Miracle-dz/UniFAR-FTUs huggingface dataset:
A Binded VAE for Inorganic Material Generation

Designing new industrial materials with desired properties can be very expensive and time consuming. The main difficulty is to generate compounds that correspond to realistic materials. Indeed, the description of compounds as vectors of components' proportions is characterized by discrete features and a severe sparsity. Furthermore, traditional generative model validation processes as visual verification, FID and Inception scores are tailored for images and cannot then be used as such in this context. To tackle these issues, we develop an original Binded-VAE model dedicated to the generation of discrete datasets with high sparsity. We validate the model with novel metrics adapted to the problem of compounds generation. We show on a real issue of rubber compound design that the proposed approach outperforms the standard generative models which opens new perspectives for material design optimization.
An Investigation of Image Deblurring Techniques

The Image Deblurring problem seems to be one of perpetual actuality. The research started in the Second World War for military purposes and continued until today proposing numerous techniques for a huge palette of applications, all aiming at recovering the original signal from a blurred one. The current paper synthesizes most of the efforts carried over the time, constructively comparing available approaches and offering a high degree of up-to-date completeness over this vast research subject.
Learning Bounded Context-Free-Grammar via LSTM and the Transformer: Difference and Explanations

Long Short-Term Memory (LSTM) and Transformers are two popular neural architectures used for natural language processing tasks. Theoretical results show that both are Turing-complete and can represent any context-free language (CFL). In practice, it is often observed that Transformer models have better representation power than LSTM. But the reason is barely understood. We study such practical differences between LSTM and Transformer and propose an explanation based on their latent space decomposition patterns. To achieve this goal, we introduce an oracle training paradigm, which forces the decomposition of the latent representation of LSTMand the Transformer and supervises with the transitions of the Pushdown Automaton (PDA) of the corresponding CFL. With the forced decomposition, we show that the performance upper bounds of LSTM and Transformer in learning CFL are close: both of them can simulate a stack and perform stack operation along with state transitions. However, the absence of forced decomposition leads to the failure of LSTM models to capture the stack and stack operations, while having a marginal impact on the Transformer model. Lastly, we connect the experiment on the prototypical PDA to a real-world parsing task to re-verify the conclusions
Test Particle Energization of Heavy Ions in Magnetohydrodynamic Turbulence

In the present work, we study the energization and displacement of heavy ions through the use of test particles interacting with the electromagnetic fields of magnetohydrodynamic (MHD) turbulence. These fields are obtained from pseudospectral direct numerical solutions of the compressible three-dimensional MHD equations with a strong background magnetic field. We find particle energization to be predominantly perpendicular as the ions become heavier (lower charge-to-mass ratio) and that high displacement is detrimental for perpendicular energization. On the other hand, perpendicular displacement is unaffected by the charge-to-mass ratio, which we explain with a simple guide center model. Using Voronoi tessellation along with this model, we analyze preferential concentration and find that particles behave as tracers in the perpendicular plane, clustering in regions with ∇⊥ · u ⊥ < 0. These regions also have (∇ × E ) z < 0, which is optimal for perpendicular energization, thus providing a mechanism to understand precedent results.
Learning Reward Machines: A Study in Partially Observable Reinforcement Learning

Reinforcement learning (RL) is a central problem in artificial intelligence. This problem consists of defining artificial agents that can learn optimal behaviour by interacting with an environment -- where the optimal behaviour is defined with respect to a reward signal that the agent seeks to maximize. Reward machines (RMs) provide a structured, automata-based representation of a reward function that enables an RL agent to decompose an RL problem into structured subproblems that can be efficiently learned via off-policy learning. Here we show that RMs can be learned from experience, instead of being specified by the user, and that the resulting problem decomposition can be used to effectively solve partially observable RL problems. We pose the task of learning RMs as a discrete optimization problem where the objective is to find an RM that decomposes the problem into a set of subproblems such that the combination of their optimal memoryless policies is an optimal policy for the original problem. We show the effectiveness of this approach on three partially observable domains, where it significantly outperforms A3C, PPO, and ACER, and discuss its advantages, limitations, and broader potential.
Canonical systems and quantum graphs

Ju ′ = −zHu of arbitrary order. Next, canonical systems on graphs, not necessarily compact but with finitely many vertices, are introduced and proved to be unitarily equivalent to certain higher order canonical systems. It is shown that any Schrödinger operator on a graph is unitarily equivalent to a canonical system on the same graph. Consequently, for an arbitrary canonical system or Schrödinger operator on a graph, a representation of the resolvent as an integral operator and a spectral representation are obtained.
Internet of Bio-Nano Things: A Review of Applications, Enabling Technologies and Key Challenges

