Miracle-dz/UniFAR-FTUs huggingface dataset:
VpROM: A novel Variational AutoEncoder-boosted Reduced Order Model for the treatment of parametric dependencies in nonlinear systems

Reduced Order Models (ROMs) are of considerable importance in many areas of engineering in which computational time presents difficulties. Established approaches employ projection-based reduction, such as Proper Orthogonal Decomposition. The limitation of the linear nature of such operators is typically tackled via a library of local reduction subspaces, which requires the assembly of numerous local ROMs to address parametric dependencies. Our work attempts to define a more generalisable mapping between parametric inputs and reduced bases for the purpose of generative modeling. We propose the use of Variational Autoencoders (VAEs) in place of the typically utilised clustering or interpolation operations, for inferring the fundamental vectors, termed as modes, which approximate the manifold of the model response for any and each parametric input state. The derived ROM still relies on projection bases, built on the basis of full-order model simulations, thus retaining the imprinted physical connotation. However, it additionally exploits a matrix of coefficients that relates each local sample response and dynamics to the global phenomena across the parametric input domain. The VAE scheme is utilised for approximating these coefficients for any input state. This coupling leads to a high-precision low-order representation, which is particularly suited for problems where model dependencies or excitation traits cause the dynamic behavior to span multiple response regimes. Moreover, the probabilistic treatment of the VAE representation allows for uncertainty quantification on the reduction bases, which may then be propagated to the ROM response. The performance of the proposed approach is validated on an open-source simulation benchmark featuring hysteresis and multi-parametric dependencies, and on a large-scale wind turbine tower characterised by nonlinear material behavior and model uncertainty.
Dynamic Ineffectuality-based Clustered Architectures

The direction of conditional branches is predicted correctly in modern processors with great accuracy. We find several instructions in the dynamic instruction stream that contribute only towards computing the condition of these instructions. Hence, when the predicted direction of conditional branches is indeed correct, these instructions become Ineffectual - the functional state of the program would not be different had these instructions been dropped. However, the execution of ineffectual instructions cannot be avoided altogether because it is possible that the prediction of the branch direction is wrong. In this work, we determine all sources of ineffectuality in an instruction stream such as conditional branches, predicated instructions, indirect jumps and dynamically dead instructions. Then, we propose a technique to steer the ineffectual instructions away from the primary execution cluster so that effectual instructions can execute uncontended. We find that such ineffectuality-based clustering of instructions naturally simplifies the design and avoids several caveats of a clustered architecture. Finally, we propose a technique to detect instances when instructions were incorrectly marked as ineffectual, say due to a branch misprediction, and recover the pipeline. The empirical evaluation of the proposed changes on the SPEC CPU2017 and GAPBS benchmarks show performance uplifts of up to 4.9% and 10.3% on average respectively.
The Score-Difference Flow for Implicit Generative Modeling

Implicit generative modeling (IGM) aims to produce samples of synthetic data matching the characteristics of a target data distribution. Recent work (e.g. score-matching networks, diffusion models) has approached the IGM problem from the perspective of pushing synthetic source data toward the target distribution via dynamical perturbations or flows in the ambient space. In this direction, we present the score difference (SD) between arbitrary target and source distributions as a flow that optimally reduces the Kullback-Leibler divergence between them while also solving the Schroedinger bridge problem. We apply the SD flow to convenient proxy distributions, which are aligned if and only if the original distributions are aligned. We demonstrate the formal equivalence of this formulation to denoising diffusion models under certain conditions. We also show that the training of generative adversarial networks includes a hidden data-optimization sub-problem, which induces the SD flow under certain choices of loss function when the discriminator is optimal. As a result, the SD flow provides a theoretical link between model classes that individually address the three challenges of the"generative modeling trilemma"-- high sample quality, mode coverage, and fast sampling -- thereby setting the stage for a unified approach.
Evaluating Adversarial Robustness on Document Image Classification

Adversarial attacks and defenses have gained increasing interest on computer vision systems in recent years, but as of today, most investigations are limited to images. However, many artificial intelligence models actually handle documentary data, which is very different from real world images. Hence, in this work, we try to apply the adversarial attack philosophy on documentary and natural data and to protect models against such attacks. We focus our work on untargeted gradient-based, transfer-based and score-based attacks and evaluate the impact of adversarial training, JPEG input compression and grey-scale input transformation on the robustness of ResNet50 and EfficientNetB0 model architectures. To the best of our knowledge, no such work has been conducted by the community in order to study the impact of these attacks on the document image classification task.
Improvements of Rain/No-Rain Classification Methods for Microwave Radiometer over Coasts by Dynamic Surface-Type Classification

