Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless Self‐Powered Optogenetic System for Long‐Term Cardiac Neuromodulation to Improve Post‐MI Cardiac Remodeling and Malignant Arrhythmia

Autonomic imbalance is an important characteristic of patients after myocardial infarction (MI) and adversely contributes to post‐MI cardiac remodeling and ventricular arrhythmias (VAs). A previous study proved that optogenetic modulation could precisely inhibit cardiac sympathetic hyperactivity and prevent acute ischemia‐induced VAs. Here, a wireless self‐powered optogenetic modulation system is introduced, which achieves long‐term precise cardiac neuromodulation in ambulatory canines. The wireless self‐powered optical system based on a triboelectric nanogenerator is powered by energy harvested from body motion and realized the effective optical illumination that is required for optogenetic neuromodulation (ON). It is further demonstrated that long‐term ON significantly mitigates MI‐induced sympathetic remodeling and hyperactivity, and improves a variety of clinically relevant outcomes such as improves ventricular dysfunction, reduces infarct size, increases electrophysiological stability, and reduces susceptibility to VAs. These novel insights suggest that wireless ON holds translational potential for the clinical treatment of arrhythmia and other cardiovascular diseases related to sympathetic hyperactivity. Moreover, this innovative self‐powered optical system may provide an opportunity to develop implantable/wearable and self‐controllable devices for long‐term optogenetic therapy.
Irregular Prime Divisors of the Bernoulli Numbers

If p is an irregular prime, p p and that B2n/2n #(B2n+p1/2n + p - 1) (mod p2) if (p, 2n) is an irregular pair. As a result, we obtain another verification that the cyclotomic invariants ,p of Iwasawa all vanish for primes p 3, e Z 1, there exist infinitely many Bernoulli numerators P2, which are divisible by p'. If we add the restriction that (p, n) = 1, however, then the problem of determining when pe divides P2, becomes more difficult. It turns out to be convenient to study the quotients B2n/2n = P2 n/2nQ2nw which, when reduced, are p-integers if p - 1 f 2n by the theorems of von Staudt-Clausen and J. C. Adams. The general problem, then, is to determine, for a given prime-power pe, those indices, 2n, p - 1 f 2n, for which pe divides the p-integer B2n/2n. It follows im- mediately from a congruence of Kummer that p must be irregular, and that p divides B2n/2n if and only if p divides P2,,', where 2n' is the least positive residue of 2n (mod p - 1). This settles the case e = 1. Moreover, we see that any irregular prime p divides infinitely many Bernoulli numerators P2, with (p, n) = 1. This paper reports on some computations done recently on the PDP-10 computer at Bowdoin College to investigate the case e = 2. About fifty years ago, Pollaczek (9) noted that 372 divides B284/284, showing that the case e = 2 is possible. Mont- gomery (8) raised the question whether or not p2 divides P2n for 0 < 2n < p- 1. Our computations show that the answer to this is negative for all irregular primes p < 8000. Further, for the irregular primes p < 8000, we can characterize precisely those indices 2n for which p2 divides B2n/2n. Our results show that the square of any irregular prime p < 8000 divides infinitely many Bernoulli numerators PM, with (p, n) = 1. Finally, we compare some of our computations to those done earlier
Clinical Reasoning: A 25-Year-Old Woman With Eye Swelling and Headache

Cerebral venous sinus thrombosis (CVST) is uncommon. Risk factors include inherited and acquired factors. Rapid diagnosis and treatment is essential and can help prevent complications, which can include seizures and visual disturbance. A 25-year-old woman with a background history of CVST and intermittent warfarin use presented to the hospital in 2021 with a 3-month history of progressive eye swelling and headache. Her headache was located in the right frontal region and worsened with movement. Her workup was consistent with recurrent CVST and dural arteriovenous fistula. IR-guided embolization of the fistulas and stenting of her sinuses was performed. She was treated with dual antiplatelet therapy and therapeutic tinzaparin. Her symptoms improved markedly over several days, with improvement in headache and visual acuity. This case illustrates the potential for severe complications including visual disturbance in untreated CVST, as well as the importance of a thorough history and examination in aiding the recognition of the condition.
Optimized contact configuration for the study of transport phenomena in ropes of single-wall carbon nanotubes

The study of the intrinsic transport properties of carbon nanotubes suffers from the difficulties in fabricating noninvasive contacts. Here, we present a scheme for the investigation of transport phenomena in metallic single-wall carbon nanotubes by means of a special four-terminal measurement configuration. To suppress the impact of the contacts on the measured conductance in a tube, we found a combination of top and bottom contacts to the rope of single-wall nanotubes to be most appropriate. Our experimental findings demonstrate that a linear decrease of the sample resistance can be observed under these circumstances without the common increase of resistance for decreasing temperatures.
Break It Down: Evidence for Structural Compositionality in Neural Networks

