Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards model-free RL algorithms that scale well with unstructured data

Conventional reinforcement learning (RL) algorithms exhibit broad generality in their theoretical formulation and high performance on several challenging domains when combined with powerful function approximation. However, developing RL algorithms that perform well across problems with unstructured observations at scale remains challenging because most function approximation methods rely on externally provisioned knowledge about the structure of the input for good performance (e.g. convolutional networks, graph neural networks, tile-coding). A common practice in RL is to evaluate algorithms on a single problem, or on problems with limited variation in the observation scale. RL practitioners lack a systematic way to study how well a single RL algorithm performs when instantiated across a range of problem scales, and they lack function approximation techniques that scale well with unstructured observations. We address these limitations by providing environments and algorithms to study scaling for unstructured observation vectors and flat action spaces. We introduce a family of combinatorial RL problems with an exponentially large state space and high-dimensional dynamics but where linear computation is sufficient to learn a (nonlinear) value function estimate for performant control. We provide an algorithm that constructs reward-relevant general value function (GVF) questions to find and exploit predictive structure directly from the experience stream. In an empirical evaluation of the approach on synthetic problems, we observe a sample complexity that scales linearly with the observation size. The proposed algorithm reliably outperforms a conventional deep RL algorithm on these scaling problems, and they exhibit several desirable auxiliary properties. These results suggest new algorithmic mechanisms by which algorithms can learn at scale from unstructured data.
Well-posed nonvacuum solutions in Robinson-Trautman geometry

We study nonlinear matter models compatible with radiative Robinson--Trautman spacetimes and analyze their stability and well-posedness. The results lead us to formulate a conjecture relating the (in)stability and well/ill-posedness to the character of singularity appearing in the solutions. We consider two types of nonlinear electrodynamics models, namely we provide a radiative ModMax solution and extend recent results for the RegMax model by considering the magnetically charged case. In both cases, we investigate linear perturbations around stationary spherically symmetric solutions to determine the stability and principal symbol of the system to argue about well-posedness of these geometries. Additionally, we consider a nonlinear sigma model as a source for Robinson--Trautman geometry. This leads to stationary solutions with toroidal (as opposed to spherical) topology thus demanding modification of the analysis.
Visual-information-driven model for crowd simulation using temporal convolutional network

Crowd simulations play a pivotal role in building design, influencing both user experience and public safety. While traditional knowledge-driven models have their merits, data-driven crowd simulation models promise to bring a new dimension of realism to these simulations. However, most of the existing data-driven models are designed for specific geometries, leading to poor adaptability and applicability. A promising strategy for enhancing the adaptability and realism of data-driven crowd simulation models is to incorporate visual information, including the scenario geometry and pedestrian locomotion. Consequently, this paper proposes a novel visual-information-driven (VID) crowd simulation model. The VID model predicts the pedestrian velocity at the next time step based on the prior social-visual information and motion data of an individual. A radar-geometry-locomotion method is established to extract the visual information of pedestrians. Moreover, a temporal convolutional network (TCN)-based deep learning model, named social-visual TCN, is developed for velocity prediction. The VID model is tested on three public pedestrian motion datasets with distinct geometries, i.e., corridor, corner, and T-junction. Both qualitative and quantitative metrics are employed to evaluate the VID model, and the results highlight the improved adaptability of the model across all three geometric scenarios. Overall, the proposed method demonstrates effectiveness in enhancing the adaptability of data-driven crowd models.
Tailoring Self-Rationalizers with Multi-Reward Distillation

Large language models (LMs) are capable of generating free-text rationales to aid question answering. However, prior work 1) suggests that useful self-rationalization is emergent only at significant scales (e.g., 175B parameter GPT-3); and 2) focuses largely on downstream performance, ignoring the semantics of the rationales themselves, e.g., are they faithful, true, and helpful for humans? In this work, we enable small-scale LMs (approx. 200x smaller than GPT-3) to generate rationales that not only improve downstream task performance, but are also more plausible, consistent, and diverse, assessed both by automatic and human evaluation. Our method, MaRio (Multi-rewArd RatIOnalization), is a multi-reward conditioned self-rationalization algorithm that optimizes multiple distinct properties like plausibility, diversity and consistency. Results on five difficult question-answering datasets StrategyQA, QuaRel, OpenBookQA, NumerSense and QASC show that not only does MaRio improve task accuracy, but it also improves the self-rationalization quality of small LMs across the aforementioned axes better than a supervised fine-tuning (SFT) baseline. Extensive human evaluations confirm that MaRio rationales are preferred vs. SFT rationales, as well as qualitative improvements in plausibility and consistency.
Multi-State Brain Network Discovery

