Miracle-dz/UniFAR-FTUs huggingface dataset:
Mirror Learning: A Unifying Framework of Policy Optimisation

Modern deep reinforcement learning (RL) algorithms are motivated by either the generalised policy iteration (GPI) or trust-region learning (TRL) frameworks. However, algorithms that strictly respect these theoretical frameworks have proven unscalable. Surprisingly, the only known scalable algorithms violate the GPI/TRL assumptions, e.g. due to required regularisation or other heuristics. The current explanation of their empirical success is essentially"by analogy": they are deemed approximate adaptations of theoretically sound methods. Unfortunately, studies have shown that in practice these algorithms differ greatly from their conceptual ancestors. In contrast, in this paper we introduce a novel theoretical framework, named Mirror Learning, which provides theoretical guarantees to a large class of algorithms, including TRPO and PPO. While the latter two exploit the flexibility of our framework, GPI and TRL fit in merely as pathologically restrictive corner cases thereof. This suggests that the empirical performance of state-of-the-art methods is a direct consequence of their theoretical properties, rather than of aforementioned approximate analogies. Mirror learning sets us free to boldly explore novel, theoretically sound RL algorithms, a thus far uncharted wonderland.
3D Intracranial Aneurysm Classification and Segmentation via Unsupervised Dual-Branch Learning

Intracranial aneurysms are common nowadays and how to detect them intelligently is of great significance in digital health. Whereas most existing deep learning research focused on medical images in a supervised way, we introduce an unsupervised method for the detection of intracranial aneurysms based on 3D point cloud data. In particular, our method consists of two stages: unsupervised pre-training and downstream tasks. As for the former, the main idea is to pair each point cloud with its jittering counterpart and maximise their correspondence. Then we design a dual-branch contrastive network with an encoder for each branch and a subsequent common projection head. As for the latter, we design simple networks for supervised classification and segmentation training. Experiments on the public dataset (IntrA) show that our unsupervised method achieves comparable or even better performance than some state-of-the-art supervised techniques, and it is most prominent in the detection of aneurysmal vessels. Experiments on the ModelNet-40 also show that our method achieves the accuracy of 90.79% which outperforms existing state-of-the-art unsupervised models.
Investigating Expectation Violations in Mobile Apps

Information technology and software services are pervasive, occupying the centre of most aspects of contemporary societies. This has given rise to commonly expected norms and expectations around how such systems should work, appropriate penalties for violating these expectations, and more importantly, indicators of how to reduce the consequences of violations and sanctions. Evidence for expectation violations and ensuing sanctions exists in a range of portals used by individuals and groups to start new friendships, explore new ideas, and provide feedback for products and services. Therein lies insights that could lead to functional socio-technical systems, and general awareness and anticipations of human actions (and interactions) when using information technology and software services. However, limited previous work has examined such artifacts to provide these understandings. To contribute to such understandings and theoretical advancement we study expectation violations in mobile apps, considered among the most engaging socio-technical systems. We used content analysis and expectancy violation theory (EVT) and expectation confirmation theory (ECT) to explore the evidence and nature of sanctions in app reviews for a specific domain of apps. Our outcomes show that users respond to expectation violation with sanctions when their app does not work as anticipated, developers seem to target specific market niches when providing services in an app domain, and users within an app domain respond with similar sanctions. We contribute to the advancement of expectation violation theories, and we provide practical insights for the mobile app community.
Bregman Divergence Based Em Algorithm and its Application to Classical and Quantum Rate Distortion Theory

We formulate em algorithm in the framework of Bregman divergence, which is a general problem setting of information geometry. That is, we address the minimization problem of the Bregman divergence between an exponential subfamily and a mixture subfamily in a Bregman divergence system. Then, we show the convergence and its speed under several conditions. We apply this algorithm to rate distortion and its variants including the quantum setting, and show the usefulness of our general algorithm. In fact, existing applications of Arimoto-Blahut algorithm to rate distortion theory make the optimization of the weighted sum of the mutual information and the cost function by using the Lagrange multiplier. However, in rate distortion theory, it is needed to minimize the mutual information under the constant constraint for the cost function. Our algorithm directly solves this minimization. In addition, we have numerically checked the convergence speed of our algorithm in the classical case of rate distortion problem.
Modeling the {\gamma}-ray Pulsar Wind Nebulae population in our Galaxy

