Miracle-dz/UniFAR-FTUs huggingface dataset:
MINT: Multiplier-less Integer Quantization for Spiking Neural Networks

—We propose Multiplier-less INTeger (MINT) quantization, an efﬁcient uniform quantization scheme for the weights and membrane potentials in spiking neural networks (SNNs). Unlike prior SNN quantization works, MINT quantizes the memory-hungry membrane potentials to extremely low bit-width (2-bit) to signiﬁcantly reduce the total memory footprint. Additionally, MINT quantization shares the quantization scale between the weights and membrane potentials, eliminating the need for multipliers and ﬂoating arithmetic units, which are required by the standard uniform quantization. Experimental results demonstrate that our proposed method achieves accuracy that matches other state-of-the-art SNN quantization works while outperforming them on total memory footprint and hardware cost at deployment time. For instance, 2-bit MINT VGG-16 achieves 48.6% accuracy on TinyImageNet (+0.28% from the full-precision baseline) with approximately 93.8% reduction in total memory footprint from the full-precision model; meanwhile, our model reduces area by 93% and dynamic power by 98% compared to other SNN quantization counterparts.
A farewell to particles

We outline the course of affairs in the experimental and theoretical fields of nuclear and particle physics which determined its finale, and give several fragmentary remarks on its present state. The essay tells about events and their participants, known from the literature, but presented here from the perspective of a person whose 50-year labor activity, 1972--2022, proceeded at the All-Union Scientific Research Institute of Experimental Physics, Sarov. In order to predict the fate of particle physics and related astrophysics and cosmology, it is useful to become aware of the facts about another branch of physics that has already gone through its decline $\sim 30$ years ago, the physics of nuclear weapons. These facts are important not in themselves, but as evidence of the growing problems of science and social life, which are not only far from a satisfactory solution, but have not even been the subject of any serious discussion.
Constructing nonorientable genus embedding of complete bipartite graph minus a matching

$G_{m,n,k}$ is a subgraph of the complete bipartite graph $K_{m,n}$ with a $k$-matching removed. By a new method based on the embeddings of some $G_{m,n,k}$ with small $m,n,k$ and bipartite joins with small bipartite graphs, we construct the nonorientable genus embedding of $G_{m,n,k}$ for all $m,n\geq 3$ with $(m,n,k)\neq (5,4,4), (4,5,4),(5,5,5)$. Hence, we solve the cases $G_{n+1,n,n}$($n$ is even) and $G_{n,n,n}$, with the values of $n$ that have not been previously solved, i.e., $n\geq 6$. This completes previous work on the nonorientable genus of $G_{m,n,k}$.
Convergence of commutator of linear integral operators with separable kernel representing monomial covariance type commutation relations in $L_p$

Representations by linear integral operators on $L_p$ spaces over measure spaces are investigated for the polynomial covariance type commutation relations and more general two-sided generalizations of covariance commutation relations extending simultaneously the covariance and the reciprocal covariance type commutation relations. Necessary and sufficient conditions for the defining kernels of the integral operators are obtained for the operators to satisfy such commutation relations associated to pairs of polynomials. Representations by integral operators are studied both for general polynomial covariance type commutation relations and for important classes of polynomial covariance commutation relations associated with arbitrary monomials and affine functions. The convergence of commutators of operators in the sequences of non-commuting operators satisfying the corresponding monomial covariance commutation relation is investigated, and the sequences of such non-commuting operators converging to commuting operators, are presented.
On the Difficulty of Intersection Checking with Polynomial Zonotopes

