Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation Framework for Computer Vision-Based Guidance of the Visually Impaired

— Visually impaired persons have significant problems in their everyday movement. Therefore, some of our previous work involves computer vision in developing assistance systems for guiding the visually impaired in critical situations. Some of those situations includes crosswalks on road crossings and stairs in indoor and outdoor environment. This paper presents an evaluation framework for computer vision-based guiding of the visually impaired persons in such critical situations. Presented framework includes the interface for labeling and storing referent human decisions for guiding directions and compares them to computer vision-based decisions. Since strict evaluation methodology in this research field is not clearly defined and due to the specifics of the transfer of information to visually impaired persons, evaluation criterion for specific simplified guiding instructions is proposed.
The I-Band Tully-Fisher Relation for Sc Galaxies: 21 Centimeter H I Line Data

A compilation of 21 cm line spectral parameters specifically designed for application of the Tully-Fisher (TF) distance method is presented for 1201 spiral galaxies, primarily field Sc galaxies, for which optical I-band photometric imaging is also available. New H I line spectra have been obtained for 881 galaxies. For an additional 320 galaxies, spectra available in a digital archive have been reexamined to allow application of a single algorithm for the derivation of the TF velocity width parameter. A velocity width algorithm is used that provides a robust measurement of rotational velocity and permits an estimate of the error on that width taking into account the effects of instrumental broadening and signal-to-noise. The digital data are used to establish regression relations between measurements of velocity widths using other common prescriptions so that comparable widths can be derived through conversion of values published in the literature. The uniform H I line widths presented here provide the rotational velocity measurement to be used in deriving peculiar velocities via the TF method.
Trajectory-resolved Weiss fields for quantum spin dynamics

We explore the dynamics of quantum spin systems in two and three dimensions using an exact mapping to classical stochastic processes. In recent work we explored the effectiveness of sampling around the mean field evolution as determined by a stochastically averaged Weiss field. Here, we show that this approach can be significantly extended by sampling around the instantaneous Weiss field associated with each stochastic trajectory taken separately. This trajectory-resolved approach incorporates sample to sample fluctuations and allows for longer simulation times. We demonstrate the utility of this approach for quenches in the two-dimensional and three-dimensional quantum Ising model. We show that the method is particularly advantageous in situations where the average Weiss-field vanishes, but the trajectory-resolved Weiss fields are non-zero. We discuss the connection to the gauge-P phase space approach, where the trajectory-resolved Weiss field can be interpreted as a gauge degree of freedom.
Deep Learning-Based Signal Detection for Dual-Mode Index Modulation 3D-OFDM

In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D-OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector.
Universal asymptotic correlation functions for point group C_{6v} and an observation for triangular lattice Q-state Potts model.

We investigate a universal curve in asymptotic correlation functions of off-critical systems that possess C_{6v} symmetry following the argument for C_{4v} symmetry in our previous paper [Phys. Rev. E 102, 032141 (2020)2470-004510.1103/PhysRevE.102. 032141]. Unlike the C_{4v} case, a minimal asymptotic form exists, which contains only two free parameters: the normalization constant and the modulus of the universal curve. We perform large-scale Monte Carlo simulations of the triangular lattice Q-state Potts model above the transition temperature. For Q=1, 2, 3, and 4, we successfully obtain numerical evidence that the minimal form gives the leading asymptotic behavior. We also discuss the possibility that the corrections to the minimal form are expressed using this form as a building block. From the minimal form with optimized parameters, we derive the equilibrium crystal shape of the honeycomb lattice Potts model, which is given by an algebraic curve of genus 1 and is universal among models with C_{6v}. Although the curve differs from those obtained in the C_{4v} case, the latter curves also have genus 1. We indicate that the birational equivalence concept can play an important role in comparing asymptotic forms for different point group symmetries, for example, C_{6v} and C_{4v}.
Safe Reinforcement Learning with Contrastive Risk Prediction

As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL). In this work, we propose a risk preventive training method for safe RL, which learns a statistical contrastive classifier to predict the probability of a state-action pair leading to unsafe states. Based on the predicted risk probabilities, we can collect risk preventive trajectories and reshape the reward function with risk penalties to induce safe RL policies. We conduct experiments in robotic simulation environments. The results show the proposed approach has comparable performance with the state-of-the-art model-based methods and outperforms conventional model-free safe RL approaches.
A unifying view on the irreversible investment exercise boundary in a stochastic, time-inhomogeneous capacity expansion problem