Many tasks can be described as compositions over subroutines. Though modern neural networks have achieved impressive performance on both vision and language tasks, we know little about the functions that they implement. One possibility is that neural networks implicitly break down complex tasks into subroutines, implement modular solutions to these subroutines, and compose them into an overall solution to a task -- a property we term structural compositionality. Or they may simply learn to match new inputs to memorized representations, eliding task decomposition entirely. Here, we leverage model pruning techniques to investigate this question in both vision and language, across a variety of architectures, tasks, and pretraining regimens. Our results demonstrate that models oftentimes implement solutions to subroutines via modular subnetworks, which can be ablated while maintaining the functionality of other subroutines. This suggests that neural networks may be able to learn to exhibit compositionality, obviating the need for specialized symbolic mechanisms.
Ergodic control of jump diffusions in R d under a near-monotone cost assumption

We study the ergodic control problem for jump diffusion processes over R d . Neither the running cost, nor the data are assumed to be bounded. However, the Levy measure is assumed to be locally compactly supported. We follow the vanishing discount approach and derive the HJB equation for the ergodic criterion by taking the limit of the HJB equation for the discounted criterion, as the discount factor approaches zero. In this manner, optimality over all stationary policies is established. Index Terms— I. INTRODUCTION The problem of control of jump diffusions has recently attracted some attention from the control community, pri- marily due to its application to network control problems and also because of the current interest in stochastic hybrid systems. Many results for the discounted problem are avail- able in (2), including the game theoretic setting. Control of reflected jump diffusions over a bounded domain can be found in (11). More recently the ergodic control problem has been tackled, under a blanket stability assumption (12). In this work, we present the solution of the ergodic control problem over R d in adequate generality. Instead of imposing any stability assumptions, we concentrate on cost functions that are near-monotone (4). The coefficients of the stochastic differential equation are allowed to grow unbounded and so does the running cost. However the Levy measure is assumed to be locally compactly supported. In other words we consider only stochastic equations in which the jump intensity during an infinitesimal period of time is locally finite. We follow the vanishing discount approach and derive the HJB equation for the ergodic criterion by taking the limit of the HJB equation for the discounted criterion, as the discount factor approaches zero. This is facilitated by what we believe is a new result in PDE for integro-differential operators, namely a Harnack inequality (Theorem 4.2). The approach taken shows optimality among the class of stationary policies. In order to establish optimality over all admissible policies, a convex analytic approach is best suited (3), (4). This is not undertaken in this paper. II. THE MODEL AND ASSUMPTIONS
Simultaneous SPECT and CT with an opposed dual head gamma camera system and conventional parallel hole collimators

All examinations aiming at quantitative assessment of activity concentrations suffer from non-uniform attenuation due to inhomogenous tissue density in various body regions. In such applications, a combined transmission tomography with SPECT has demonstrated great clinical advantages since it allows a pixel by pixel correction for attenuation based on measured data. The authors have developed a method for transmission studies that is applicable to dual head detector systems with conventional parallel-hole collimators. A scanning line-source (Am-241) is mounted close to one camera head with the beam directed towards the other head. Two energy discrimination windows are used for each camera head. The camera-head near to the transmission source registrates SPECT-data in one window and the spill-over of scattered emission photons into the Am-window. The opposed camera registrates SPECT-data in one window and the transmission data from the Am-source in the second one. Post acquisition corrections are made for differences in camera uniformity and the loss of SPECT-data due to shadowing of the field-of-view during the transverse movement of the transmission device (about 5%). The height of the collimated slit source is only 30 mm to allow a close adjustment of both camera heads to the patient.
TranSOP: Transformer-Based Multimodal Classification for Stroke Treatment Outcome Prediction

Acute ischaemic stroke, caused by an interruption in blood flow to brain tissue, is a leading cause of disability and mortality worldwide. The selection of patients for the most optimal ischaemic stroke treatment is a crucial step for a successful outcome, as the effect of treatment highly depends on the time to treatment. We propose a transformer-based multimodal network (TranSOP) for a classification approach that employs clinical metadata and imaging information, acquired on hospital admission, to predict the functional outcome of stroke treatment based on the modified Rankin Scale (mRS). This includes a fusion module to efficiently combine 3D non-contrast computed tomography (NCCT) features and clinical information. In comparative experiments using unimodal and multimodal data on the MRCLEAN dataset, we achieve a state of the art AUC score of 0.85.
Multi-Agent congestion cost minimization with linear function approximation