Polynomial zonotopes, a non-convex set representation, have a wide range of applications from real-time motion planning and control in robotics, to reachability analysis of nonlinear systems and safety shielding in reinforcement learning. Despite this widespread use, a frequently overlooked difficulty associated with polynomial zonotopes is intersection checking. Determining whether the reachable set, represented as a polynomial zonotope, intersects an unsafe set is not straightforward. In fact, we show that this fundamental operation is NP-hard, even for a simple class of polynomial zonotopes. The standard method for intersection checking with polynomial zonotopes is a two-part algorithm that overapproximates a polynomial zonotope with a regular zonotope and then, if the overapproximation error is deemed too large, splits the set and recursively tries again. Beyond the possible need for a large number of splits, we identify two sources of concern related to this algorithm: (1) overapproximating a polynomial zonotope with a zonotope has unbounded error, and (2) after splitting a polynomial zonotope, the overapproximation error can actually increase. Taken together, this implies there may be a possibility that the algorithm does not always terminate.We perform a rigorous analysis of the method and detail necessary conditions for the union of overapproximations to provably converge to the original polynomial zonotope.
Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully Convolutional Networks

Automatic and accurate segmentation of aortic vessel tree (AVT) in computed tomography (CT) scans is crucial for early detection, diagnosis and prognosis of aortic diseases, such as aneurysms, dissections and stenosis. However, this task remains challenges, due to the complexity of aortic vessel tree and amount of CT angiography data. In this technical report, we use two-stage fully convolutional networks (FCNs) to automatically segment AVT in CTA scans from multiple centers. Specifically, we firstly adopt a 3D FCN with U-shape network architecture to segment AVT in order to produce topology attention and accelerate medical image analysis pipeline. And then another one 3D FCN is trained to segment branches of AVT along the pseudo-centerline of AVT. In the 2023 MICCAI Segmentation of the Aorta (SEG.A.) Challenge , the reported method was evaluated on the public dataset of 56 cases. The resulting Dice Similarity Coefficient (DSC) is 0.920, Jaccard Similarity Coefficient (JSC) is 0.861, Recall is 0.922, and Precision is 0.926 on a 5-fold random split of training and validation set.
Statistical Bergman geometry

We introduce an embedding $\Phi: \Omega \rightarrow \mathcal{P}(\Omega)$, where $\Omega \subset \mathbb{C}^n$ is a bounded domain, and $\mathcal{P}(\Omega)$ denotes the space of all probability distributions defined on $\Omega$. We establish that the pullback of the Fisher information metric, the fundamental Riemannian metric in Information geometry, from $\mathcal{P}(\Omega)$ through $\Phi$ is exactly the Bergman metric of $\Omega$. By employing the embedding $\Phi$, we consider $\Omega$ as a statistical manifold in $\mathcal{P}(\Omega)$ and present several results in this framework. Firstly, we express the holomorphic sectional curvature of the Bergman metric in terms of the expectation and provide a reconfirmation of Kobayashi's result that the holomorphic sectional curvature lies within the range $(-\infty, 2]$. Secondly, regarding two bounded domains $\Omega_1$ and $\Omega_2$ as statistical manifolds, we show that if a proper holomorphic map $f: \Omega_1 \rightarrow \Omega_2$ preserves the Fisher information metrics, then $f$ must be a biholomorphism. Finally, we prove the asymptotic normality with the Bergman metric, subject to mild conditions imposed on the Bergman kernels that are satisfied by bounded Hermitian symmetric domains.
A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them In Zero Shot

Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities. They incorporate elements like text, visuals, audio, and storytelling techniques, employing devices like emotions, symbolism, and slogans to convey meaning. While previous research in multi-media understanding has focused mainly on videos with specific actions like cooking, there is a dearth of large annotated training datasets, hindering the development of supervised learning models with satisfactory performance for real-world applications. However, the rise of large language models (LLMs) has witnessed remarkable zero-shot performance in various natural language processing (NLP) tasks, such as emotion classification, question-answering, and topic classification. To bridge this performance gap in multimedia understanding, we propose verbalizing story videos to generate their descriptions in natural language and then performing video-understanding tasks on the generated story as opposed to the original video. Through extensive experiments on five video-understanding tasks, we demonstrate that our method, despite being zero-shot, achieves significantly better results than supervised base-lines for video understanding. Further, alleviating a lack of story understanding benchmarks, we publicly release the first dataset on a crucial task in computational social science, persuasion strategy identification.
Euclid preparation. XXIX. Water ice in spacecraft. I. The physics of ice formation and contamination

