Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Particle Creation: From the Dynamical Casimir Effect to Cosmology

We study a stochastic version of the dynamical Casimir effect, computing the particle creation inside a cavity produced by a random motion of one of its walls. We first present a calculation perturbative in the amplitude of the motion. We compare the stochastic particle creation with the deterministic counterpart. Then, we go beyond the perturbative evaluation using a stochastic version of the multiple scale analysis, that takes into account stochastic parametric resonance. We stress the relevance of the coupling between the different modes induced by the stochastic motion. In the single-mode approximation, the equations are formally analogous to those that describe the stochastic particle creation in a cosmological context, that we rederive using multiple scale analysis.
MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images

Myocardial infarction (MI) occurs when an artery supplying blood to the heart is abruptly occluded. The “gold standard” method for imaging MI is cardiovascular magnetic resonance imaging (MRI) with intravenously administered gadolinium-based contrast (with damaged areas apparent as late gadolinium enhancement [LGE]). However, no “gold standard” fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. It has the potential to reduce uncertainty due to technical variability across labs and the inherent problems of data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by atrous spatial pyramid pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: (i) background, (ii) heart muscle, (iii) blood and (iv) LGE areas. Our experiments show that the model named MI-ResNet50-AC provides the best global accuracy (97.38%), mean accuracy (86.01%), weighted intersection over union (IoU) of 96.47%, and bfscore of 64.46% for the global segmentation. However, in detecting only LGE tissue, a smaller model, MI-ResNet18-AC, exhibited higher accuracy (74.41%) than MI-ResNet50-AC (64.29%). New models were compared with state-of-the-art models and manual quantification. Our models demonstrated favorable performance in global segmentation and LGE detection relative to the state-of-the-art, including a four-fold better performance in matching LGE pixels to contours produced by clinicians.
Encryption Mechanism And Resource Allocation Optimization Based On Edge Computing Environment

A method for optimizing encryption mechanism and resource allocation based on edge computing environment is proposed. A local differential privacy algorithm based on a histogram algorithm is used to protect user information during task offloading, which allows accurate preservation of user contextual information while reducing interference with the playback decision. To efficiently offload tasks and improve offloading performance, a joint optimization algorithm for task offloading and resource allocation is proposed that optimizes overall latency. A balance will be found between privacy protection and task offloading accuracy. The impact of contextual data interference on task offloading decisions is minimized while ensuring a predefined level of privacy protection. In the concrete connected vehicle example, the method distributes tasks among roadside devices and neighboring vehicles with sufficient computational resources.
Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.
Using machine learning algorithms to determine the emotional disadaptation of a person by his rhythmogram

In this study we applyed machine-learning algorithms to determine the emotional disadaptation of a person by his rhythmogram. We used the method of determining a subject level of emotional disadaptation and recording of cardiorhythmography. We show that electrocardiogram (ECG) signals can be used for the registration of the emotional disadaptation of a person.
Lossy Micromaser Battery: Almost Pure States in the Jaynes–Cummings Regime

We consider a micromaser model of a quantum battery, where the battery is a single mode of the electromagnetic field in a cavity, charged via repeated interactions with a stream of qubits, all prepared in the same non-equilibrium state, either incoherent or coherent, with the matter–field interaction modeled by the Jaynes–Cummings model. We show that the coherent protocol is superior to the incoherent one, in that an effective pure steady state is achieved for generic values of the model parameters. Finally, we supplement the above collision model with cavity losses, described by a Lindblad master equation. We show that battery performances, in terms of stored energy, charging power, and steady-state purity, are slightly degraded up to moderated dissipation rate. Our results show that micromasers are robust and reliable quantum batteries, thus making them a promising model for experimental implementations.
Systematic study of Schmidt-type partitions via weighted words