AbstractThe rain/no-rain classification for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) fails to detect rain over coasts, where the microwave footprint encompasses a mixture of radiometrically cold ocean and radiometrically warm land. A static land–ocean–coast mask is used to determine the surface type of each satellite footprint. The coast mask is conservatively wide to account for the largest footprints, preventing use of the more appropriate ocean or land algorithm for coastal regions.The purpose of this paper is to develop a classification whereby the smallest region possible is defined as coast. In this endeavor, two major improvements are applied to the land–ocean–coast classification. First, the surface classification based on microwave footprints of the high frequency actually used in rain detection is employed. Second, the footprint area of the surface classification is established using an effective field-of-view size and scan geometry of the TMI. These improvements are.
MG-ShopDial: A Multi-Goal Conversational Dataset for e-Commerce

Conversational systems can be particularly effective in supporting complex information seeking scenarios with evolving information needs. Finding the right products on an e-commerce platform is one such scenario, where a conversational agent would need to be able to provide search capabilities over the item catalog, understand and make recommendations based on the user's preferences, and answer a range of questions related to items and their usage. Yet, existing conversational datasets do not fully support the idea of mixing different conversational goals (i.e., search, recommendation, and question answering) and instead focus on a single goal. To address this, we introduce MG-ShopDial: a dataset of conversations mixing different goals in the domain of e-commerce. Specifically, we make the following contributions. First, we develop a coached human-human data collection protocol where each dialogue participant is given a set of instructions, instead of a specific script or answers to choose from. Second, we implement a data collection tool to facilitate the collection of multi-goal conversations via a web chat interface, using the above protocol. Third, we create the MG-ShopDial collection, which contains 64 high-quality dialogues with a total of 2,196 utterances for e-commerce scenarios of varying complexity. The dataset is additionally annotated with both intents and goals on the utterance level. Finally, we present an analysis of this dataset and identify multi-goal conversational patterns.
Patch-Based 3D Natural Scene Generation from a Single Example

We target a 3D generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of having ad hoc designs in presence of varying scene characteristics, renders existing setups intractable. Inspired by classical patch-based image models, we advocate for synthesizing 3D scenes at the patch level, given a single example. At the core of this work lies important algorithmic designs w.r. t the scene representation and generative patch nearest-neighbor module, that address unique challenges arising from lifting classical 2D patch-based framework to 3D generation. These design choices, on a collective level, contribute to a robust, effective, and efficient model that can generate high-quality general natural scenes with both realistic geometric structure and visual appearance, in large quantities and varieties, as demonstrated upon a variety of exemplar scenes. Data and code can be found at http://wyysf-98.github. io/Sin3DGen.
Galactic archeology with extremely metal-poor stars

Abstract Extremely metal-poor (EMP) stars are thought to be formed in the low-mass protogalaxies as building blocks of the Milky Way and can be probes to investigate the early stage of galaxy formation and star formation in the early universe. We study the formation history of EMP stars in the Milky Way halo using a new model of chemical evolution based on the hierarchical theory of the galaxy formation. We construct the merging history of the Milky Way halo based on the extended Press-Schechter formalism, and follow the star formation and chemical evolution along the merger tree. The abundance trends and number of low-mass stars predicted in our model are compared with those of observed EMP stars. Additionally, in order to clarify the origin of hyper metal poor stars, we investigate the change of the surface metal abundances of stars by accretion of interstellar matter. We also investigate the pre-enrichment of intergalactic matter by the first supernovae.
Gravitational lensing of massive particles in the charged NUT spacetime

In astronomy gravitational lensing of light leads to the formation of multiple images, arcs, Einstein rings, and, most important, the shadow of black holes. Analogously in the vicinity of a massive compact object massive particles, following timelike geodesics, are gravitationally lensed. So far gravitational lensing of massive particles was mainly investigated in the weak and strong field limits. In this paper we will, for the first time, investigate exact gravitational lensing of massive particles using the example of the charged NUT metric (and its special cases) which contains three physical parameters, the mass parameter $m$, the electric charge $e$, and the gravitomagnetic charge $n$. We will first discuss and solve the equations of motion for timelike geodesics using elementary and Jacobi's elliptic functions and Legendre's elliptic integrals. Then we will introduce an orthonormal tetrad to relate the $z$ component of the angular momentum and the Carter constant to the energy $E$ of the particles along the timelike geodesics and latitude-longitude coordinates on the celestial sphere of a stationary observer in the domain of outer communication. We will use these relations to derive the angular radius of the particle shadow of the black hole, to formulate an exact lens equation, and to derive the travel time of the particles in terms of the time coordinate and the proper time. Finally, we will discuss the impact of the physical parameters and the energy on observable lensing features.
The impact of directly observed therapy on the efficacy of Tuberculosis treatment: a Bayesian multilevel approach

