Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuum of metastable conical states of monoaxial chiral helimagnets

At low temperature and zero applied magnetic field, besides the equilibrium helical state, monoaxial chiral helimagnets have a continuum of helical states differing by the wave number of the modulation. The wave number of these states in units of the equilibrium state wave number is denoted here by p, and accordingly the corresponding states are called the p-states. In this work we study in detail the metastability of the p-states. The application of an external magnetic field in the direction of the chiral axis has a double effect: on one hand, it introduces a conical deformation of the p-states, and on the other hand it destabilizes some of them, shrinking the range of p in which the p-states are metastable. If a polarized current is applied along the chiral axis, the p-states reach a steady moving state with a constant velocity proportional to the current intensity. Besides this dynamical effect, the polarized current also induces a conical deformation and reduces the range of stability of the p-states. The stability diagram in the plane applied field - applied current intensity has interesting features that, among other things, permit the manipulation of p-states by a combination of applied fields and currents. These features can be exploited to devise processes to switch between p-states. In particular there are p-states with negative p, opening the possibility to helicity switching. The theoretical feasibility of such processes, crucial from the point of view of applications, is shown by micromagnetic simulations. Analogous $p$-states exists in cubic chiral helimagnets and therefore similar effects are expected in those systems.
HGIB: Prognosis for Alzheimer's Disease via Hypergraph Information Bottleneck

Alzheimer's disease prognosis is critical for early Mild Cognitive Impairment patients for timely treatment to improve the patient's quality of life. Whilst existing prognosis techniques demonstrate potential results, they are highly limited in terms of using a single modality. Most importantly, they fail in considering a key element for prognosis: not all features extracted at the current moment may contribute to the prognosis prediction several years later. To address the current drawbacks of the literature, we propose a novel hypergraph framework based on an information bottleneck strategy (HGIB). Firstly, our framework seeks to discriminate irrelevant information, and therefore, solely focus on harmonising relevant information for future MCI conversion prediction e.g., two years later). Secondly, our model simultaneously accounts for multi-modal data based on imaging and non-imaging modalities. HGIB uses a hypergraph structure to represent the multi-modality data and accounts for various data modality types. Thirdly, the key of our model is based on a new optimisation scheme. It is based on modelling the principle of information bottleneck into loss functions that can be integrated into our hypergraph neural network. We demonstrate, through extensive experiments on ADNI, that our proposed HGIB framework outperforms existing state-of-the-art hypergraph neural networks for Alzheimer's disease prognosis. We showcase our model even under fewer labels. Finally, we further support the robustness and generalisation capabilities of our framework under both topological and feature perturbations.
PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients

The traditional framework of federated learning (FL) requires each client to re-train their models in every iteration, making it infeasible for resource-constrained mobile devices to train deep-learning (DL) models. Split learning (SL) provides an alternative by using a centralized server to offload the computation of activations and gradients for a subset of the model but suffers from problems of slow convergence and lower accuracy. In this paper, we implement PFSL, a new framework of distributed split learning where a large number of thin clients perform transfer learning in parallel, starting with a pre-trained DL model without sharing their data or labels with a central server. We implement a lightweight step of personalization of client models to provide high performance for their respective data distributions. Furthermore, we evaluate performance fairness amongst clients under a work fairness constraint for various scenarios of non-i.i. d. data distributions and unequal sample sizes. Our accuracy far exceeds that of current SL algorithms and is very close to that of centralized learning on several real-life benchmarks. It has a very low computation cost compared to FL variants and promises to deliver the full benefits of DL to extremely thin, resource-constrained clients.
Minimum Wage Pass-through to Wholesale and Retail Prices: Evidence from Cannabis Scanner Data

A growing empirical literature finds that firms pass the cost of minimum wage hikes onto consumers via higher retail prices. Yet, little is known about minimum wage effects on wholesale prices and whether retailers face a wholesale cost shock in addition to the labor cost shock. I exploit the vertically disintegrated market structure of Washington state's legal recreational cannabis industry to investigate minimum wage pass-through to wholesale and retail prices. In a difference-in-differences with continuous treatment framework, I utilize scanner data on $6 billion of transactions across the supply chain and leverage geographic variation in firms' minimum wage exposure across six minimum wage hikes between 2018 and 2021. When ignoring wholesale cost effects, I find retail pass-through elasticities consistent with existing literature -- yet retail pass-through elasticities more than double once wholesale cost effects are accounted for. Retail markups do not adjust to the wholesale cost shock, indicating a full pass-through of the wholesale cost shock to retail prices. The results highlight the importance of analyzing the entire supply chain when evaluating the product market effects of minimum wage hikes.
Forecasting COVID-19 Case Counts Based on 2020 Ontario Data