Let $S=(s_n)_{n\geq 1}$ be a sequence with elements in a commutative monoid $(\mathcal{M},+,0)$. In this paper, we provide an explicit formula for $$\sum_{\la} C(\la) q^{\sum_{n\geq 1} \la_n\cdot s_n}$$ where $\la=(\la_1,\ldots)$ run through some subsets of over-partitions, and $C(\la)$ is a certain product of ``colors'' assigned to the parts of $\la$, and $q^s$ is a formal power of $q$ for $s\in M$. This formula allows us not only to retrieve several known Schmidt-type theorems but also to provide new Schmidt-type theorems for non-periodic sequences $S$. For example, when $(M,+,0)=(\mathbb{Z}_{\geq 0},+,0)$, $s_n=1$ if there exists $i\geq 1$ such $n=\{i(i-1)/2+1\}$ and $s_n=0$ otherwise, we obtain the following statement: for all non-negative integer $m$, the number of partitions such that $\sum_{i\geq 1}\la_{i(i-1)/2+1} =m$ is equal to the number of plane partitions of $m$. Furthermore, we introduce a new family of partitions, the block partitions, generalizing the $k$-elongated partitions. From that family of partitions, we provide a generalization of a Schmidt-type theorem due to Andrews and Paule regarding $k$-elongated partitions and establish a link with the Eulerian polynomials.
Physics of flow in weighted complex networks

This thesis uses concepts from statistical physics to understand the physics of flow in weighted complex networks. The traditional model for random networks is the Erdohs-Renyi (ER.) network, where a network of N nodes is created by connecting each of the N(N - 1)/2 pairs of nodes with a probability p. The degree distribution, which is the probability distribution of the number of links per node, is a Poisson distribution. Recent studies of the topology in many networks such as the Internet and the world-wide airport network (WAN) reveal a power law degree distribution, known as a scale-free (SF) distribution. To yield a better description of network dynamics, we study weighted networks, where each link or node is given a number. One asks how the weights affect the static and the dynamic properties of the network. In this thesis, two important dynamic problems are studied: the current flow problem, described by Kirchhoff's laws, and the maximum flow problem, which maximizes the flow between two nodes. Percolation theory is applied to these studies of the dynamics in complex networks. We find that the current flow in disordered media belongs to the same universality class as the optimal path. In a randomly weighted network, we identify the infinite incipient percolation cluster as the "superhighway", which contains most of the traffic in a network. We propose an efficient strategy to improve significantly the global transport by improving the superhighways, which comprise a small fraction of the network. We also propose a network model with correlated weights to describe weighted networks such as the WAN. Our model agrees with WAN data, and provides insight into the advantages of correlated weights in networks. Lastly, the upper critical dimension is evaluated using two different numerical methods, and the result is consistent with the theoretical prediction.
Infrared studies of the S235 molecular cloud

Infrared observations from 7.8 to 200 . mu.m have been obtained for the S235 molecular cloud. Far-infrared maps were obtained for a region of active star formation, as marked by the presence of compact H II regions, water masers, and compact near-infrared sources. The primary heating source for the far-infrared emission appears to be the compact H II region, S235A. Detailed examination of the gas energetics in the region supports the plausibility of the picture in which the gas is heated by collisions with warm dust grains. The ratio of far-infrared optical depth to /sup 13/CO column density is somewhat lower in this source than is commonly found. This effect may be caused by the presence of substantial /sup 13/CO in regions where the dust is not warm enough to emit substantial 50--100 . mu.m radiation.
On Higher Order Moments and Rates of Convergence for SDEs with Switching

Second order recurrence of a $d$-dimensional diffusion with an additive Wiener process, with switching, and with one recurrent and one transient regime and constant switching intensities is established under suitable conditions. The approach is based on embedded Markov chains and a priori bounds for the moments of $X_t$ at moments of jumps of the discrete component, as well as on some simple martingale properties.
Sparse Cuts in Hypergraphs from Random Walks on Simplicial Complexes

