-anisotropy parameters. Recent research indicates that not all those parameters for such constant- Q media are frequency-independent. Here, we present concise analytic formulas for the Thomsen-type attenuation parameters for Kjartansson’s constant- Q TI model and show that one of them ( δ Q) varies with frequency. The analytic expression for δ Q helps evaluate the frequency dependence of the normalized attenuation coefficients of P- and SV-waves by introducing the newly defined “dispersion factors. ” These factors are frequency-independent and expressed in terms of the Thomsen and Thomsen-type parameters defined at a specified reference frequency. Viscoacoustic constant- Q transverse isotropy is also discussed as a special case, for which the elliptical condition and simplified expressions for the parameters δ Q and δ Q are derived. Our results show that, in the presence of significant absorption, the attenuation coefficients of the constant- Q model vary with frequency for oblique propagation with respect to the symmetry axis. This variation needs to be taken into account when applying the spectral-ratio method and other attenuation-analysis techniques.
Understanding Representation Quality in Self-Supervised Models

Self-supervised learning has shown impressive results in downstream classiﬁcation tasks. However, there is limited work in understanding their failure modes and interpreting their learned representations. In this paper, we study the representation space of six state-of-the-art self-supervised models including SimCLR, SwaV, MoCo, BYOL, DINO and SimSiam. Without the use of class label information, we discover highly activating features that correspond to unique physical attributes in images and exist mostly in correctly-classiﬁed representations. Using these features, we propose Self-Supervised Representation Quality Score (or Q-Score), a model-agnostic, unsupervised score that can reliably predict if a given sample is likely to be mis-classiﬁed during linear evaluation, achieving AUPRC of 91 . 45 on ImageNet-100 and 78 . 78 on ImageNet-1K. Q-Score can also be used as a regularization term on any self-supervised model to remedy low-quality representations through the course of pre-training. We show that pre-training with Q-Score regularization can boost the performance of six state-of-the-art self-supervised models on ImageNet-1K, ImageNet-100, CIFAR-10, CIFAR-100 and STL-10, showing an average relative increase of 1 . 8% top-1 accuracy on linear evaluation. On ImageNet-100, BYOL shows 7 . 2% relative improvement and on ImageNet-1K, SimCLR shows 4 . 7% relative improvement compared to their baselines. Finally, using gradient heatmaps and Salient ImageNet masks, we deﬁne a metric to quantify the interpretability of each representation. We show that highly activating features are strongly correlated to core attributes and enhancing these features through Q-score regularization improves the over-all representation interpretability
Autonomous Drone Racing: A Survey

Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms which can operate drones in complex and uncertain environments. Additionally, flying fast enables drones to cover more ground which in turn increases productivity and further strengthens their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing, where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 kph and 4 g respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of autonomous drone racing across model-based and learning-based approaches. We provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future.