Objective: To develop machine learning models that can predict the number of COVID-19 cases per day given the last 14 days of environmental and mobility data. Approach: COVID-19 data from four counties around Toronto, Ontario, were used. Data were prepared into daily records containing the number of new COVID case counts, patient demographic data, outdoor weather variables, indoor environment factors, and human movement based on cell mobility and public health restrictions. This data was analyzed to determine the most important variables and their interactions. Predictive models were developed using CNN and LSTM deep neural network approaches. A 5-fold chronological cross-validation approach used these methods to develop predictive models using data from Mar 1 to Oct 14 2020, and test them on data covering Oct 15 to Dec 24 2020. Results: The best LSTM models forecasted tomorrow's daily COVID case counts with 90.7% accuracy, and the 7-day rolling average COVID case counts with 98.1% accuracy using independent test data. The best models to forecast the next 7 days of daily COVID case counts did so with 79.4% accuracy over all days. Models forecasting the 7-day rolling average case counts had a mean accuracy of 83.6% on the same test set. Conclusions: Our findings point to the importance of indoor humidity for the transmission of a virus such as COVID-19. During the coldest portions of the year, when humans spend greater amounts of time indoors or in vehicles, air quality drops within buildings, most significantly indoor relative humidity levels. Moderate to high indoor temperatures coupled with low IRH (below 20%) create conditions where viral transmission is more likely because water vapour ejected from an infected person's mouth can remain longer in the air because of evaporation and dry skin conditions, particularly in a recipient's airway, promotes transmission.
Convergence Guarantees of Overparametrized Wide Deep Inverse Prior

Neural networks have become a prominent approach to solve inverse problems in recent years. Amongst the different existing methods, the Deep Image/Inverse Priors (DIPs) technique is an unsupervised approach that optimizes a highly overparametrized neural network to transform a random input into an object whose image under the forward model matches the observation. However, the level of overparametrization necessary for such methods remains an open problem. In this work, we aim to investigate this question for a two-layers neural network with a smooth activation function. We provide overparametrization bounds under which such network trained via continuous-time gradient descent will converge exponentially fast with high probability which allows to derive recovery prediction bounds. This work is thus a first step towards a theoretical understanding of overparametrized DIP networks, and more broadly it participates to the theoretical understanding of neural networks in inverse problem settings.
Localized surface plasmon resonance: nano-sinusoid arrays

A new nano-sinusoid shape has recently been proposed, which offers the advantage of more resonance wavelength tunability than that offered by other sharp-tip nano-particles. In this paper, a one-dimensional (1D) chain of the nano-sinusoids is modelled, and results are compared with those describing chains of nano-triangles and nano-diamonds. It is demonstrated that the chain of nano-sinusoids provides more enhancement at hot spots than other examined nano-particle shapes. This enhancement is analytically quantified using the coupling constant values used in the electrostatic eigenmode method for analytically solving Maxwell's equations for the nano-plasmonic devices. In addition, investigating LSPR spectrum of two-dimensional (2D) arrays of NPs demonstrates existence of enhanced surface electric fields on hot spots of the outer rows of the array.
Rotating without Seeing: Towards In-hand Dexterity through Touch

Tactile information plays a critical role in human dexterity. It reveals useful contact information that may not be inferred directly from vision. In fact, humans can even perform in-hand dexterous manipulation without using vision. Can we enable the same ability for the multi-finger robot hand? In this paper, we present Touch Dexterity, a new system that can perform in-hand object rotation using only touching without seeing the object. Instead of relying on precise tactile sensing in a small region, we introduce a new system design using dense binary force sensors (touch or no touch) overlaying one side of the whole robot hand (palm, finger links, fingertips). Such a design is low-cost, giving a larger coverage of the object, and minimizing the Sim2Real gap at the same time. We train an in-hand rotation policy using Reinforcement Learning on diverse objects in simulation. Relying on touch-only sensing, we can directly deploy the policy in a real robot hand and rotate novel objects that are not presented in training. Extensive ablations are performed on how tactile information help in-hand manipulation.Our project is available at https://touchdexterity.github. io.
VR-SLAM: A Visual-Range Simultaneous Localization and Mapping System using Monocular Camera and Ultra-wideband Sensors