There are a lot of recent works on generalizing the spectral theory of graphs and graph partitioning to hypergraphs. There have been two broad directions toward this goal. One generalizes the notion of graph conductance to hypergraph conductance [LM16, CLTZ18]. In the second approach one can view a hypergraph as a simplicial complex and study its various topological properties [LM06, MW09, DKW16, PR17] and spectral properties [KM17, DK17, KO18a, KO18b, Opp20]. In this work, we attempt to bridge these two directions of study by relating the spectrum of {\em up-down walks} and {\em swap-walks} on the simplicial complex to hypergraph expansion. In surprising contrast to random-walks on graphs, we show that the spectral gap of swap-walks and up-down walks between level $m$ and $l$ with $1<m \leq l$ can not be used to infer any bounds on hypergraph conductance. Moreover, we show that the spectral gap of swap-walks between $X(1)$ and $X(k-1)$ can not be used to infer any bounds on hypergraph conductance, whereas we give a Cheeger-like inequality relating the spectral of walks between level $1$ and $l$ for any $l \leq k$ to hypergraph expansion. This is a surprising difference between swaps-walks and up-down walks! Finally, we also give a construction to show that the well-studied notion of {\em link expansion} in simplicial complexes can not be used to bound hypergraph expansion in a Cheeger-like manner.
Susceptibilities from holographic transport with topological term

In this note we calculate susceptibilities, as derivatives of a thermodynamical potential, for the general perturbative holographic set-up for transport with magnetic ﬁeld, charge density and topological term, and compare with the quantities obtained in the standard AdS 4 dyonic black hole analysis of Hartnoll and Kovtun. We ﬁnd that the results do not match, despite previous expectations.
On the Level Sets and Invariance of Neural Tuning Landscapes

Visual representations can be defined as the activations of neuronal populations in response to images. The activation of a neuron as a function over all image space has been described as a"tuning landscape". As a function over a high-dimensional space, what is the structure of this landscape? In this study, we characterize tuning landscapes through the lens of level sets and Morse theory. A recent study measured the in vivo two-dimensional tuning maps of neurons in different brain regions. Here, we developed a statistically reliable signature for these maps based on the change of topology in level sets. We found this topological signature changed progressively throughout the cortical hierarchy, with similar trends found for units in convolutional neural networks (CNNs). Further, we analyzed the geometry of level sets on the tuning landscapes of CNN units. We advanced the hypothesis that higher-order units can be locally regarded as isotropic radial basis functions, but not globally. This shows the power of level sets as a conceptual tool to understand neuronal activations over image space.
Variance Reduction for Score Functions Using Optimal Baselines

Many problems involve the use of models which learn probability distributions or incorporate randomness in some way. In such problems, because computing the true expected gradient may be intractable, a gradient estimator is used to update the model parameters. When the model parameters directly affect a probability distribution, the gradient estimator will involve score function terms. This paper studies baselines, a variance reduction technique for score functions. Motivated primarily by reinforcement learning, we derive for the first time an expression for the optimal state-dependent baseline, the baseline which results in a gradient estimator with minimum variance. Although we show that there exist examples where the optimal baseline may be arbitrarily better than a value function baseline, we find that the value function baseline usually performs similarly to an optimal baseline in terms of variance reduction. Moreover, the value function can also be used for bootstrapping estimators of the return, leading to additional variance reduction. Our results give new insight and justification for why value function baselines and the generalized advantage estimator (GAE) work well in practice.
How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection

With the proliferation of deep generative models, deepfakes are improving in quality and quantity everyday. However, there are subtle authenticity signals in pristine videos, not replicated by SOTA GANs. We contrast the movement in deepfakes and authentic videos by motion magnification towards building a generalized deepfake source detector. The sub-muscular motion in faces has different interpretations per different generative models which is reflected in their generative residue. Our approach exploits the difference between real motion and the amplified GAN fingerprints, by combining deep and traditional motion magnification, to detect whether a video is fake and its source generator if so. Evaluating our approach on two multi-source datasets, we obtain 97.17% and 94.03% for video source detection. We compare against the prior deepfake source detector and other complex architectures. We also analyze the importance of magnification amount, phase extraction window, backbone network architecture, sample counts, and sample lengths. Finally, we report our results for different skin tones to assess the bias.
MVTN: Learning Multi-View Transformations for 3D Understanding