Pulsar wind nebulae (PWNe) represent the largest class of sources that upcoming {\gamma}-ray surveys will detect. Therefore, accurate modelling of their global emission properties is one of the most urgent problems in high-energy astrophysics. Correctly characterizing these dominant objects is a needed step to allow {\gamma}-ray surveys to detect fainter sources, investigate the signatures of cosmic-ray propagation and estimate the diffuse emission in the Galaxy. In this paper we present an observationally motivated construction of the Galactic PWNe population. We made use of a modified one-zone model to evolve for a long period of time the entire population. The model provides, for every source, at any age, a simplified description of the dynamical and spectral evolution. The long term effects of the reverberation phase on the spectral evolution are described, for the first time, based on physically motivated prescriptions for the evolution of the nebular radius supported by numerical studies. This effort tries to solve one of the most critical aspects of one-zone modeling, namely the typical overcompression of the nebula during the reverberation phase, resulting in a strong modification of its spectral properties at all frequencies. We compare the emission properties of our synthetic Pulsar Wind Nebulae population with the most updated catalogues of TeV Galactic sources. We find that the firmly identified and candidate PWNe sum up to about 50% of the expected objects in this class above threshold for detection. Finally, we estimate that CTA will increase the number of TeV detected PWNe by a factor$\geq3$.
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs

We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data. Taking insights from self-supervised learning, we randomly mask a large proportion of edges and try to reconstruct these missing edges during training. MGAE has two core designs. First, we find that masking a high ratio of the input graph structure, e.g., $70\%$, yields a nontrivial and meaningful self-supervisory task that benefits downstream applications. Second, we employ a graph neural network (GNN) as an encoder to perform message propagation on the partially-masked graph. To reconstruct the large number of masked edges, a tailored cross-correlation decoder is proposed. It could capture the cross-correlation between the head and tail nodes of anchor edge in multi-granularity. Coupling these two designs enables MGAE to be trained efficiently and effectively. Extensive experiments on multiple open datasets (Planetoid and OGB benchmarks) demonstrate that MGAE generally performs better than state-of-the-art unsupervised learning competitors on link prediction and node classification.
Residual stresses and shear-induced overaging in boehmite gels

Colloidal gels respond like soft solids at rest, whereas they flow like liquids under external shear. Starting from a fluidized state under an applied shear rate $\dot\gamma_{p}$, abrupt flow cessation triggers a liquid-to-solid transition during which the stress relaxes towards a so-called \textit{residual stress} $\sigma_{\rm res}$ that tallies a macroscopic signature of previous shear history. Here, we report on the liquid-to-solid transition in gels of boehmite, an aluminum oxide, that shows a remarkable non-monotonic stress relaxation towards a residual stress $\sigma_{\rm res}(\dot\gamma_{p})$ characterized by a dual behavior relative to a critical value $\dot\gamma_{c}$ of the shear rate $\dot\gamma_{p}$. Following shear at $\dot\gamma_{p}>\dot\gamma_{c}$, the gel obtained upon flow cessation is insensitive to shear history, and the residual stress is negligible. However, for $\dot\gamma_{p}<\dot\gamma_{c}$, the gel encodes some memory of the shear history, and $\sigma_{\rm res}$ increases for decreasing shear rate, directly contributing to reinforcing the gel viscoelastic properties. Moreover, we show that both $\sigma_{\rm res}$ and the gel viscoelastic properties increase logarithmically with the strain accumulated during the shear period preceding flow cessation. Such a shear-induced"overaging"phenomenon bears great potential for tuning the rheological properties of colloidal gels.
Geometric thermodynamic uncertainty relation in a periodically driven thermoelectric heat engine

Thermodynamic uncertainty relation, quantifying a trade-off among average current, the associated fluctuation (precision), and entropy production (cost), has been formulated in nonequilibrium steady state and various stochastic systems. Herein, we study the thermodynamic uncertainty relation in generic thermoelectric heat engines under a periodic control protocol, by uncovering the underlying Berry-phase-like contribution. We show that our thermodynamic uncertainty relation breaks the seminal steady-state results, originating from the non-vanishing geometric effect. Furthermore, by deriving the consequent trade-off relation binding efficiency, power, and constancy, we prove that the periodically driven thermoelectric heat engines can generally outperform the steady-state analogies. The general bounds are illustrated by an analytically solvable two-terminal single quantum dot heat engine under the periodic modulation. Our work provides a geometric framework in bounding and optimizing a wide range of periodically driven thermoelectric thermal machines.
Local and Global Convergence of General Burer-Monteiro Tensor Optimizations