In this work, we propose a simultaneous localization and mapping (SLAM) system using a monocular camera and Ultra-wideband (UWB) sensors. Our system, referred to as VRSLAM, is a multi-stage framework that leverages the strengths and compensates for the weaknesses of each sensor. Firstly, we introduce a UWB-aided 7 degree-of-freedom (scale factor, 3D position, and 3D orientation) global alignment module to initialize the visual odometry (VO) system in the world frame defined by the UWB anchors. This module loosely fuses up-to-scale VO and ranging data using either a quadratically constrained quadratic programming (QCQP) or nonlinear least squares (NLS) algorithm based on whether a good initial guess is available. Secondly, we provide an accompanied theoretical analysis that includes the derivation and interpretation of the Fisher Information Matrix (FIM) and its determinant. Thirdly, we present UWBaided bundle adjustment (UBA) and UWB-aided pose graph optimization (UPGO) modules to improve short-term odometry accuracy, reduce long-term drift as well as correct any alignment and scale errors. Extensive simulations and experiments show that our solution outperforms UWB/camera-only and previous approaches, can quickly recover from tracking failure without relying on visual relocalization, and can effortlessly obtain a global map even if there are no loop closures.
Structure-Based Experimental Datasets for Benchmarking of Protein Simulation Force Fields

This review article provides an overview of structurally oriented, experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss why these observables are useful for assessing force field accuracy, how they can be calculated from simulation trajectories, and statistical issues that arise when comparing simulations with experiment. The target audience for this article is computational researchers and trainees who develop, benchmark, or use protein force fields for molecular simulations.
This Was (Not) Intended: How Intent Communication and Biometrics Can Enhance Social Interactions With Robots

Socially Assistive Robots (SARs) are robots that are designed to replicate the role of a caregiver, coach, or teacher, providing emotional, cognitive, and social cues to support a specific group. SARs are becoming increasingly prevalent, especially in elderly care. Effective communication, both explicit and implicit, is a critical aspect of human-robot interaction involving SARs. Intent communication is necessary for SARs to engage in effective communication with humans. Biometrics can provide crucial information about a person's identity or emotions. By linking these biometric signals to the communication of intent, SARs can gain a profound understanding of their users and tailor their interactions accordingly. The development of reliable and robust biometric sensing and analysis systems is critical to the success of SARs. In this work, we focus on four different aspects to evaluate the communication of intent involving SARs, existing works, and our outlook on future works and applications.
Lattice Green functions for pedestrians: Exponential decay

The exponential decay of lattice Green functions is one of the main technical ingredients of the Ba{\l}aban's approach to renormalization. We give here a self-contained proof, whose various ingredients were scattered in the literature. The main sources of exponential decay are the Combes-Thomas method and the analyticity of the Fourier transforms. They are combined using a renormalization group equation and the method of images.
Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control

Automated anesthesia promises to enable more personalized and precise anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most critical aspects of surgical care for patients. Current research has typically focused on creating simulated environments from which agents can learn. These approaches have demonstrated good experimental results, but are still far from clinical application. In this paper, Policy Constraint Q-Learning (PCQL), a data-driven reinforcement learning algorithm for solving the problem of learning strategies on real world anesthesia data, is proposed. Conservative Q-Learning is first introduced to alleviate the problem of Q function overestimation in an offline context. A policy constraint term is then added to agent training to keep the action distribution of the agent and the anesthesiologist consistent, ensuring the agent makes safer decisions in anesthesia scenarios. The effectiveness of PCQL was validated by extensive experiments on a real clinical anesthesia dataset we collected. Experimental results show that PCQL is predicted to achieve higher gains than the baseline approaches while maintaining good agreement with the reference dose given by the anesthesiologist, using less total dose, and being more responsive to the patient's vital signs. In addition, the confidence intervals of the agent were investigated and were founded to cover most of the clinical decisions of the anesthesiologist. Finally, an interpretable method, SHAP, was used to analyze the contributing components of the model predictions, increasing the transparency of the model.
GraVoS: Voxel Selection for 3D Point-Cloud Detection

3D object detection within large 3D scenes is challenging not only due to the sparsity and irregularity of 3D point clouds, but also due to both the extreme foreground-background scene imbalance and class imbalance. A common approach is to add ground-truth objects from other scenes. Differently, we propose to modify the scenes by removing elements (voxels), rather than adding ones. Our approach selects the “meaningful” voxels, in a manner that addresses both types of dataset imbalance. The approach is general and can be applied to any voxel-based detector, yet the meaningfulness of a voxel is network-dependent. Our voxel selection is shown to improve the performance of several prominent 3D detection methods.
Quantum dynamics of floppy molecular systems with ELVIBROT and TNUM