Brain network discovery aims to find nodes and edges from the spatio-temporal signals obtained by neuroimaging data, such as fMRI scans of human brains. Existing methods tend to derive representative or average brain networks, assuming observed signals are generated by only a single brain activity state. However, the human brain usually involves multiple activity states, which jointly determine the brain activities. The brain regions and their connectivity usually exhibit intricate patterns that are difficult to capture with only a single-state network. Recent studies find that brain parcellation and connectivity change according to the brain activity state. We refer to such brain networks as multi-state, and this mixture can help us understand human behavior. Thus, compared to a single-state network, a multi-state network can prevent us from losing crucial information of cognitive brain network. To achieve this, we propose a new model called MNGL (Multi-state Network Graphical Lasso), which successfully models multi-state brain networks by combining CGL (coherent graphical lasso) with GMM (Gaussian Mixture Model). Using both synthetic and real world ADHD-200 fMRI datasets, we demonstrate that MNGL outperforms recent state-of-the-art alternatives by discovering more explanatory and realistic results.
Including Neutrino-driven Convection in the Force Explosion Condition to Predict Explodability of Multidimensional Core-collapse Supernovae (FEC+)

Most massive stars end their lives with core collapse. However, it is not clear which explode as a core-collapse supernova (CCSN), leaving behind a neutron star, and which collapse to a black hole, aborting the explosion. One path to predict explodability without expensive multidimensional simulations is to develop analytic explosion conditions. These analytic conditions also provide a deeper understanding of the explosion mechanism and they provide some insight into why some simulations explode and some do not. The analytic force explosion condition (FEC) reproduces the explosion conditions of spherically symmetric CCSN simulations. In this follow-up manuscript, we include the dominant multidimensional effect that aids explosion—neutrino-driven convection—in the FEC. This generalized critical condition (FEC+) is suitable for multidimensional simulations and has potential to accurately predict explosion conditions of two- and three-dimensional CCSN simulations. We show that adding neutrino-driven convection reduces the critical condition by ∼30%, which is consistent with previous multidimensional simulations.
Neural Network Reconstruction of the Left Atrium using Sparse Catheter Paths

Catheter based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate map of the left atrial sub-endocardial surface including the ostia of the pulmonary veins, which requires dense sampling of the surface and takes more than 10 minutes. The focus of this work is to provide left atrial visualization early in the procedure to ease procedure complexity and enable further workflows, such as using catheters that have difficulty sampling the surface. We propose a dense encoder-decoder network with a novel regularization term to reconstruct the shape of the left atrium from partial data which is derived from simple catheter maneuvers. To train the network, we acquire a large dataset of 3D atria shapes and generate corresponding catheter trajectories. Once trained, we show that the suggested network can sufficiently approximate the atrium shape based on a given trajectory. We compare several network solutions for the 3D atrium reconstruction. We demonstrate that the solution proposed produces realistic visualization using partial acquisition within a 3-minute time interval. Synthetic and human clinical cases are shown.
Consumer Perceptions of Seafood Industries in the Wake of the Deepwater Horizon Oil Spill and Fukushima Daiichi Nuclear Disaster

The impact of environmental disasters on consumers’ perceptions and preferences for specific food items has seldom been studied in the applied economics literature. Recent aquatic disasters, namely the Deepwater Horizon Oil Spill and Fukushima Daiichi Nuclear Disaster, have had profound impacts on fisheries serving US consumers and on agribusinesses within the aquaculture industry. This study explores consumer preferences using a nation-wide representative sample, and finds that twenty-nine percent of US consumers sought to reduce their seafood consumption due to the Deepwater Horizon Oil Spill and one-third of respondents indicated they sought to reduce their seafood consumption in the wake of the Daiichi nuclear disaster Additionally, over 50% believed that Asian seafood poses a consumer health risk because of the Japanese nuclear disaster. Understanding key factors that influence consumer behavior in the wake of environmental disasters can make fisheries, seafood industries and agribusiness more resilient when facing such catastrophic events. Our results find that key socio-demographic variables affect consumer behavior including gender, age, food safety concerns, value for country of origin labeling, and geographic location. Careful and efficient response by the seafood supply chain will enable effective communication with consumers and allow for optimal policy decision-making.
Metachronal coordination at the mesoscale

In nature, metachronal coordination is an efficient strategy for fluid pumping and self-propulsion. Yet, mimetic systems for this type of organization are scarce. Recently, metachronal motion was observed in a bead-based magnetocapillary mesoswimmer, but the mechanism of such device's behavior remained unclear. Here, we combine theory and experiments to explain two swimming regimes that we identify by modulation of the driving frequency. In the linear, low-frequency regime, the swimmer motion originates from individual bead rotations. However, the high-frequency, metachronal regime is dominated by deformations of the device near a mechanical resonance, which highlights the role of bead inertia for optimized self-propulsion.
A Unified Dynamic Model for the Decomposition of Skin Conductance and the Inference of Sudomotor Nerve Activities