Molecular contamination is a well-known problem in space flight. Water is the most common contaminant and alters numerous properties of a cryogenic optical system. Too much ice means that Euclid's calibration requirements and science goals cannot be met. Euclid must then be thermally decontaminated, a long and risky process. We need to understand how iced optics affect the data and when a decontamination is required. This is essential to build adequate calibration and survey plans, yet a comprehensive analysis in the context of an astrophysical space survey has not been done before. In this paper we look at other spacecraft with well-documented outgassing records, and we review the formation of thin ice films. A mix of amorphous and crystalline ices is expected for Euclid. Their surface topography depends on the competing energetic needs of the substrate-water and the water-water interfaces, and is hard to predict with current theories. We illustrate that with scanning-tunnelling and atomic-force microscope images. Industrial tools exist to estimate contamination, and we must understand their uncertainties. We find considerable knowledge errors on the diffusion and sublimation coefficients, limiting the accuracy of these tools. We developed a water transport model to compute contamination rates in Euclid, and find general agreement with industry estimates. Tests of the Euclid flight hardware in space simulators did not pick up contamination signals; our in-flight calibrations observations will be much more sensitive. We must understand the link between the amount of ice on the optics and its effect on Euclid's data. Little research is available about this link, possibly because other spacecraft can decontaminate easily, quenching the need for a deeper understanding. In our second paper we quantify the various effects of iced optics on spectrophotometric data.
Adapting the HHL algorithm to (non-unitary) quantum many-body theory

Rapid progress in developing near- and long-term quantum algorithms for quantum chemistry has provided us with an impetus to move beyond traditional approaches and explore new ways to apply quantum computing to electronic structure calculations. In this work, we identify the connection between quantum many-body theory and a quantum linear solver, and implement the Harrow-Hassidim-Lloyd (HHL) algorithm to make precise predictions of correlation energies for light molecular systems via the (non-unitary) linearised coupled cluster theory. We alter the HHL algorithm to integrate two novel aspects- (a) we prescribe a novel scaling approach that allows one to scale any arbitrary symmetric positive definite matrix A, to solve for Ax = b and achieve x with reasonable precision, all the while without having to compute the eigenvalues of A, and (b) we devise techniques that reduce the depth of the overall circuit. In this context, we introduce the following variants of HHL for different eras of quantum computing- AdaptHHLite in its appropriate forms for noisy intermediate scale quantum (NISQ), late-NISQ, and the early fault-tolerant eras, as well as AdaptHHL for the fault-tolerant quantum computing era. We demonstrate the ability of the NISQ variant of AdaptHHLite to capture correlation energy precisely, while simultaneously being resource-lean, using simulation as well as the 11-qubit IonQ quantum hardware.
Outage Performance and Novel Loss Function for an ML-Assisted Resource Allocation: An Exact Analytical Framework