This paper studies the investment exercise boundary erasing in a stochastic, continuous time capacity expansion problem with irreversible investment on the ﬁnite time interval [0 , T ] and a state dependent scrap value associated with the production facility at the ﬁnite horizon T . The capacity process is a time-inhomogeneous diﬀusion in which a monotone nondecreasing, possibly singular, process representing the cumulative investment enters additively. The levels of capacity, employment and operating capital contribute to the ﬁrm’s production and are optimally chosen in order to maximize the expected total discounted proﬁts. Two diﬀerent approaches are employed to study and characterize the boundary. From one side, some ﬁrst order condition are solved by using the Bank and El Karoui Representation Theorem [1], and that sheds further light on the connection between the threshold which the optimal policy of the singular stochastic control problem activates at and the optional solution of Representation Theorem. Its application in the presence of the scrap value is new. It is accomplished by a suitable ﬁctitious extension to + ∞ of the ﬁrm’s horizon and a devise to overcome the diﬃculties due to the presence of a non integral term in the maximizing functional. The optimal investment process is shown to become active at the so-called “base capacity” level, which is given as the unique solution of an integral equation. On the other hand, when the coeﬃcients of the uncontrolled capacity process are deterministic, the optimal stopping problem classically associated to the original capacity problem is resumed and, without invoking variational teckniques but only by means of probabilistic methods, some essential properties of the investment exercise boundary, the “free boundary” of its continuation region, are obtained. Despite the lack of knowledge of boundary’s continuity, the optimal investment process is proved to be continuous, except for a possible initial jump. Finally, unifying approaches and views, the exercise boundary is shown to coincide with the base capacity, and hence it is characterized by an integral equation not requiring any a priori regularity.
Data structures for topologically sound higher-dimensional diagram rewriting

We present a computational implementation of diagrammatic sets, a model of higher-dimensional diagram rewriting that is"topologically sound": diagrams admit a functorial interpretation as homotopies in cell complexes. This has potential applications both in the formalisation of higher algebra and category theory and in computational algebraic topology. We describe data structures for well-formed shapes of diagrams of arbitrary dimensions and provide a solution to their isomorphism problem in time O(n^3 log n). On top of this, we define a type theory for rewriting in diagrammatic sets and provide a semantic characterisation of its syntactic category. All data structures and algorithms are implemented in the Python library rewalt, which also supports various visualisations of diagrams.
Tailoring negative pressure by crystal defects: Crack induced hydride formation in Al alloys

Climate change motivates the search for non-carbon-emitting energy generation and storage solutions. Metal hydrides show promising characteristics for this purpose. They can be further stabilized by tailoring the negative pressure of microstructural and structural defects. Using systematic ab initio and atomistic simulations, we demonstrate that an enhancement in the formation of hydrides at the negatively pressurized crack tip region is feasible by increasing the mechanical tensile load on the specimen. The theoretical predictions have been used to reassess and interpret atom probe tomography experiments for a high-strength 7XXX-aluminium alloy that show a substantial enhancement of hydrogen concentration at structural defects near a stress-corrosion crack tip. These results contain important implications for enhancing the capability of metals as H-storage materials.
Entropy production of nonreciprocal interactions

Non-reciprocal interactions are present in many systems out of equilibrium. The rate of entropy production is a measure that quantifies the time irreversibility of a system, and thus how far it is from equilibrium. In this work, we introduce a non-motile active particle system where activity originates from asymmetric, pairwise interaction forces that result in an injection of energy at the microscopic scale. We calculate stationary correlation functions and entropy production rate in three exactly solvable cases, and analyse a more general case in a perturbation theory as an expansion in weak interactions using a fully microscopic description. Our results show that equilibrium may be recovered by adjusting the diffusion constants despite non-reciprocity, revealing an equivalence in the absolute amplitude of the force and diffusivity. We support our analytical results with numerical simulations.
Deep dive into the IoT backend ecosystem