In behavioral health informatics, inferring an individual's psychological state from physiological and behavioral data is fundamental. A key physiological signal in this endeavor is electrodermal activity (EDA), often quantified as skin conductance (SC), known for its sensitivity to a variety of psychological stimuli. Traditional methods to analyze skin conductance, such as the trough-to-peak method, often result in imprecise estimations due to overlapping skin conductance responses. While various mathematical models have been proposed to improve the analysis, many of them do not incorporate the tonic level in the dynamic system. This paper introduces a novel fourth order dynamic system to model the temporal dynamics of skin conductance, unifying both the tonic level and phasic response. Applied to a large dataset with over 200 participants, majority of the models achieved an $R^2$ value above 0.99. Furthermore, this work offers a unique three-component decomposition of skin conductance, shedding light on its temporal dynamics. Comparative evaluations highlight the model's capability to differentiate arousal levels and maintain an appropriate sparsity level for the estimated sudomotor nerve activities signal. The code of the proposed model and algorithm are available for open access.
Thermal Face Image Classification using Deep Learning Techniques

Thermal images have various applications in security, medical and industrial domains. This paper proposes a practical deep-learning approach for thermal image classification. Accurate and efficient classification of thermal images poses a significant challenge across various fields due to the complex image content and the scarcity of annotated datasets. This work uses a convolutional neural network (CNN) architecture, specifically ResNet-50 and VGGNet-19, to extract features from thermal images. This work also applied Kalman filter on thermal input images for image denoising. The experimental results demonstrate the effectiveness of the proposed approach in terms of accuracy and efficiency.
Energy-Efficient Multidimensional Constellation Based on Leech Lattice for Visible Light Communications

In this paper, a 24-dimensional geometrically-shaped constellation design based on Leech lattice is presented for indoor visible light communications (VLCs) with a peak-and an average-intensity input constraints. Firstly, by leveraging tools from large deviation theory, we characterize second-order asymptotics of the optimal constellation shaping region under aforementioned intensity constraints, which further refine our previous results in [Chen. et. al, 2020]. Within the optimal geometrical shaping region, we develop an energy-efficient 24-dimensional constellation design, where a significant coding gain brought by the Leech lattice and the nearly-maximum shaping gain are incorporated by using a strategy called coarsely shaping and finely coding. Fast algorithms for constellation mapping and demodulation are presented as well. Numerical results verifies the superiority of our results as compared with existing methods.
Group Testing for Accurate and Efficient Range-Based Near Neighbor Search : An Adaptive Binary Splitting Approach

This work presents an adaptive group testing framework for the range-based high dimensional near neighbor search problem. The proposed method detects high-similarity vectors from an extensive collection of high dimensional vectors, where each vector represents an image descriptor. Our method efficiently marks each item in the collection as neighbor or non-neighbor on the basis of a cosine distance threshold without exhaustive search. Like other methods in the domain of large scale retrieval, our approach exploits the assumption that most of the items in the collection are unrelated to the query. Unlike other methods, it does not assume a large difference between the cosine similarity of the query vector with the least related neighbor and that with the least unrelated non-neighbor. Following the procedure of binary splitting, a multi-stage adaptive group testing algorithm, we split the set of items to be searched into half at each step, and perform dot product tests on smaller and smaller subsets, many of which we are able to prune away. We experimentally show that our method achieves a speed-up over exhaustive search by a factor of more than ten with an accuracy same as that of exhaustive search, on a variety of large datasets. We present a theoretical analysis of the expected number of distance computations per query and the probability that a pool with a certain number of members will be pruned. In this way, our method exploits very useful and practical distributional properties unlike other methods. In our method, all required data structures are created purely offline. Moreover, our method does not impose any strong assumptions on the number of true near neighbors, is adaptible to streaming settings where new vectors are dynamically added to the database, and does not require any parameter tuning.
Enriched Presheaf Model of Quantum FPC

Selinger gave a superoperator model of a first-order quantum programming language and proved that it is fully definable and hence fully abstract. This paper proposes an extension of the superoperator model to higher-order programs based on modules over superoperators or, equivalently, enriched presheaves over the category of superoperators. The enriched presheaf category can be easily proved to be a model of intuitionistic linear logic with cofree exponential, from which one can cave out a model of classical linear logic by a kind of bi-orthogonality construction. Although the structures of an enriched presheaf category are usually rather complex, a morphism in the classical model can be expressed simply as a matrix of completely positive maps. The model inherits many desirable properties from the superoperator model. A conceptually interesting property is that our model has only a state whose “total probability” is bounded by 1, i.e. does not have a state where true and false each occur with probability 2/3. Another convenient property inherited from the superoperator model is a ωCPO-enrichment. Remarkably, our model has a sufficient structure to interpret arbitrary recursive types by the standard domain theoretic technique. We introduce Quantum FPC, a quantum λ-calculus with recursive types, and prove that our model is a fully abstract model of Quantum FPC.
Evolution of the Size–Mass Relation of Star-forming Galaxies Since z = 5.5 Revealed by CEERS