The description of the dynamics of polyatomic molecules provides a significant challenge to molecular theorists. For floppy molecular systems the use of curvilinear coordinates (z-matrix, polyspherical.) enables one to perform the dynamics more efficiently. The complexity of the kinetic energy operator is hidden behind a numerical and exact description using TNUM. Furthermore, for molecules with a large number of atoms, the number of internal degrees of freedom makes the reduction of dimensionality a requirement that cannot be ignored. Several physical models of increasing accurateness based on a separation between n slow degrees of freedom (active modes) and m fast ones (inactive modes) can be used (constraint models, adiabatic approximation, coupled adiabatic channels or exact models). All those models are implemented in the fortran code ELVIBROT without built-in limitation. This program allows simulations of vibrational spectra of floppy molecular systems (methanol, ammonia.) as well as time-depend.
Evolution of the number and temperature of the remaining cold atoms in CW-laser photoionization of laser-cooled $^{87}$Rb atoms

Based on the Rb$^+$-Rb hybrid trap, we investigate the effect of ion-atom elastic collisions on the number and temperature of the remaining atoms. We measured the remaining atomic number and temperature as a function of the wavelength and intensity of the ionization laser, and whether the ion trap was turned on. Fittings with a single exponential decay function plus an offset to the number and radius of the remaining atoms are found to be in good agreement. We found a difference in the exponential factor of different wavelengths of ionization laser with the ion trap on or off. We suppose that the presence of electrons affects ion-atom collisions through disorder-induced heating. Our research contributes to a better understanding of how ultracold neutral plasma evolves, particularly the subsequent kinetics of atomic processes, which also serves as a useful reference for high-energy-density plasma.
Training Deep Boltzmann Networks with Sparse Ising Machines

The slowing down of Moore's law has driven the development of unconventional computing paradigms, such as specialized Ising machines tailored to solve combinatorial optimization problems. In this paper, we show a new application domain for probabilistic bit (p-bit) based Ising machines by training deep generative AI models with them. Using sparse, asynchronous, and massively parallel Ising machines we train deep Boltzmann networks in a hybrid probabilistic-classical computing setup. We use the full MNIST and Fashion MNIST (FMNIST) dataset without any downsampling and a reduced version of CIFAR-10 dataset in hardware-aware network topologies implemented in moderately sized Field Programmable Gate Arrays (FPGA). For MNIST, our machine using only 4,264 nodes (p-bits) and about 30,000 parameters achieves the same classification accuracy (90%) as an optimized software-based restricted Boltzmann Machine (RBM) with approximately 3.25 million parameters. Similar results follow for FMNIST and CIFAR-10. Additionally, the sparse deep Boltzmann network can generate new handwritten digits and fashion products, a task the 3.25 million parameter RBM fails at despite achieving the same accuracy. Our hybrid computer takes a measured 50 to 64 billion probabilistic flips per second, which is at least an order of magnitude faster than superficially similar Graphics and Tensor Processing Unit (GPU/TPU) based implementations. The massively parallel architecture can comfortably perform the contrastive divergence algorithm (CD-n) with up to n = 10 million sweeps per update, beyond the capabilities of existing software implementations. These results demonstrate the potential of using Ising machines for traditionally hard-to-train deep generative Boltzmann networks, with further possible improvement in nanodevice-based realizations.
Performance evolution of Nb/sub 3/Sn cable-in-conduit conductors under cyclic load [for Tokamaks]

Two subsize, Nb/sub 3/Sn cable-in-conduit conductors, are tested as short samples in the SULTAN test facility. Transverse load is applied by sweeping up and down the operating current in high background field. A serious performance loss of I/sub c/ and n-factor is observed upon cyclic load, with a drop of the critical current, I/sub c/, larger than 30%. The results are critically discussed and compared to similar findings from the literature. The impact of the cyclic load on the Nb/sub 3/Sn CICC performance must be carefully evaluated in the magnet design.
Characterizing Nexus of Similarity within Knowledge Bases: A Logic-based Framework and its Computational Complexity Aspects

Similarities between entities occur frequently in many real-world scenarios. For over a century, researchers in different fields have proposed a range of approaches to measure the similarity between entities. More recently, inspired by"Google Sets", significant academic and commercial efforts have been devoted to expanding a given set of entities with similar ones. As a result, existing approaches nowadays are able to take into account properties shared by entities, hereinafter called nexus of similarity. Accordingly, machines are largely able to deal with both similarity measures and set expansions. To the best of our knowledge, however, there is no way to characterize nexus of similarity between entities, namely identifying such nexus in a formal and comprehensive way so that they are both machine- and human-readable; moreover, there is a lack of consensus on evaluating existing approaches for weakly similar entities. As a first step towards filling these gaps, we aim to complement existing literature by developing a novel logic-based framework to formally and automatically characterize nexus of similarity between tuples of entities within a knowledge base. Furthermore, we analyze computational complexity aspects of this framework.