Internet of Things (IoT) devices are becoming increasingly ubiquitous, e.g., at home, in enterprise environments, and in production lines. To support the advanced functionalities of IoT devices, IoT vendors as well as service and cloud companies operate IoT backends---the focus of this paper. We propose a methodology to identify and locate them by (a) compiling a list of domains used exclusively by major IoT backend providers and (b) then identifying their server IP addresses. We rely on multiple sources, including IoT backend provider documentation, passive DNS data, and active scanning. For analyzing IoT traffic patterns, we rely on passive network flows from a major European ISP. Our analysis focuses on the top IoT backends and unveils diverse operational strategies---from operating their own infrastructure to utilizing the public cloud. We find that the majority of the top IoT backend providers are located in multiple locations and countries. Still, a handful are located only in one country, which could raise regulatory scrutiny as the client IoT devices are located in other regions. Indeed, our analysis shows that up to 35% of IoT traffic is exchanged with IoT backend servers located in other continents. We also find that at least six of the top IoT backends rely on other IoT backend providers. We also evaluate if cascading effects among the IoT backend providers are possible in the event of an outage, a misconfiguration, or an attack.
Cross-modal Learning for Image-Guided Point Cloud Shape Completion

In this paper we explore the recent topic of point cloud completion, guided by an auxiliary image. We show how it is possible to effectively combine the information from the two modalities in a localized latent space, thus avoiding the need for complex point cloud reconstruction methods from single views used by the state-of-the-art. We also investigate a novel weakly-supervised setting where the auxiliary image provides a supervisory signal to the training process by using a differentiable renderer on the completed point cloud to measure fidelity in the image space. Experiments show significant improvements over state-of-the-art supervised methods for both unimodal and multimodal completion. We also show the effectiveness of the weakly-supervised approach which outperforms a number of supervised methods and is competitive with the latest supervised models only exploiting point cloud information.
Embedding formalism for CFTs in general states on curved backgrounds

We present a generalisation of the embedding space formalism to conformal field theories (CFTs) on non-trivial states and curved backgrounds, based on the ambient metric of Fefferman and Graham. The ambient metric is a Lorentzian Ricci-flat metric in $d+2$ dimensions and replaces the Minkowski metric of the embedding space. It is canonically associated with a $d$-dimensional conformal manifold, which is the physical spacetime where the CFT${}_d$ lives. We propose a construction of CFT${}_d$ $n$-point functions in non-trivial states and on curved backgrounds using appropriate geometric invariants of the ambient space as building blocks. This captures the contributions of non-vanishing 1-point functions of multi-stress-energy tensors, at least in holographic CFTs. We apply the formalism to 2-point functions of thermal CFT, finding exact agreement with a holographic computation and expectations based on thermal operator product expansions (OPEs), and to CFTs on squashed spheres where no prior results are known and existing methods are difficult to apply, demonstrating the utility of the method.
The first neural machine translation system for the Erzya language

We present the first neural machine translation system for translation between the endangered Erzya language and Russian and the dataset collected by us to train and evaluate it. The BLEU scores are 17 and 19 for translation to Erzya and Russian respectively, and more than half of the translations are rated as acceptable by native speakers. We also adapt our model to translate between Erzya and 10 other languages, but without additional parallel data, the quality on these directions remains low. We release the translation models along with the collected text corpus, a new language identification model, and a multilingual sentence encoder adapted for the Erzya language. These resources will be available at https://github.com/slone-nlp/myv-nmt.
Exactly solvable piecewise analytic double well potential VD(x) = min[(x + d)2, (x − d)2] and its dual single well potential VS(x) = max[(x + d)2, (x − d)2]

By putting two harmonic oscillator potentials x2 side by side with a separation 2 d, two exactly solvable piecewise analytic quantum systems with a free parameter d > 0 are obtained. Due to the mirror symmetry, their eigenvalues { E} for the even and odd parity sectors are determined exactly as the zeros of certain combinations of the confluent hypergeometric function [Formula: see text] of d and E, which are common to VD and VS but in two different branches. The eigenfunctions are the piecewise square integrable combinations of [Formula: see text], the so-called U functions. By comparing the eigenvalues and eigenfunctions for various values of the separation d, vivid pictures unfold showing the tunneling effects between the two wells.
Multi-robot Implicit Control of Massive Herds

This paper solves the problem of herding countless evaders by means of a few robots. The objective is to steer all the evaders towards a desired tracking reference while avoiding escapes. The problem is very challenging due to the highly complex repulsive evaders' dynamics and the underdetermined states to control. We propose a solution that is based on Implicit Control and a novel dynamic assignment strategy to select the evaders to be directly controlled. The former is a general technique that explicitly computes control inputs even in highly complex input-nonaffine dynamics. The latter is built upon a convex-hull dynamic clustering inspired by the Voronoi tessellation problem. The combination of both allows to choose the best evaders to directly control, while the others are indirectly controlled by exploiting the repulsive interactions among them. Simulations show that massive herds can be herd throughout complex patterns by means of a few herders.
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference

Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict scenarios a several thousand times faster than the original model, making them ideal for policy exploration. We demonstrate that replacing a traditional epidemic model with a learned simulator facilitates robust Bayesian inference.
BP-Im2col: Implicit Im2col Supporting AI Backpropagation on Systolic Arrays*

State-of-the-art systolic array-based accelerators adopt the traditional im2col algorithm to accelerate the inference of convolutional layers. However, traditional im2col cannot efficiently support AI backpropagation. Backpropagation in convolutional layers involves performing transposed convolution and dilated convolution, which usually introduces plenty of zero-spaces into the feature map or kernel. The zero-space data reorganization interfere with the continuity of training and incur additional and non-negligible overhead in terms of off- and on-chip storage, access and performance. Since countermeasures for backpropagation are rarely proposed, we propose BP-im2col, a novel im2col algorithm for AI backpropagation, and implement it in RTL on a TPU-like accelerator. Experiments on TPU-like accelerator indicate that BP-im2col reduces the backpropagation runtime by 34.9% on average, and reduces the bandwidth of off-chip memory and on-chip buffers by at least 22.7% and 70.6% respectively, over a baseline accelerator adopting the traditional im2col. It further reduces the additional storage overhead in the backpropagation process by at least 74.78%.
A generalization of piecewise linear Lyapunov functions

The purpose of this paper is to propose a generalization of piecewise linear Lyapunov functions (PLLFs). In original PLLF candidates, functions are parameterized by hyperplanes, which intersect the stability region and stability conditions are formulated as linear programming problems (LPs) in terms of the parameters inserted by the hyperplanes. The piecewise linear hyperplanes (PLHPs) were introduced to reduce the size of LPs and produce a new class of PLLF candidates; however, applicable systems of this idea were restricted in a certain class of systems. In this paper, this restriction is removed and new PLLF candidates are characterized as the sum of piecewise linear functionals corresponding to PLHPs.
A Simple Framework to Quantify Different Types of Uncertainty in Deep Neural Networks for Image Classification

Quantifying uncertainty in a model's predictions is important as it enables the safety of an AI system to be increased by acting on the model's output in an informed manner. This is crucial for applications where the cost of an error is high, such as in autonomous vehicle control, medical image analysis, financial estimations or legal fields. Deep Neural Networks are powerful predictors that have recently achieved state-of-the-art performance on a wide spectrum of tasks. Quantifying predictive uncertainty in DNNs is a challenging and yet on-going problem. In this paper we propose a complete framework to capture and quantify three known types of uncertainty in DNNs for the task of image classification. This framework includes an ensemble of CNNs for model uncertainty, a supervised reconstruction auto-encoder to capture distributional uncertainty and using the output of activation functions in the last layer of the network, to capture data uncertainty. Finally we demonstrate the efficiency of our method on popular image datasets for classification.
Cubes are dense in $(\infty,\infty)$-categories

We show that the strict 1-category (cid:3) of cubes – deﬁned to be the full subcategory of strict ω -categories whose objects are the Gray tensor powers of the arrow category – are dense in the ( ∞ , 1)-category Cat ω of weak ( ∞ , ∞ )-categories, in both Rezk-complete and incomplete variants. More precisely, we show that Joyal’s category Θ is contained in the idempotent completion of (cid:3) , and in fact that the idempotent completion of (cid:3) is closed under suspensions and wedge sums. This result extends a theorem of Campbell and Maehara in dimension 2. Following Campbell and Maehara’s program, we will in future work apply this result to give a new construction of the Gray tensor product of weak ( ∞ ,n )-categories.
Optical properties of SiV and GeV color centers in nanodiamonds under hydrostatic pressures up to 180 GPa

We investigate the optical properties of silicon-vacancy (SiV) and germanium-vacancy (GeV) color centers in nanodiamonds under hydrostatic pressure up to 180 GPa. The nanodiamonds were synthetized by Si or Ge-doped plasma assisted chemical vapor deposition and, for our experiment, pressurized in a diamond anvil cell. Under hydrostatic pressure we observe blue-shifts of the SiV and GeV zero-phonon lines by 17 THz (70 meV) and 78 THz (320 meV), respectively. These measured pressure induced shifts are in good agreement with ab initio calculations that take into account the lattice compression based on the equation of state of diamond and that are extended to the case of the tin-vacancy (SnV) center. This work provides guidance on the use of group-IV-vacancy centers as quantum sensors under extreme pressures that will exploit their speciﬁc optical and spin properties induced by their intrinsic inversion-symmetric structure.