We combine deep imaging data from the CEERS early release JWST survey and Hubble Space Telescope imaging from CANDELS to examine the size–mass relation of star-forming galaxies and the morphology–quenching relation at stellar masses M ⋆ ≥ 109.5 M ⊙ over the redshift range 0.5 < z < 5.5. In this study with a sample of 2450 galaxies, we separate star-forming and quiescent galaxies based on their star formation activity and confirm that star-forming and quiescent galaxies have different morphologies out to z = 5.5, extending the results of earlier studies out to higher redshifts. We find that star-forming and quiescent galaxies have typical Sérsic indices of n ∼ 1.3 and n ∼ 4.3, respectively. Focusing on star-forming galaxies, we find that the slope of the size–mass relation is nearly constant with redshift, as was found previously, but shows a modest increase at z ∼ 4.2. The intercept in the size–mass relation declines out to z = 5.5 at rates that are similar to what earlier studies found. The intrinsic scatter in the size–mass relation is relatively constant out to z = 5.5.
Microwave generation and vortex jets in superconductor nanotubes

The dynamics of magnetic flux quanta (Abrikosov vortices) determine the resistive response of superconductors. In pinning-free planar thin films, the penetration and motion of vortices are controlled by edge defects, leading to such arrangements as vortex chains, vortex jets, and phase-slip regimes. Here, relying upon the time-dependent Ginzburg-Landau equation, we predict that these vortex patterns should appear in superconductor open nanotubes even without edge defects, due to the inhomogeneity of the normal magnetic induction component $B_\mathrm{n}$, caused by the 3D tube geometry. The crossing of the half-tubes by dc-driven vortices induces GHz-frequency voltage $U$ oscillations with spectra $U_\mathrm{f}(B)$ evolving between $nf_1$ and $\frac{n}{m}f_1$ [$f_1$: vortex nucleation frequency; $n,m\geq 2$] and blurred in certain ranges of currents and fields. An $nf_1$-spectrum corresponds to a single vortex-chain regime typical for low $B$ and for tubes of small radii. At higher fields, an $\frac{n}{m}f_1$-spectrum points to the presence of $m$ vortex chains in the vortex jets which, in contrast to planar thin films, are not diverging because of constraint to the tube areas where $B_\mathrm{n}$ is close to maximum. A blurry spectrum implies complex arrangements of vortices because of multifurcations of their trajectories. Finally, due to a stronger confinement of single vortex chains in tubes of small radii, we reveal peaks in $dU/dB$ and jumps in the frequency of microwave generation, which occur when the number of fluxons moving in the half-tubes increases by one. In all, our findings are essential for novel 3D superconductor devices which can operate in few- and multi-fluxon regimes.
Iwasawa theory and mock plectic points

We use Iwasawa theory, at a prime $p$ inert in a quadratic imaginary field $K$, to study the arithmetic properties of mock plectic invariants for elliptic curves of rank two. More precisely, under some minor technical assumptions, we prove that the non-vanishing of the mock plectic invariant $\mathcal{Q}_K$ attached to an elliptic curve $E_{/\mathbb{Q}}$ of even analytic rank $r_\mathrm{an}(E/K)\ge2$, and with multiplicative reduction at $p$, implies that the $p$-Selmer rank $r_p(E/K)$ equals $2$. The proof rests on one inclusion of Perrin-Riou's Heegner point main conjecture for elliptic curves with multiplicative reduction at $p$ which we obtain using bipartite Euler systems.
TFNet: Tuning Fork Network with Neighborhood Pixel Aggregation for Improved Building Footprint Extraction

This paper considers the problem of extracting building footprints from satellite imagery -- a task that is critical for many urban planning and decision-making applications. While recent advancements in deep learning have made great strides in automated detection of building footprints, state-of-the-art methods available in existing literature often generate erroneous results for areas with densely connected buildings. Moreover, these methods do not incorporate the context of neighborhood images during training thus generally resulting in poor performance at image boundaries. In light of these gaps, we propose a novel Tuning Fork Network (TFNet) design for deep semantic segmentation that not only performs well for widely-spaced building but also has good performance for buildings that are closely packed together. The novelty of TFNet architecture lies in a a single encoder followed by two parallel decoders to separately reconstruct the building footprint and the building edge. In addition, the TFNet design is coupled with a novel methodology of incorporating neighborhood information at the tile boundaries during the training process. This methodology further improves performance, especially at the tile boundaries. For performance comparisons, we utilize the SpaceNet2 and WHU datasets, as well as a dataset from an area in Lahore, Pakistan that captures closely connected buildings. For all three datasets, the proposed methodology is found to significantly outperform benchmark methods.