Internet of Bio-Nano Things (IoBNT) is envisioned to be a heterogeneous network of nanoscale and biological devices, so called Bio-Nano Things (BNTs), communicating via non-conventional means, e.g., molecular communications (MC), in non-conventional environments, e.g., inside human body. The main objective of this emerging networking framework is to enable direct and seamless interaction with biological systems for accurate sensing and control of their dynamics in real time. This close interaction between bio and cyber domains with unprecedentedly high spatio-temporal resolution is expected to open up vast opportunities to devise novel applications, especially in healthcare area, such as intrabody continuous health monitoring. There are, however, substantial challenges to be overcome if the enormous potential of the IoBNT is to be realized. These range from developing feasible nanocommunication and energy harvesting techniques for BNTs to handling the big data generated by IoBNT. In this survey, we attempt to provide a comprehensive overview of the IoBNT framework along with its main components and applications. An investigation of key technological challenges is presented together with a detailed review of the state-of-the-art approaches and a discussion of future research directions.
Morphisms and minimization of weighted automata

This paper studies the algorithms for the minimisation of weighted automata. It starts with the deﬁnition of morphisms — which generalises and uniﬁes the notion of bisimulation to the whole class of weighted automata — and the unicity of a minimal quotient for every automaton, obtained by partition reﬁnement. From a general scheme for the reﬁnement of partitions, two strategies are considered for the computation of the minimal quotient: the Domain Split and the Predecesor Class Split algorithms. They correspond respectivly to the classical Moore and Hopcroft algorithms for the computation of the minimal quotient of deterministic Boolean automata. We show that these two strategies yield algorithms with the same quadratic complexity and we study the cases when the second one can be improved in order to achieve a complexity similar to the one of Hopcroft algorithm.
Sequential decision making for a class of hidden Markov processes, application to medical treatment optimisation

Motivated by a medical decision making problem, this paper focuses on an impulse control problem for a class of piecewise deterministic semi-Markov processes. The process evolves deterministically between jumps and the inter-jump times have a general distribution. The discrete coordinate (e.g. global health state of the patient) is not observed, the continuous one (e.g. result of some blood measurement) is observed with noise at some (possibly scarce) observation times. The objective is to optimally select the observation dates while controlling the process so that it remains close to a nominal value. At each visit to the medical center, a cancer patient undergoes possibly invasive analyses, and treatment and next visit dates are scheduled according to their result and the patient history. Frequent observations lead to a better estimation of the hidden state of the process but may be too costly for the center and/or patient. Rare observations may lead to undetected possibly lethal degradation of the patient’s health. We exhibit an explicit policy close to optimality based on discretisations of the process. Construction of discretisation grids are discussed at length. The paper is illustrated with experiments on synthetic data fitted from the Intergroupe Francophone du Myélome 2009 clinical trial.
Dialog+ in Broadcasting: First Field Tests Using Deep-Learning-Based Dialogue Enhancement

Difficulties in following speech due to loud background sounds are common in broadcasting. Object-based audio, e.g., MPEG-H Audio solves this problem by providing a user-adjustable speech level. While object-based audio is gaining momentum, transitioning to it requires time and effort. Also, lots of content exists, produced and archived outside the object-based workflows. To address this, Fraunhofer IIS has developed a deep-learning solution called Dialog+, capable of enabling speech level personalization also for content with only the final audio tracks available. This paper reports on public field tests evaluating Dialog+, conducted together with Westdeutscher Rundfunk (WDR) and Bayerischer Rundfunk (BR), starting from September 2020. To our knowledge, these are the first large-scale tests of this kind. As part of one of these, a survey with more than 2,000 participants showed that 90% of the people above 60 years old have problems in understanding speech in TV “often” or “very often”. Overall, 83% of the participants liked the possibility to switch to Dialog+, including those who do not normally struggle with speech intelligibility. Dialog+ introduces a clear benefit for the audience, filling the gap between objectbased broadcasting and traditionally produced material.
Mobility Styles and Travel Behavior: Application of a Lifestyle Approach to Leisure Travel