In this paper, we present Machine Learning (ML) solutions to address the reliability challenges likely to be encountered in advanced wireless systems (5G, 6G, and indeed beyond). Specifically, we introduce a novel loss function to minimize the outage probability of an ML-based resource allocation system. A single-user multi-resource greedy allocation strategy constitutes our application scenario, for which an ML binary classification predictor assists in selecting a resource satisfying the established outage criterium. While other resource allocation policies may be suitable, they are not the focus of our study. Instead, our primary emphasis is on theoretically developing this loss function and leveraging it to train an ML model to address the outage probability challenge. With no access to future channel state information, this predictor foresees each resource’s likely future outage status. When the predictor encounters a resource it believes will be satisfactory, it allocates it to the user. The predictor aims to ensure that a user avoids resources likely to undergo an outage. Our main result establishes exact and asymptotic expressions for this system’s outage probability. These expressions reveal that focusing solely on the optimization of the per-resource outage probability conditioned on the ML predictor recommending resource allocation (a strategy that - at face value - looks to be the most appropriate) may produce inadequate predictors that reject every resource. They also reveal that focusing on standard metrics, like precision, false-positive rate, or recall, may not produce optimal predictors. With our result, we formulate a theoretically optimal, differentiable loss function to train our predictor. We then compare predictors trained using this and traditional loss functions namely, binary cross-entropy (BCE), mean squared error (MSE), and mean absolute error (MAE). In all scenarios, predictors trained using our novel loss function provide superior outage probability performance. Moreover, in some cases, our loss function outperforms predictors trained with BCE, MAE, and MSE by multiple orders of magnitude. Additionally, when applied to another ML-based resource allocation scheme (a modified greedy algorithm), our proposed loss function maintains its efficacy.
Characterizing Long-Tail Categories on Graphs

Long-tail data distributions are prevalent in many real-world networks, including financial transaction networks, e-commerce networks, and collaboration networks. Despite the success of recent developments, the existing works mainly focus on debiasing the machine learning models via graph augmentation or objective reweighting. However, there is limited literature that provides a theoretical tool to characterize the behaviors of long-tail categories on graphs and understand the generalization performance in real scenarios. To bridge this gap, we propose the first generalization bound for long-tail classification on graphs by formulating the problem in the fashion of multi-task learning, i.e., each task corresponds to the prediction of one particular category. Our theoretical results show that the generalization performance of long-tail classification is dominated by the range of losses across all tasks and the total number of tasks. Building upon the theoretical findings, we propose a novel generic framework Tail2Learn to improve the performance of long-tail categories on graphs. In particular, we start with a hierarchical task grouping module that allows label-limited classes to benefit from the relevant information shared by other classes; then, we further design a balanced contrastive learning module to balance the gradient contributions of head and tail classes. Finally, extensive experiments on various real-world datasets demonstrate the effectiveness of Tail2Learn in capturing long-tail categories on graphs.
Cognitive non-linear controller design for magnetic levitation system

This paper proposes a new development of a position-tracking control algorithm for a non-linear magnetic ball levitation system by utilizing the back-stepping technique based on the cognitive online auto-tune algorithm. The aim of the proposed cognitive robust non-linear controller is to find the optimal voltage or current control action for the real magnetic ball levitation model during the tracking of pre-defined position of the steel ball precisely and quickly. The cognitive methodology guided by the Lyapunov stability criterion is implemented as a stable and robust online auto-tune algorithm to find optimal parameters for the proposed controller. Matlab simulation results and laboratory work confirm the validity and the robustness of the proposed controller algorithm in terms of overcoming the noise signal and disturbances, minimizing the tracking error, and obtaining an optimal and smooth control signal, with the minimum number of fitness evaluations.
Review of spatial disability in individuals with attention deficit-hyperactivity disorder: Toward spatial cognition theory

Spatial cognition is the ability to detect, process, integrate, and formulate the spatial aspects of the environment. Spatial abilities, as perceptual doorway of information processing, influence on higher cognitive functions. This systematic review aimed to explore impaired spatial ability in individuals with Attention Deficit-Hyperactivity Disorders (ADHD). The data from 18 empirical experiments that explored at least one factor of spatial ability in individuals with ADHD was collected in accordance with the PRISMA approach. This study discussed several determinants of impaired spatial ability, including factors, domains, tasks, and measures of spatial ability. Furthermore, the impact of age, gender, and comorbidities are discussed. Finally, a model was proposed to explain the impaired cognitive functions in children with ADHD based on spatial abilities.
Large-scale surface hopping simulation of charge transport in hexagonal molecular crystals: role of electronic coupling signs

