Miracle-dz/UniFAR-FTUs huggingface dataset:
PatchNR: learning from very few images by patch normalizing flow regularization

Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a powerful regularizer for the variational modeling of inverse problems in imaging. Our regularizer, called patch normalizing flow regularizer (patchNR), involves a normalizing flow learned on small patches of very few images. In particular, the training is independent of the considered inverse problem such that the same regularizer can be applied for different forward operators acting on the same class of images. By investigating the distribution of patches versus those of the whole image class, we prove that our model is indeed a maximum a posteriori approach. Numerical examples for low-dose and limited-angle computed tomography (CT) as well as superresolution of material images demonstrate that our method provides very high quality results. The training set consists of just six images for CT and one image for superresolution. Finally, we combine our patchNR with ideas from internal learning for performing superresolution of natural images directly from the low-resolution observation without knowledge of any high-resolution image.
Wave propagation, bidirectional transparency, and coherent perfect absorption-lasing in finite periodic PT-symmetric waveguide networks

We theoretically and numerically investigate the scattering behavior of a periodic parity-time (PT)-symmetric waveguide network composed of a finite number of unit cells. Specifically, we put forward rigorous and formally exact expressions for wave propagation, bi-directional reflectionless, and coherent perfect absorption and lasing (CPAL) occuring in a finite periodic optical waveguide network. Through the use of the generalized parametric space derived from observation of PT-symmetric transfer matrix, Lorentz reciprocity theorem and non-imaginary Bloch phase, we observe that when the unit cell is operated at the PT broken phase or exceptional point, the system can always have propagating modes, independent of the number and transmission phase of the unit cell. On the other hand, when the unit cell is operated at the exact PT-symmetric phase, the formation of propagating waves would depend on the transmission phase of the unit cell. More interestingly, we find that even though the unit cell is not operated at the exceptional point, reflectionless with bi-directionality as well as unity transmittance can be achieved by choosing appropriate number of unite cells and specific PT phases. We also find two approaches to implement CPAL. One is to exploit odd number of the unit cell operated at the CPAL point. Another way is to manipulate specific broken phase with an appropriate number of the unit cells, while making transmission phase to be null. We believe this work may offer a theoretical underpinnings for studying extraordinary wave phenomena of PT-symmetric photonics and may open avenues for manipulation of light.
High intake of dietary fructose in overweight/obese teenagers associated with depletion of Eubacterium and Streptococcus in gut microbiome

ABSTRACT Background: A western high fat, high carbohydrate diet has been shown to be associated with decreased gut bacterial diversity and reductions in beneficial bacteria. This gut bacteria dysbiosis could develop in early life and contribute to chronic disease risk such as obesity, type 2 diabetes and non-alcoholic fatty liver disease. Objective: To determine how dietary macronutrients are associated with the relative abundance of gut bacteria in healthy adolescents. Methods: Fifty-two obese participants (12–19 years) from two studies, many who were primarily of Hispanic background, provided fecal samples for 16S rRNA gene sequencing. Dietary macronutrients were assessed using 24-hour diet recalls and body composition was assessed using DEXA. General regression models assuming a negative binomial distribution were used to examine the associations between gut bacteria and dietary fiber, saturated fat, unsaturated fats, protein, added sugar, total sugar and free fructose after adjusting for age, gender, race/ethnicity, body fat percentage, study and caloric intake. Results: The genera Eubacterium (Benjamini-Hochberg (BH) corrected p-value = 0.10) and Streptococcus (BH corrected p-value = 0.04) were inversely associated with dietary fructose intake. There were no other significant associations between abundances of gut microbes and other dietary macronutrients, including fiber, fat, protein, total sugar or added sugar. Conclusions: High dietary fructose was associated with lower abundance of the beneficial microbes Eubacterium and Streptococcus, which are involved with carbohydrate metabolism.
Non-verbal Facial Action Units-Based Automatic Depression Classification

Depression is a common mental disorder that causes people to experience depressed mood, loss of interest or pleasure, feelings of guilt or low self-worth. Traditional clinical depression diagnosis methods are subjective and time consuming. Since depression can be reflected by human facial expressions, We propose a non-verbal facial behavior-based automatic depression classification approach. In this paper, both short-term behavior-based and clip-based depression classification are constructed. The final clip-level decision of short-term behavior-based depression detection is yielded by averaging the predictions of all short-term behaviors while we modelling behaviors contained in all frames based on two Gaussian Mixture Models. To evaluate the proposed approaches, we select a gender balanced subset from AVEC 2019 depression corpus containing 30 participants. The experimental results show that our method achieved more than 75& depression classification accuracy, where both GMM-based clip-level depression modelling and rank pooling-based short-term depression behavior modelling achieved at least 70& classification accuracy. The result indicates that our approach can leverage complementary information from both systems to achieve promising depression predictions from facial behaviors.
$(p,q,t)$-Catalan continued fractions, gamma expansions and pattern avoidances