We propose and discuss a Bayesian procedure to estimate causal effects for multilevel observations in the presence of confounding. This work is motivated by an interest in determining the causal impact of directly observed therapy on the successful treatment of Tuberculosis. We focus on propensity score regression and covariate adjustment to balance the treatment allocation. We discuss the need to include latent local-level random effects in the propensity score model to reduce bias in the estimation of causal effects. A simulation study suggests that accounting for the multilevel nature of the data with latent structures in both the outcome and propensity score models has the potential to reduce bias in the estimation of causal effects.
GAPs for Shallow Implementation of Quantum Finite Automata

Quantum fingerprinting is a technique that maps classical input word to a quantum state. The obtained quantum state is much shorter than the original word, and its processing uses less resources, making it useful in quantum algorithms, communication, and cryptography. One of the examples of quantum fingerprinting is quantum automata algorithms for MOD_p languages, where p is a prime number. However, implementing such an automaton on the current quantum hardware is not efficient. Quantum fingerprinting maps a word of length N to a state of O(log N) qubits, and uses O(N) unitary operations. Computing quantum fingerprint using all available qubits of the current quantum computers is infeasible due to a large number of quantum operations. To make quantum fingerprinting practical, we should optimize the circuit for depth instead of width in contrast to the previous works. We propose explicit methods of quantum fingerprinting based on tools from additive combinatorics, such as generalized arithmetic progressions (GAPs), and prove that these methods provide circuit depth comparable to a probabilistic method. We also compare our method to prior work on explicit quantum fingerprinting methods.
Immunohistochemistry Biomarkers-Guided Image Search for Histopathology

Medical practitioners use a number of diagnostic tests to make a reliable diagnosis. Traditionally, Haematoxylin and Eosin (H&E) stained glass slides have been used for cancer diagnosis and tumor detection. However, recently a variety of immunohistochemistry (IHC) stained slides can be requested by pathologists to examine and confirm diagnoses for determining the subtype of a tumor when this is difficult using H&E slides only. Deep learning (DL) has received a lot of interest recently for image search engines to extract features from tissue regions, which may or may not be the target region for diagnosis. This approach generally fails to capture high-level patterns corresponding to the malignant or abnormal content of histopathology images. In this work, we are proposing a targeted image search approach, inspired by the pathologists’ workflow, which may use information from multiple IHC biomarker images when available. These IHC images could be aligned, filtered, and merged together to generate a composite biomarker image (CBI) that could eventually be used to generate an attention map to guide the search engine for localized search. In our experiments, we observed that an IHC-guided image search engine can retrieve relevant data more accurately than a conventional (i.e., H&E-only) search engine without IHC guidance. Moreover, such engines are also able to accurately conclude the subtypes through majority votes.
Growth and decay of isolated turbulent band in Plane-Couette flow

The transition of plane Couette flow is numerically investigated in a large computational domain. It is found that the averaged period of the transient growth reduces slowly with the decrease of the Reynolds number (Re) except when Re is close to a threshold value of 286. During the decay process, the band contracts from its both ends with a statistical constant velocity, but keeps its center, width, and tilt angle statistically unchanged. For self-sustained turbulent band, three growth styles are observed. At moderate Re, the isolated band extends obliquely as what happens in plane Poiseuille flow. With the increase of Re, transverse split occurs and the band breaks into several attached segments, forming a shape of `F'. Further increasing Re leads to a longitudinal split, i.e. the isolated band becomes wider at first and then splits into two parallel bands.
Queue Routing Strategies to Improve Equitable Housing Coordination in New York City