In large-scale surface hopping simulations with a huge number of electronic states, trivial crossings could easily lead to incorrect long-range charge transfer and induce large numerical errors. We here study the charge transport in two-dimensional hexagonal molecular crystals with a parameter-free full crossing corrected global flux surface hopping method. Fast time-step size convergence and system size independence have been realized in large systems containing thousands of molecular sites. In hexagonal systems, each molecular site has six nearest neighbours. We find that the signs of their electronic couplings have a strong impact on the charge mobility and delocalization strength. In particular, changing the signs of electronic couplings can even lead to a transition from hopping to band-like transport. In comparison, such phenomena cannot be observed in extensively studied two-dimensional square systems. This is attributed to symmetry of the electronic Hamiltonian and distribution of the energy levels. Due to its high performance, the proposed approach is promising to be applied to more realistic and complex systems for molecular design.
The Great Opportunity for Workforce Health

The terms “Quiet Quitting” and the “Great Resignation” became popular in 2021 to describe the increase in workers disengaged from, and voluntarily leaving their jobs during the pandemic. Anecdotal reports of employee disengagement were validated by increased quit rates calculated by the U.S. Bureau of LaborMarket Statistics beyond what would be expected by economic factors. These trends sound an alarm about the health of the U.S. workforce. Surveys have found that workers are dissatisfied with how they are treated, stressed from work-life imbalance and suffering burn out. Improving worker health means more than just ensuring healthcare benefits and sick leave – although these are important. To ensure a healthy (and thus productive) workforce, we must acknowledge that there is a problem and that the status quo is not an option. Pandemicrelated disruptions paired with the high cost of disengagement and turnover offer employers both an opportunity and a motivation to act. In this edition of Knowing Well Being Well, contributors offer perspectives on the workforce issues that have been uncovered by the Great Resignation and on the changes needed so workers can thrive. In this overview, I describe how and why the pandemic may have spurred these trends, and their potential consequences for workers’ health and wellbeing.
Pirtobrutinib in Covalent Bruton Tyrosine Kinase Inhibitor Pretreated Mantle-Cell Lymphoma

PURPOSE Pirtobrutinib is a highly selective, noncovalent (reversible) Bruton tyrosine kinase inhibitor (BTKi). We report the safety and efficacy of pirtobrutinib in patients with covalent Bruton tyrosine kinase inhibitor (cBTKi) pretreated mantle-cell lymphoma (MCL), a population with poor prognosis. METHODS Patients with cBTKi pretreated relapsed/refractory (R/R) MCL received pirtobrutinib monotherapy in a multicenter phase I/II trial (BRUIN; ClinicalTrials.gov identifier: NCT03740529). Efficacy was assessed in the first 90 consecutively enrolled patients who met criteria for inclusion in the primary efficacy cohort. The primary end point was overall response rate (ORR). Secondary end points included duration of response (DOR) and safety. RESULTS The median patient age was 70 years (range, 46-87), the median prior lines of therapy was 3 (range, 1-8), 82.2% had discontinued a prior cBTKi because of disease progression, and 77.8% had intermediate- or high-risk simplified MCL International Prognostic Index score. The ORR was 57.8% (95% CI, 46.9 to 68.1), including 20.0% complete responses (n = 18). At a median follow-up of 12 months, the median DOR was 21.6 months (95% CI, 7.5 to not reached). The 6- and 12-month estimated DOR rates were 73.6% and 57.1%, respectively. In the MCL safety cohort (n = 164), the most common treatment-emergent adverse events (TEAEs) were fatigue (29.9%), diarrhea (21.3%), and dyspnea (16.5%). Grade ≥3 TEAEs of hemorrhage (3.7%) and atrial fibrillation/flutter (1.2%) were less common. Only 3% of patients discontinued pirtobrutinib because of a treatment-related adverse event. CONCLUSION Pirtobrutinib is a first-in-class novel noncovalent (reversible) BTKi and the first BTKi of any kind to demonstrate durable efficacy after prior cBTKi therapy in heavily pretreated R/R MCL. Pirtobrutinib was well tolerated with low rates of treatment discontinuation because of toxicity.