This work considers multiple agents traversing a network from a source node to the goal node. The cost to an agent for traveling a link has a private as well as a congestion component. The agent's objective is to find a path to the goal node with minimum overall cost in a decentralized way. We model this as a fully decentralized multi-agent reinforcement learning problem and propose a novel multi-agent congestion cost minimization (MACCM) algorithm. Our MACCM algorithm uses linear function approximations of transition probabilities and the global cost function. In the absence of a central controller and to preserve privacy, agents communicate the cost function parameters to their neighbors via a time-varying communication network. Moreover, each agent maintains its estimate of the global state-action value, which is updated via a multi-agent extended value iteration (MAEVI) sub-routine. We show that our MACCM algorithm achieves a sub-linear regret. The proof requires the convergence of cost function parameters, the MAEVI algorithm, and analysis of the regret bounds induced by the MAEVI triggering condition for each agent. We implement our algorithm on a two node network with multiple links to validate it. We first identify the optimal policy, the optimal number of agents going to the goal node in each period. We observe that the average regret is close to zero for 2 and 3 agents. The optimal policy captures the trade-off between the minimum cost of staying at a node and the congestion cost of going to the goal node. Our work is a generalization of learning the stochastic shortest path problem.
Inspecting class hierarchies in classification-based metric learning models

Most classification models treat all misclassifications equally. However, different classes may be related, and these hierarchical relationships must be considered in some classification problems. These problems can be addressed by using hierarchical information during training. Unfortunately, this information is not available for all datasets. Many classification-based metric learning methods use class representatives in embedding space to represent different classes. The relationships among the learned class representatives can then be used to estimate class hierarchical structures. If we have a predefined class hierarchy, the learned class representatives can be assessed to determine whether the metric learning model learned semantic distances that match our prior knowledge. In this work, we train a softmax classifier and three metric learning models with several training options on benchmark and real-world datasets. In addition to the standard classification accuracy, we evaluate the hierarchical inference performance by inspecting learned class representatives and the hierarchy-informed performance, i.e., the classification performance, and the metric learning performance by considering predefined hierarchical structures. Furthermore, we investigate how the considered measures are affected by various models and training options. When our proposed ProxyDR model is trained without using predefined hierarchical structures, the hierarchical inference performance is significantly better than that of the popular NormFace model. Additionally, our model enhances some hierarchy-informed performance measures under the same training options. We also found that convolutional neural networks (CNNs) with random weights correspond to the predefined hierarchies better than random chance.
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?

Memes can sway people’s opinions over social media as they combine visual and textual information in an easy-to-consume manner. Since memes instantly turn viral, it becomes crucial to infer their intent and potentially associated harmfulness to take timely measures as needed. A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities. Here, we aim to understand whether the meme glorifies, vilifies, or victimizes each entity it refers to. To this end, we address the task of role identification of entities in harmful memes, i.e., detecting who is the ‘hero’, the ‘villain’, and the ‘victim’ in the meme, if any. We utilize HVVMemes – a memes dataset on US Politics and Covid-19 memes, released recently as part of the CONSTRAINT@ACL-2022 shared-task. It contains memes, entities referenced, and their associated roles: hero, villain, victim, and other. We further design VECTOR (Visual-semantic role dEteCToR), a robust multi-modal framework for the task, which integrates entity-based contextual information in the multi-modal representation and compare it to several standard unimodal (text-only or image-only) or multi-modal (image+text) models. Our experimental results show that our proposed model achieves an improvement of 4% over the best baseline and 1% over the best competing stand-alone submission from the shared-task. Besides divulging an extensive experimental setup with comparative analyses, we finally highlight the challenges encountered in addressing the complex task of semantic role labeling within memes.
Modified gravity: A unified approach to metric-affine models

The starting point of this work is the original Einstein action, sometimes called the Gamma squared action. Continuing from our previous results, we study various modified theories of gravity following the Palatini approach. The metric and the connection will be treated as independent variables leading to generalized theories, which may contain torsion or non-metricity or both. Due to our particular approach involving the Einstein action, our setup allows us to formulate a substantial number of new theories not previously studied. Our results can be linked back to well-known models, such as Einstein–Cartan theory and metric-affine theories, and also links to many recently studied modified gravity models. In particular, we propose an Einstein–Cartan type modified theory of gravity, which contains propagating torsion, provided our function depends non-linearly on a boundary term. We also can state precise conditions for the existence of propagating torsion. Our work concludes with a brief discussion of cosmology and the role of cosmological torsion in our model. We find solutions with early-time inflation and late-time matter dominated behavior. No matter sources are required to drive inflation, and it becomes a purely geometrical effect.
Deep Laplacian-based Options for Temporally-Extended Exploration