Tensor optimization is crucial to massive machine learning and signal processing tasks. In this paper, we consider tensor optimization with a convex and well-conditioned objective function and reformulate it into a nonconvex optimization using the Burer-Monteiro type parameterization. We analyze the local convergence of applying vanilla gradient descent to the factored formulation and establish a local regularity condition under mild assumptions. We also provide a linear convergence analysis of the gradient descent algorithm started in a neighborhood of the true tensor factors. Complementary to the local analysis, this work also characterizes the global geometry of the best rank-one tensor approximation problem and demonstrates that for orthogonally decomposable tensors the problem has no spurious local minima and all saddle points are strict except for the one at zero which is a third-order saddle point.
Long time and large crowd dynamics of discrete Cucker-Smale alignment models

We provide a bird's eye view on developments in analyzing the long time, large crowd behavior of Cucker-Smale alignment dynamics. We consider a class of (fully-)discrete models, paying particular attention to general alignment protocols in which agents, with possibly time-dependent masses, are driven by a large class of heavy-tailed communication kernels. The presence of time-dependent masses allows, in particular, non-symmetric communication. While revisiting known results in the literature, we also shed new light of various aspects on the long time flocking/swarming behavior, driven by the decay of energy fluctuations and heavy-tailed connectivity. We also discuss the large crowd dynamics in terms of the hydrodynamic description of Euler alignment models.
Uncertainty-Aware Cascaded Dilation Filtering for High-Efficiency Deraining

Deraining is a significant and fundamental computer vision task, aiming to remove the rain streaks and accumulations in an image or video captured under a rainy day. Existing deraining methods usually make heuristic assumptions of the rain model, which compels them to employ complex optimization or iterative refinement for high recovery quality. This, however, leads to time-consuming methods and affects the effectiveness for addressing rain patterns deviated from from the assumptions. In this paper, we propose a simple yet efficient deraining method by formulating deraining as a predictive filtering problem without complex rain model assumptions. Specifically, we identify spatially-variant predictive filtering (SPFilt) that adaptively predicts proper kernels via a deep network to filter different individual pixels. Since the filtering can be implemented via well-accelerated convolution, our method can be significantly efficient. We further propose the EfDeRain+ that contains three main contributions to address residual rain traces, multi-scale, and diverse rain patterns without harming the efficiency. First, we propose the uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify the difficulties of reconstructing clean pixels via predicted kernels and remove the residual rain traces effectively. Second, we design the weight-sharing multi-scale dilated filtering (WS-MS-DFilt) to handle multi-scale rain streaks without harming the efficiency. Third, to eliminate the gap across diverse rain patterns, we propose a novel data augmentation method (i.e., RainMix) to train our deep models. By combining all contributions with sophisticated analysis on different variants, our final method outperforms baseline methods on four single-image deraining datasets and one video deraining dataset in terms of both recovery quality and speed.
Sharing pain: Using pain domain transfer for video recognition of low grade orthopedic pain in horses

Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition.
The impact of master scheduling models on student performance as identified by the Academic Excellence Indicator System (AEIS) database in the high schools of the San Antonio Independent School District, San Antonio, Texas

The Impact of Master Scheduling Models on Student Performance as Identified by the Academic Excellence Indicator System (AEIS) Database in the High Schools of the San Antonio Independent School District, San Antonio, Texas. (August 2005) Scott Edwin Morgan, B.S., Angelo State University; M. Ed., Angelo State University Co-Chairs of Advisory Committee: Dr. Virginia Collier Dr. Stephen L. Stark This study determined the impact of master scheduling models on student performance as reported by the AEIS database in the high schools of the SAISD. General student performance and the Texas Assessment of Knowledge and Skills were the primary measures for comparison. The SAISD made a transition from an A-B block schedule in 2002 to a traditional-seven period model in 2003. Conclusions have been made as to the degree of influence that traditional and block schedules have on student performance. The population of this study was the eight high schools of the SAISD. All students enrolled on these campuses were included in the data analysis. The population was 14,418 students during the 2002-2003 school year and 13,689 in 2003-2004. Descriptive statistics and analysis of variance (ANOVA) were the measures utilized for the purposes of population comparisons and data review. Based on the findings of this study, the recommendations for practice indicate the following:
The puzzling mutational landscape of the SARS‐2‐variant Omicron