We introduce a kind of $(p, q, t)$-Catalan numbers of Type A by generalizing the Jacobian type continued fraction formula, we proved that the corresponding expansions could be expressed by the polynomials counting permutations on $\S_n(321)$ by various descent statistics. Moreover, we introduce a kind of $(p, q, t)$-Catalan numbers of Type B by generalizing the Jacobian type continued fraction formula, we proved that the Taylor coefficients and their $\gamma$-coefficients could be expressed by the polynomials counting permutations on $\S_n(3124, 4123, 3142, 4132)$ by various descent statistics. Our methods include permutation enumeration techniques involving variations of bijections from permutation patterns to labeled Motzkin paths and modified Foata-Strehl action.
ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation

The domain discrepancy existed between medical images acquired in different situations renders a major hurdle in deploying pre-trained medical image segmentation models for clinical use. Since it is less possible to distribute training data with the pre-trained model due to the huge data size and privacy concern, source-free unsupervised domain adaptation (SFDA) has recently been increasingly studied based on either pseudo labels or prior knowledge. However, the image features and probability maps used by pseudo label-based SFDA and the consistent prior assumption and the prior prediction network used by prior-guided SFDA may become less reliable when the domain discrepancy is large. In this paper, we propose a \textbf{Pro}mpt learning based \textbf{SFDA} (\textbf{ProSFDA}) method for medical image segmentation, which aims to improve the quality of domain adaption by minimizing explicitly the domain discrepancy. Specifically, in the prompt learning stage, we estimate source-domain images via adding a domain-aware prompt to target-domain images, then optimize the prompt via minimizing the statistic alignment loss, and thereby prompt the source model to generate reliable predictions on (altered) target-domain images. In the feature alignment stage, we also align the features of target-domain images and their styles-augmented counterparts to optimize the source model, and hence push the model to extract compact features. We evaluate our ProSFDA on two multi-domain medical image segmentation benchmarks. Our results indicate that the proposed ProSFDA outperforms substantially other SFDA methods and is even comparable to UDA methods. Code will be available at \url{https://github.com/ShishuaiHu/ProSFDA}.
Reconstructing quantum geometry from quantum information: spin networks as harmonic oscillators

Loop quantum gravity defines the quantum states of space geometry as spin networks and describes their evolution in time. We reformulate spin networks in terms of harmonic oscillators and show how the holographic degrees of freedom of the theory are described as matrix models. This allows us to make a link with non-commutative geometry and to look at the issue of the semi-classical limit of loop quantum gravity from a new perspective. This work is thought of as part of a bigger project of describing quantum geometry in quantum information terms.
Sharpness-Aware Training for Accurate Inference on Noisy DNN Accelerators

Energy-efﬁcient deep neural network (DNN) accelerators are prone to non-idealities that degrade DNN performance at inference time. To mitigate such degradation, existing methods typically add perturbations to the DNN weights during training to simulate inference on noisy hardware. However, this often requires knowledge about the target hardware and leads to a trade-off between DNN performance and robustness, decreasing the former to increase the latter. In this work, we show that applying sharpness-aware training by optimizing for both the loss value and the loss sharpness signiﬁcantly improves robustness to noisy hardware at inference time while also increasing DNN performance. We further motivate our results by showing a high correlation between loss sharpness and model robustness. We show superior performance compared to injecting noise during training and aggressive weight clipping on multiple architectures, optimizers, datasets, and training regimes without relying on any assumptions about the target hardware. This is observed on a generic noise model as well as on accurate noise simulations from real hardware.
BrainTTA: A 35 fJ/op Compiler Programmable Mixed-Precision Transport-Triggered NN SoC

Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widths as low as 1-bit have gained popularity due to their ability to largely cut down energy cost per inference. In this paper, a flexible SoC with mixed-precision support is presented. Contrary to the current trend of fixed-datapath accelerators, this architecture makes use of a flexible datapath based on a Transport-Triggered Architecture (TTA). The architecture is fully programmable using C. The accelerator has a peak energy efficiency of 35/67/405 fJ/op (binary, ternary, and 8-bit precision) and a throughput of 614/307/77 GOPS, which is unprecedented for a programmable architecture.
High energy constraints on Lorentz symmetry violations