Selecting exploratory actions that generate a rich stream of experience for better learning is a fundamental challenge in reinforcement learning (RL). An approach to tackle this problem consists in selecting actions according to specific policies for an extended period of time, also known as options. A recent line of work to derive such exploratory options builds upon the eigenfunctions of the graph Laplacian. Importantly, until now these methods have been mostly limited to tabular domains where (1) the graph Laplacian matrix was either given or could be fully estimated, (2) performing eigendecomposition on this matrix was computationally tractable, and (3) value functions could be learned exactly. Additionally, these methods required a separate option discovery phase. These assumptions are fundamentally not scalable. In this paper we address these limitations and show how recent results for directly approximating the eigenfunctions of the Laplacian can be leveraged to truly scale up options-based exploration. To do so, we introduce a fully online deep RL algorithm for discovering Laplacian-based options and evaluate our approach on a variety of pixel-based tasks. We compare to several state-of-the-art exploration methods and show that our approach is effective, general, and especially promising in non-stationary settings.
Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content

Our work advances an approach for predicting hate speech in social media, drawing out the critical need to consider the discussions that follow a post to successfully detect when hateful discourse may arise. Using graph transformer networks, coupled with modelling attention and BERT-level natural language processing, our approach can capture context and anticipate upcoming anti-social behaviour. In this paper, we offer a detailed qualitative analysis of this solution for hate speech detection in social networks, leading to insights into where the method has the most impressive outcomes in comparison with competitors and identifying scenarios where there are challenges to achieving ideal performance. Included is an exploration of the kinds of posts that permeate social media today, including the use of hateful images. This suggests avenues for extending our model to be more comprehensive. A key insight is that the focus on reasoning about the concept of context positions us well to be able to support multi-modal analysis of online posts. We conclude with a reflection on how the problem we are addressing relates especially well to the theme of dynamic change, a critical concern for all AI solutions for social impact. We also comment briefly on how mental health well-being can be advanced with our work, through curated content attuned to the extent of hate in posts.
Pseudoplane-wave gravitational calibrator for gravitational wave observatories

The precisions of existing gravitational calibrators for gravitational wave observatories are limited by their dependence on the relative position between the calibrators and the observatory's test masses. Here we present a novel geometry consisting of four quadrupole rotors placed at the vertices of a rectangle centered on the test mass. The phases and rotation directions are selected to produce a pseudo plane-wave sinusoidal gravitational acceleration with amplitude of ~ 100 fm/s^2. We show that this acceleration only has minimal dependence on the test mass position relative to the rotor array and can yield 0.15% acceleration amplitude uncertainty while tolerating a 1-cm test mass position uncertainty. The acceleration can be directed precisely along the optical axis of the interferometer arm and applies no torque on the test mass. In addition, the small size of the rotors has significant engineering and safety benefits.
Socio-economic utility and chemical potential

In statistical physics, the conservation of particle number results in the equalization of the chemical potential throughout a system at equilibrium. In contrast, the homogeneity of utility in socio-economic models is usually thought to rely on the competition between individuals, leading to Nash equilibrium. We show that both views can be reconciled by introducing a notion of chemical potential in a wide class of socio-economic models, and by relating it in a direct way to the equilibrium value of the utility. This approach also allows the dependence of utility across the system to be determined when agents take decisions in a probabilistic way. Numerical simulations of a urban economic model also suggest that our result is valid beyond the initially considered class of solvable models.
Evolution of Filter Bubbles and Polarization in News Recommendation

Recent work in news recommendation has demonstrated that recommenders can over-expose users to articles that support their pre-existing opinions. However, most existing work focuses on a static setting or over a short-time window, leaving open questions about the long-term and dynamic impacts of news recommendations. In this paper, we explore these dynamic impacts through a systematic study of three research questions: 1) How do the news reading behaviors of users change after repeated long-term interactions with recommenders? 2) How do the inherent preferences of users change over time in such a dynamic recommender system? 3) Can the existing SOTA static method alleviate the problem in the dynamic environment? Concretely, we conduct a comprehensive data-driven study through simulation experiments of political polarization in news recommendations based on 40,000 annotated news articles. We find that users are rapidly exposed to more extreme content as the recommender evolves. We also find that a calibration-based intervention can slow down this polarization, but leaves open significant opportunities for future improvements