Recently, several efforts have been made to include individuals’ attitudes, values, and orientations within travel behavior research. The restrictions of the traditional modeling variables become obvious when one is trying to explain changes in travel patterns related to changes in Western societies. Traditional modeling efforts also neglect the intrinsic dimensions of traveling and of nonwork travel. A lifestyle approach has been used to construct mobility styles by applying factor and cluster analysis to leisure and mobility orientations. A data set was collected in four neighborhoods of Cologne (Germany). The analysis focuses on leisure travel on weekends, because it is assumed that mobility styles are more important for leisure than for work-related traveling. The key question is whether the utility of mobility styles as a tool for the analysis of travel behavior can be demonstrated. The results indicate, first, that there is a correlation of mobility styles with several other factors, such as personal and household characteristics, availability of transport modes, and urban form elements (e.g., residential neighborhood, garden ownership, and dwelling type). Furthermore, a correlation is observed between mobility styles and participation, frequency, and car use for leisure travel. Finally, multivariate analysis indicates that the mobility styles largely explain the participation in traveling for different leisure purposes and the distances traveled by car. For the mode choice, other factors such as availability of transport modes and workday use of a transport mode appear to be more important than the mobility style.
Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders

We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders. Reverse engineering from a raw geometry to a CAD model is an essential task to enable manipulation of the 3D data in shape editing software and thus expand their usages in many downstream applications. Particularly, the form of CAD models having a sequence of extrusion cylinders - a 2D sketch plus an extrusion axis and range - and their boolean combinations is not only widely used in the CAD community/software but also has great expressivity of shapes, compared to having limited types of primitives (e.g., planes, spheres, and cylinders). In this work, we introduce a neural network that solves the extrusion cylinder decomposition problem in a geometry-grounded way by first learning underlying geometric proxies. Precisely, our approach first predicts per-point segmentation, base/barrel labels and normals, then estimates for the underlying extrusion parameters in differentiable and closed-form formulations. Our experiments show that our approach demonstrates the best performance on two recent CAD datasets, Fusion Gallery and DeepCAD, and we further showcase our approach on reverse engineering and editing.
A model for calculating volumes of trains as flows given demand and capacity restriction

This report presents a novel approach to calculate volumes of trains from a set of requirements describing the transportation demands. The model presented is based on a multi commodity network flow model. The model comprises three different layers: the demand level in which the requirements on the transports are presented, the route layer which describes the routes possible to implement the demands and the flow layer in which the resulting flows of train volumes are calculated and distributed over discrete time periods. This structure corresponds well to other industrial planning processes where different products or services that are to be accomplished are first decomposed by methods into different layers of tasks, by e.g. a work breakdown structure (WBS). Resources are then allocated to the tasks as well as scheduled in time. The most important feature of the proposed model is that it is volume based, i.e. volume of trains are handled instead of scheduling individual trains. Scheduling a timetable is a very complex task, and by concentrating on the volumes of trains over time rather than the detailed scheduling of them, the complexity is greatly reduced which is a large computational advantage. The disadvantage is that the representation of capacity in terms of volumes of trains with different properties becomes crucial. The report discusses these advantages and disadvantages as well as gives two examples of the use of the model. 1 ar X iv :2 11 2. 09 50 7v 1 [ m at h. O C ] 1 7 D ec 2 02 1
Records professionals: an invisible profession in Malaysia

Purpose – This study seeks to examine the existence of records professionals and their role in Malaysian organizations.Design/methodology/approach – The study compares case studies done in six government‐controlled companies. Interviews were conducted with the persons handling the records and information (including records in electronic environment) in the organizations. The interviews were then transcribed and analyzed manually to obtain the comparative data from each case.Findings – The findings indicate that there are no “real” records professionals in Malaysian organizations. The responsibility and accountability of documenting and managing the organizations' records are dispersed and decentralized. Records managers as a profession are not established within the Malaysian organization structures. Generally there is no specific position of “records manager” in Malaysian organizations. However, the “archivists” are confined at the National Archives and there are no such positions at any of the case orga.
Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level heterogeneity in the distribution of covariates through density ratio weighting. To safely incorporate source sites and avoid negative transfer, we introduce an adaptive weighting procedure via a penalized regression, which achieves both consistency and optimal efficiency. Our strategy is communication-efficient and privacy-preserving, allowing participating sites to share summary statistics only once with other sites. We conduct both theoretical and numerical evaluations of FACE and apply it to conduct a comparative effectiveness study of BNT162b2 (Pfizer) and mRNA-1273 (Moderna) vaccines on COVID-19 outcomes in U.S. veterans using electronic health records from five VA regional sites. We show that compared to traditional methods, FACE meaningfully increases the precision of treatment effect estimates, with reductions in standard errors ranging from $26\%$ to $67\%$.
Deep Bayesian Learning for Car Hacking Detection