Lorentz violation at high energies might lead to non linear dispersion relations for the fundamental particles. We analyze observational constraints on these without assuming any a priori equality between the coeﬃcients determining the amount of Lorentz violation for diﬀerent particle species. We focus on constraints from three high energy processes involving photons and electrons: photon decay, photo- production of electron-positron pairs, and vacuum ˇCerenkov radiation. We ﬁnd that cubic momentum terms in the dispersion relations are strongly constrained. Talk presented by S. Liberati at CPT01; the Second Meeting on CPT and Lorentz Symmetry, Bloomington, Indiana, 15-18 Aug. 2001.
A Two Body Benchmark Problem for Transient and Ultrawideband Scattering

In this paper we propose a novel benchmark for numerical solutions of transient and ultrawideband scattering from perfectly conducting targets. The target for the problem comprises two open concentric spherical shells with a common axis of symmetry, which represents the direction of propagation of the incident field. This geometry includes a variety of physical features including surfaces, edges, cavities and an inclusion within a cavity. It provides a range of scattering mechanisms, including multiple body interactions and cavity resonance effects, the relative strength of which can be varied via the parameters of the problem. The time harmonic scattering problem is solved via the method of regularization providing a semi-analytic solution of arbitrary predetermined accuracy with good stability and convergence properties. Solution of this time harmonic problem provides a benchmark for both time domain and frequency domain numerical solutions of Maxwell's equations. An example is given of this benchmark problem, as applied to a time domain integral equation method.
Explainable Model-specific Algorithm Selection for Multi-Label Classification

Multi-label classification (MLC) is an ML task of predictive modeling in which a data instance can simultaneously belong to multiple classes. MLC is increasingly gaining interest in different application domains such as text mining, computer vision, and bioinformatics. Several MLC algorithms have been proposed in the literature, resulting in a meta-optimization problem that the user needs to address: which MLC approach to select for a given dataset? To address this algorithm selection problem, we investigate in this work the quality of an automated approach that uses characteristics of the datasets - so-called features - and a trained algorithm selector to choose which algorithm to apply for a given task. For our empirical evaluation, we use a portfolio of 38 datasets. We consider eight MLC algorithms, whose quality we evaluate using six different performance metrics. We show that our automated algorithm selector outperforms any of the single MLC algorithms, and this is for all evaluated performance measures. Our selection approach is explainable, a characteristic that we exploit to investigate which meta-features have the largest influence on the decisions made by the algorithm selector. Finally, we also quantify the importance of the most significant meta-features for various domains.
Adversarial Cheap Talk

Adversarial attacks in reinforcement learning (RL) often assume highly-privileged access to the victim's parameters, environment, or data. Instead, this paper proposes a novel adversarial setting called a Cheap Talk MDP in which an Adversary can merely append deterministic messages to the Victim's observation, resulting in a minimal range of influence. The Adversary cannot occlude ground truth, influence underlying environment dynamics or reward signals, introduce non-stationarity, add stochasticity, see the Victim's actions, or access their parameters. Additionally, we present a simple meta-learning algorithm called Adversarial Cheap Talk (ACT) to train Adversaries in this setting. We demonstrate that an Adversary trained with ACT still significantly influences the Victim's training and testing performance, despite the highly constrained setting. Affecting train-time performance reveals a new attack vector and provides insight into the success and failure modes of existing RL algorithms. More specifically, we show that an ACT Adversary is capable of harming performance by interfering with the learner's function approximation, or instead helping the Victim's performance by outputting useful features. Finally, we show that an ACT Adversary can manipulate messages during train-time to directly and arbitrarily control the Victim at test-time. Project video and code are available at https://sites.google. com/view/adversarial-cheap-talk
Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy

Diabetic Retinopathy (DR) is a severe complication of diabetes that can cause blindness. Although effective treatments exist (notably laser) to slow the progression of the disease and prevent blindness, the best treatment remains prevention through regular check-ups (at least once a year) with an ophthalmologist. Optical Coherence Tomography Angiography (OCTA) allows for the visualization of the retinal vascularization, and the choroid at the microvascular level in great detail. This allows doctors to diagnose DR with more precision. In recent years, algorithms for DR diagnosis have emerged along with the development of deep learning and the improvement of computer hardware. However, these usually focus on retina photography. There are no current methods that can automatically analyze DR using Ultra-Wide OCTA (UW-OCTA). The Diabetic Retinopathy Analysis Challenge 2022 (DRAC22) provides a standardized UW-OCTA dataset to train and test the effectiveness of various algorithms on three tasks: lesions segmentation, quality assessment, and DR grading. In this paper, we will present our solutions for the three tasks of the DRAC22 challenge. The obtained results are promising and have allowed us to position ourselves in the TOP 5 of the segmentation task, the TOP 4 of the quality assessment task, and the TOP 3 of the DR grading task. The code is available at \url{https://github.com/Mostafa-EHD/Diabetic_Retinopathy_OCTA}.
BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect

A method for estimating the conditional average treatment effect under condition of censored time-to-event data called BENK (the Beran Estimator with Neural Kernels) is proposed. The main idea behind the method is to apply the Beran estimator for estimating the survival functions of controls and treatments. Instead of typical kernel functions in the Beran estimator, it is proposed to implement kernels in the form of neural networks of a specific form called the neural kernels. The conditional average treatment effect is estimated by using the survival functions as outcomes of the control and treatment neural networks which consists of a set of neural kernels with shared parameters. The neural kernels are more flexible and can accurately model a complex location structure of feature vectors. Various numerical simulation experiments illustrate BENK and compare it with the well-known T-learner, S-learner and X-learner for several types of the control and treatment outcome functions based on the Cox models, the random survival forest and the Nadaraya-Watson regression with Gaussian kernels. The code of proposed algorithms implementing BENK is available in https://github.com/Stasychbr/BENK.
Semantic segmentation for fully automated macrofouling analysis on coatings after field exposure

Abstract Biofouling is a major challenge for sustainable shipping, filter membranes, heat exchangers, and medical devices. The development of fouling-resistant coatings requires the evaluation of their effectiveness. Such an evaluation is usually based on the assessment of fouling progression after different exposure times to the target medium (e.g. salt water). The manual assessment of macrofouling requires expert knowledge about local fouling communities due to high variances in phenotypical appearance, has single-image sampling inaccuracies for certain species, and lacks spatial information. Here an approach for automatic image-based macrofouling analysis was presented. A dataset with dense labels prepared from field panel images was made and a convolutional network (adapted U-Net) for the semantic segmentation of different macrofouling classes was proposed. The establishment of macrofouling localization allows for the generation of a successional model which enables the determination of direct surface attachment and in-depth epibiotic studies. Graphical Abstract
Economics-based optimization of unstable flows

As an example for the optimization of unstable flows, we present an economics-based method for deciding the optimal rates at which vehicles are allowed to enter a highway. It exploits the naturally occurring fluctuations of traffic flow and is flexible enough to adapt in real time to the transient flow characteristics of road traffic. Simulations based on realistic parameter values show that this strategy is feasible for naturally occurring traffic, and that even far from optimality, injection policies can improve traffic flow. Moreover, the same method can be applied to the optimization of flows of gases and granular media.
Delay-Aware Backpressure Routing Using Graph Neural Networks

We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Classical BP routing provides a simple yet powerful distributed solution for resource allocation in wireless multi-hop networks but has poor delay performance. A low-cost approach to improve this delay performance is to favor shorter paths by incorporating pre-defined biases in the BP computation, such as a bias based on the shortest path (hop) distance to the destination. In this work, we improve upon the widely-used metric of hop distance (and its variants) for the shortest path bias by introducing a bias based on the link duty cycle, which we predict using a graph convolutional neural network. Numerical results show that our approach can improve the delay performance compared to classical BP and existing BP alternatives based on pre-defined bias while being adaptive to interference density. In terms of complexity, our distributed implementation only introduces a one-time overhead (linear in the number of devices in the network) compared to classical BP, and a constant overhead compared to the lowest-complexity existing bias-based BP algorithms.
A Survey of Scheduling in Time-Sensitive Networking (TSN)

—Time-Sensitive Networking (TSN) is an enhancement of Ethernet which provides various mechanisms for real-time communication. Time-triggered (TT) trafﬁc represents periodic data streams with strict real-time requirements. Amongst others, TSN supports scheduled transmission of TT streams, i.e., the transmission of their packets by edge nodes is coordinated in such a way that none or very little queuing delay occurs in intermediate nodes. TSN supports multiple priority queues per egress port. The Time-Aware Shaper (TAS) uses so-called gates to explicitly allow and block these queues for transmission on a short periodic timescale. The TAS is utilized to protect scheduled trafﬁc from other trafﬁc to minimize its queuing delay. In this work, we consider scheduling in TSN which comprises the computation of periodic transmission instants at edge nodes and the periodic opening and closing of queue gates. In this paper, we ﬁrst give a brief overview of TSN features and standards. We state the TSN scheduling problem and explain common extensions which also include optimization problems. We review scheduling and optimization methods that have been used in this context. Then, the contribution of currently available research work is surveyed. We extract and compile optimization objectives, solved problem instances, and evaluation results. Research domains are identiﬁed, and speciﬁc contributions are analyzed. Finally, we discuss potential research directions and open problems.