Multi-view projection techniques have shown themselves to be highly effective in achieving top-performing results in the recognition of 3D shapes. These methods involve learning how to combine information from multiple view-points. However, the camera view-points from which these views are obtained are often fixed for all shapes. To overcome the static nature of current multi-view techniques, we propose learning these view-points. Specifically, we introduce the Multi-View Transformation Network (MVTN), which uses differentiable rendering to determine optimal view-points for 3D shape recognition. As a result, MVTN can be trained end-to-end with any multi-view network for 3D shape classification. We integrate MVTN into a novel adaptive multi-view pipeline that is capable of rendering both 3D meshes and point clouds. Our approach demonstrates state-of-the-art performance in 3D classification and shape retrieval on several benchmarks (ModelNet40, ScanObjectNN, ShapeNet Core55). Further analysis indicates that our approach exhibits improved robustness to occlusion compared to other methods. We also investigate additional aspects of MVTN, such as 2D pretraining and its use for segmentation. To support further research in this area, we have released MVTorch, a PyTorch library for 3D understanding and generation using multi-view projections.
Fully Probabilistic Design for Optimal Transport

The goal of this paper is to introduce a new theoretical framework for Optimal Transport (OT), using the terminology and techniques of Fully Probabilistic Design (FPD). Optimal Transport is the canonical method for comparing probability measures and has been successfully applied in a wide range of areas (computer vision Rubner et al. [2004], computer graphics Solomon et al. [2015], natural language processing Kusner et al. [2015], etc.). However, we argue that the current OT framework suffers from two shortcomings: first, it is hard to induce generic constraints and probabilistic knowledge in the OT problem; second, the current formalism does not address the question of uncertainty in the marginals, lacking therefore the mechanisms to design robust solutions. By viewing the OT problem as the optimal design of a probability density function with marginal constraints, we prove that OT is an instance of the more generic FPD framework. In this new setting, we can furnish the OT framework with the necessary mechanisms for processing probabilistic constraints and deriving uncertainty quantifiers, hence establishing a new extended framework, called FPD-OT. Our main contribution in this paper is to establish the connection between OT and FPD, providing new theoretical insights for both. This will lay the foundations for the application of FPD-OT in a subsequent work, notably in processing more sophisticated knowledge constraints, as well as in designing robust solutions in the case of uncertain marginals.
A Remark on Lurie's Representability Theorem

In this note we revisit Lurie’s representability theorem for geometric stacks and prove that one of the conditions can be mildly relaxed. The proof uses ideas from Hall–Rydh’s work on the (classical) Artin’s representability theorem. We also spell out a couple of variants of the main theorem and review the relation between deformation theory and homogeneity conditions on prestacks.
Structure‐Based Drug Discovery with Deep Learning **

Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of the deep learning efforts in drug discovery have focused on ligand‐based approaches, structure‐based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding‐mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep‐learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a renaissance in structure‐based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure‐based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.
Optimal Regularization for a Data Source

In optimization-based approaches to inverse problems and to statistical estimation, it is common to augment criteria that enforce data fidelity with a regularizer that promotes desired structural properties in the solution. The choice of a suitable regularizer is typically driven by a combination of prior domain information and computational considerations. Convex regularizers are attractive computationally but they are limited in the types of structure they can promote. On the other hand, nonconvex regularizers are more flexible in the forms of structure they can promote and they have showcased strong empirical performance in some applications, but they come with the computational challenge of solving the associated optimization problems. In this paper, we seek a systematic understanding of the power and the limitations of convex regularization by investigating the following questions: Given a distribution, what is the optimal regularizer for data drawn from the distribution? What properties of a data source govern whether the optimal regularizer is convex? We address these questions for the class of regularizers specified by functionals that are continuous, positively homogeneous, and positive away from the origin. We say that a regularizer is optimal for a data distribution if the Gibbs density with energy given by the regularizer maximizes the population likelihood (or equivalently, minimizes cross-entropy loss) over all regularizer-induced Gibbs densities. As the regularizers we consider are in one-to-one correspondence with star bodies, we leverage dual Brunn-Minkowski theory to show that a radial function derived from a data distribution is akin to a ``computational sufficient statistic'' as it is the key quantity for identifying optimal regularizers and for assessing the amenability of a data source to convex regularization.