The recently emerging SARS‐CoV‐2 variant omicron displays an unusual association of 30 mutations, 3 deletions, and 1 insertion. To analyze the impact of this atypic mutational landscape, we constructed a complete structure of the omicron spike protein. Compared with the delta variant, the receptor‐binding domain (RBD) of omicron has an increased electrostatic surface potential, but a decreased affinity for the ACE‐2 receptor. The N‐terminal domain (NTD) has both a decreased surface potential and a lower affinity for lipid rafts. The omicron variant is predicted to be less fusogenic and thus less pathogenic than delta, due to a geometric reorganization of the S1‐S2 cleavage site. Overall, these virological parameters suggest that omicron does not have a significant infectivity advantage over the delta variant. However, in omicron, neutralizing epitopes are greatly affected, suggesting that current vaccines will probably confer little protection against this variant. In conclusion, the puzzling mutational pattern of the omicron variant combines contradictory properties which may either decrease (virological properties) or increase (immunological escape/facilitation) the transmission of this variant in the human population. This Janus‐like phenotype may explain some conflicting reports on the initial assessment of omicron and provide new insights about the molecular mechanisms controlling its dissemination and pathogenesis worldwide.
Ultra-high dose rate electron beams and the FLASH effect: from preclinical evidence to a new radiotherapy paradigm.

In their seminal paper from 2014, Fauvadon et al. coined the term FLASH irradiation to describe ultra-high-dose-rate irradiation with dose rates greater than 40 Gy/s, which results in delivery times of fractions of a second. The experiments presented in that paper were performed with a high-dose-per-pulse 4.5-MeV electron beam, and the results served as the basis for the modern-day field of FLASH radiation therapy (RT). In this article, we review the studies that have been published after those early experiments, demonstrating the robust effects of FLASH RT on normal tissue sparing in preclinical models. We also outline the various irradiation parameters that have been used. Although the robustness of the biological response has been established, the mechanisms behind the FLASH effect are currently under investigation in a number of laboratories. However, differences in the magnitude of the FLASH effect between experiments in different labs have been reported. Reasons for these differences even within the same animal model are currently unknown, but likely has to do with the marked differences in irradiation parameter settings used. Here we show that these parameters are often not reported, which complicates large multi-study comparisons. For this reason, we propose a new standard for beam parameter reporting and discuss a systematic path to the clinical translation of FLASH radiation therapy. This article is protected by copyright. All rights reserved.
The relationship between pathogen life‐history traits and metapopulation dynamics

Summary Plant pathogen traits, such as transmission mode and overwintering strategy, may have important effects on dispersal and persistence, and drive disease dynamics. Still, we lack insights into how life‐history traits influence spatiotemporal disease dynamics. We adopted a multifaceted approach, combining experimental assays, theory and field surveys, to investigate whether information about two pathogen life‐history traits – infectivity and overwintering strategy – can predict pathogen metapopulation dynamics in natural systems. For this, we focused on four fungal pathogens (two rust fungi, one chytrid fungus and one smut fungus) on the forest herb Anemone nemorosa. Pathogens infecting new plants mostly via spores (the chytrid and smut fungi) had higher patch occupancies and colonization rates than pathogens causing mainly systemic infections and overwintering in the rhizomes (the two rust fungi). Although the rust fungi more often occupied well‐connected plant patches, the chytrid and smut fungi were equally or more common in isolated patches. Host patch size was positively related to patch occupancy and colonization rates for all pathogens. Predicting disease dynamics is crucial for understanding the ecological and evolutionary dynamics of host–pathogen interactions, and to prevent disease outbreaks. Our study shows that combining experiments, theory and field observations is a useful way to predict disease dynamics.
Direct Comparison of SARS Co-V-2 Nasal RT- PCR and Rapid Antigen Test (BinaxNOWTM) at a Community Testing Site During an Omicron Surge

In 731 persons seeking COVID-19 testing at a walk-up San Francisco community site in January 2022, simultaneous nasal rapid antigen testing (BinaxNOWTM) and RT-PCR testing was performed. There were 296 (40.5%) positive tests by RT-PCR; 97% of a random sample were the omicron variant. Sensitivity of a single antigen test was 95.2% (95% CI 92-98%); 82.1% (95% CI 77-87%) and 65.2% (95% CI 60-70%) for Ct threshold of < 30, < 35 and no threshold, respectively. A single BinaxNowTM rapid antigen test detected 95% of high viral load omicron cases from nasal specimens. As currently recommended, repeat testing should be done for high- risk persons with an initial negative antigen test result.