With the rise of self-drive cars and connected vehicles, cars are equipped with various devices to assistant the drivers or support self-drive systems. Undoubtedly, cars have become more intelligent as we can deploy more and more devices and software on the cars. Accordingly, the security of assistant and self-drive systems in the cars becomes a life-threatening issue as smart cars can be invaded by malicious attacks that cause traffic accidents. Currently, canonical machine learning and deep learning methods are extensively employed in car hacking detection. However, machine learning and deep learning methods can easily be overconfident and defeated by carefully designed adversarial examples. Moreover, those methods cannot provide explanations for security engineers for further analysis. In this work, we investigated Deep Bayesian Learning models to detect and analyze car hacking behaviors. The Bayesian learning methods can capture the uncertainty of the data and avoid overconfident issues. Moreover, the Bayesian models can provide more information to support the prediction results that can help security engineers further identify the attacks. We have compared our model with deep learning models and the results show the advantages of our proposed model. The code of this work is publicly available
SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers

We propose a novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements (such as rains, snow, and moire patterns) that vary in successive frames. It has three stages: transformer pre-training, zero-shot restoration, and hard patch refinement. Using the pre-trained transformers, our model is able to tell the motion difference between the true image information and the obstructing elements. For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders. Each transformer has a temporal attention layer and several self-attention layers, to capture both temporal and spatial information of multiple frames. Only self-supervisedly pre-trained on the denoising task, SiamTrans is tested on three different low-level vision tasks (deraining, demoireing, and desnowing). Compared with related methods, SiamTrans achieves the best performances, even outperforming those with supervised learning.
Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits

Amina Asif, Kashif Rajpoot, David Snead, Fayyaz Minhas, Nasir Rajpoot †Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, UK ‡Department of Computer Science, University of Birmingham, UK §Department of Pathology, University Hospitals Coventry & Warwickshire, UK ¶The Alan Turing Institute, UK *Corresponding Author: Nasir Rajpoot. Tel: +44(24)7657-3795; Email: N.M. Rajpoot@warwick.ac. uk
Online Generalized Additive Model

Additive models and generalized additive models are effective semiparametric tools for multidimensional data. In this article we propose an online smoothing backfitting method for generalized additive models with local polynomial smoothers. The main idea is to use a second order expansion to approximate the nonlinear integral equations to maximize the local quasilikelihood and store the coefficients as the sufficient statistics which can be updated in an online manner by a dynamic candidate bandwidth method. The updating procedure only depends on the stored sufficient statistics and the current data block. We derive the asymptotic normality as well as the relative efficiency lower bounds of the online estimates, which provides insight into the relationship between estimation accuracy and computational cost driven by the length of candidate bandwidth sequence. Simulations and real data examples are provided to validate our findings.
Proposal for distribution of multi-photon entanglement with TF-QKD rate-distance scaling

We propose a protocol to perform long-range distribution of near-maximally entangled multiphoton states, allowing versatile applications such as quantum key distribution (QKD) and quantum metrology which can provide alternatives to state-of-the-art protocols. Our scheme uses resources available within the current integrated quantum photonic technology: squeezed vacuum states and photon-number-resolving detectors. The distributed entanglement can be certified by Bell tests which have the potential to be loophole free, and may be directly used in well established QKD protocols. Generally, this provides measurement-device-independent (MDI) levels of security, which may be upgraded to fully device-independent (DI) security if the Bell test is loophole-free. In both cases, the protocol is robust to extremely high transmission losses, matching the O(√η) scaling of key rate with channel transmittance as in the celebrated twin-field (TF) scheme.
Streaming query evaluation with constant delay enumeration over nested documents

Some of the most relevant document schemas used online, such as XML and JSON, have a nested format. In the last decade, the task of extracting data from nested documents over streams has become especially relevant. We focus on the streaming evaluation of queries with outputs of varied sizes over nested documents. With this in mind, we model queries of this kind as Visibly Pushdown Transducers (VPT), a computational model that extends visibly pushdown automata with outputs and has the same expressive power as MSO over nested documents. Since processing a document through a VPT can generate a massive number of results, we are interested in reading the input in a streaming fashion and enumerating the outputs one after another as eﬃciently as possible, namely, with constant-delay. This paper describes an algorithm that enumerates these elements with constant-delay after processing the document stream in a single pass. Furthermore, we show that this algorithm is worst-case optimal in terms of update-time per symbol and memory usage. 2012 ACM Subject Classiﬁcation Theory of computation → Database theory.