Runaway and homeless youth (RHY) are a group of youth and young adults who are at high risk of being exploited through human trafficking. Although access to housing and support services is an effective way to decrease their vulnerability to being exploited, research reveals that coordination of these services provided to RHY by non-profit and government organizations is neither standardized, nor efficient. This situation often causes decreased, delayed, and inequitable access to these scarce housing resources. In this study, we aim to increase the housing system efficiency and reduce the barriers that are contributing to inequitable access to housing through simulation modeling and analyses. Specifically, we simulate a set of crisis and emergency shelters in New York City, funded by a single governmental organization, considering a queuing network with pools of multiple parallel servers, servers with demographic eligibility criteria, stochastic RHY arrival, impatient youth behaviour (possibility of abandonment), and a decision-maker (coordinator) that determines which server pool RHY is routed to. This simulation allows us to evaluate the impact of different queue routing strategies. Our simulation results show that by changing the way RHY is routed to shelters, we can reduce the average wait time by approximately a day and decrease the proportion of RHY abandoning the shelters by 13%.
Expand-and-Cluster: Exact Parameter Recovery of Neural Networks

Can we recover the hidden parameters of an Artiﬁcial Neural Network (ANN) by probing its input-output mapping? We propose a systematic method, called ‘Expand-and-Cluster’ that needs only the number of hidden layers and the activation function of the probed ANN to identify all network parameters. In the expansion phase, we train a series of student networks of increasing size using the probed data of the ANN as a teacher. Expansion stops when a minimal loss is consistently reached in student networks of a given size. In the clustering phase, weight vectors of the expanded students are clustered, which allows structured pruning of superﬂuous neurons in a principled way. We ﬁnd that an overparameterization of a factor four is sufﬁcient to reliably identify the minimal number of neurons and to retrieve the original network parameters in 80% of tasks across a family of 150 toy problems of variable difﬁculty. Furthermore, a teacher network trained on MNIST data can be identiﬁed with less than 5% overhead in the neuron number. Thus, while direct training of a student network with a size identical to that of the teacher is practically impossible because of the non-convex loss function, training with mild overparameterization followed by clustering and structured pruning correctly identiﬁes the target network.
A twisted additive divisor problem

We give asymptotics for shifted convolutions of the form $$\sum_{n<X} \frac{\sigma_{2u}(n,\chi)\sigma_{2v}(n+k,\psi)}{n^{u+v}}$$ for nonzero complex numbers $u,v$ and nontrivial Dirichlet characters $\chi,\psi$. We use the technique of"automorphic regularization"to find the spectral decomposition of a combination of Eisenstein series which is not obviously square-integrable. The error term we obtain is in some cases smaller than what the method we use typically yields.
Semigroup algorithmic problems in metabelian groups

We consider semigroup algorithmic problems in finitely generated metabelian groups. Our paper focuses on three decision problems introduced by Choffrut and Karhum\"{a}ki (2005): the Identity Problem (does a semigroup contain a neutral element? ), the Group Problem (is a semigroup a group? ) and the Inverse Problem (does a semigroup contain the inverse of a generator? ). We show that all three problems are decidable for finitely generated sub-semigroups of finitely generated metabelian groups. In particular, we establish a correspondence between polynomial semirings and sub-semigroups of metabelian groups using an interaction of graph theory, convex polytopes, algebraic geometry and number theory. Since the Semigroup Membership problem (does a semigroup contain a given element? ) is known to be undecidable in finitely generated metabelian groups, our result completes the decidability characterization of semigroup algorithmic problems in metabelian groups.
Adaptive Collective Responses to Local Stimuli in Anonymous Dynamic Networks

We develop a framework for self-induced phase changes in programmable matter in which a collection of agents with limited computational and communication capabilities can collectively perform appropriate global tasks in response to local stimuli that dynamically appear and disappear. Agents reside on graph vertices, where each stimulus is only recognized locally, and agents communicate via token passing along edges to alert other agents to transition to an"aware"state when stimuli are present and an"unaware"state when the stimuli disappear. We present an Adaptive Stimuli Algorithm that is robust to competing waves of messages as multiple stimuli change, possibly adversarially. Moreover, in addition to handling arbitrary stimulus dynamics, the algorithm can handle agents reconfiguring the connections (edges) of the graph over time in a controlled way. As an application, we show how this Adaptive Stimuli Algorithm on reconfigurable graphs can be used to solve the foraging problem, where food sources may be discovered, removed, or shifted at arbitrary times. We would like the agents to consistently self-organize using only local interactions, such that if the food remains in position long enough, the agents transition to a gather phase, collectively forming a single large component with small perimeter around the food. Alternatively, if no food source has existed recently, the agents should self-induce a switch to a search phase in which they distribute themselves randomly throughout the lattice region to search for food. Unlike previous approaches to foraging, this process is indefinitely repeatable. Like a physical phase change, microscopic changes such as the deletion or addition of a single food source triggers these macroscopic, system-wide transitions as agents share information about the environment and respond locally to get the desired collective response.