Miracle-dz/UniFAR-FTUs huggingface dataset:
CHANGE-OVER DESIGNS

The experiments in which each experimental unit receives some or all of the treatments, one at a time, over a number of periods are known as change-over designs (CODs). These designs are also called cross-over designs, switch-over trials, time-series designs, before-after designs, repeated measurements designs, designs involving sequences of treatments, etc. These designs have been used advantageously in several fields of research, notably in nutrition experiments with dairy cattle, clinical trials in medical research, psychological experiments, long-term agricultural field experiments and bioequivalence trials. Some aspects of CODs are discussed here. Some methods of constructing these designs considering direct and first order residual effects have been described. Besides this, some methods of construction of incomplete sequence balanced CODs for direct and first order residual effects are also presented.
Reinforcement Learning for General LTL Objectives Is Intractable

In recent years, researchers have made signiﬁcant progress in devising reinforcement-learning algorithms for optimizing linear temporal logic (LTL) objectives and LTL-like objectives. Despite these advancements, there are fundamental limitations to how well this problem can be solved that previous studies have alluded to but, to our knowledge, have not examined in depth. In this paper, we address theoretically the hardness of learning with general LTL objectives. We formalize the problem under the probably approximately correct learning in Markov decision processes (PAC-MDP) framework, a standard framework for measuring sample complexity in reinforcement learning. In this formalization, we prove that the optimal policy for any LTL formula is PAC-MDP-learnable only if the formula is in the most limited class in the LTL hierarchy, consisting of only ﬁnite-horizon-decidable properties. Practically, our result implies that it is impossible for a reinforcement-learning algorithm to obtain a PAC-MDP guarantee on the performance of its learned policy after ﬁnitely many interactions with an unconstrained environment for non-ﬁnite-horizon-decidable LTL objectives.
How does AI play football? An analysis of RL and real-world football strategies

Recent advances in reinforcement learning (RL) have made it possible to develop sophisticated agents that excel in a wide range of applications. Simulations using such agents can provide valuable information in scenarios that are difficult to scientifically experiment in the real world. In this paper, we examine the play-style characteristics of football RL agents and uncover how strategies may develop during training. The learnt strategies are then compared with those of real football players. We explore what can be learnt from the use of simulated environments by using aggregated statistics and social network analysis (SNA). As a result, we found that (1) there are strong correlations between the competitiveness of an agent and various SNA metrics and (2) aspects of the RL agents play style become similar to real world footballers as the agent becomes more competitive. We discuss further advances that may be necessary to improve our understanding necessary to fully utilise RL for the analysis of football.
Ultrafast control of spin-orbital separation probed with time-resolved RIXS

Quasi-one-dimensional systems exhibit many-body effects elusive in higher dimensions. A prime example is spin-orbital separation, which has been measured by resonant inelastic X-ray scattering (RIXS) in Sr2CuO3. Here, we theoretically analyze the time-resolved RIXS spectrum of Sr2CuO3 under the action of a timedependent electric field. We show that the external field can reversibly modify the parameters in the effective t − J model used to describe spinon and orbiton dynamics in the material. For strong driving amplitudes, we find that the spectrum changes qualitatively as a result of reversing the relative spinon to orbiton velocity. The analysis shows that in general, the spin-orbital dynamics in Mott insulators in combination with time-resolved RIXS should provide a suitable platform to explore the reversible control of many-body physics in the solid with strong laser fields.
Multi-Modality Microscopy Image Style Transfer for Nuclei Segmentation

Annotating microscopy images for nuclei segmentation is laborious and time-consuming. To leverage the few existing annotations, also across multiple modalities, we propose a novel microscopy-style augmentation technique based on a generative adversarial network (GAN). Unlike other style transfer methods, it can not only deal with different cell assay types and lighting conditions, but also with different imaging modalities, such as bright-field and fluorescence microscopy. Using disentangled representations for content and style, we can preserve the structure of the original image while altering its style during augmentation. We evaluate our data augmentation on the 2018 Data Science Bowl dataset consisting of various cell assays, lighting conditions, and imaging modalities. With our style augmentation, the segmentation accuracy of the two top-ranked Mask R-CNN-based nuclei segmentation algorithms in the competition increases significantly. Thus, our augmentation technique renders the downstream task more robust to the test data heterogeneity and helps counteract class imbalance without resampling of minority classes.
Intuitive Shape Editing in Latent Space

The use of autoencoders for shape editing or generation through latent space manipulation suffers from unpredictable changes in the output shape. Our autoencoder-based method enables intuitive shape editing in latent space by disentangling latent sub-spaces into style variables and control points on the surface that can be manipulated independently. The key idea is adding a Lipschitz-type constraint to the loss function, i.e. bounding the change of the output shape proportionally to the change in latent space, leading to interpretable latent space representations. The control points on the surface that are part of the latent code of an object can then be freely moved, allowing for intuitive shape editing directly in latent space. We evaluate our method by comparing to state-of-the-art data-driven shape editing methods. We further demonstrate the expressiveness of our learned latent space by leveraging it for unsupervised part segmentation.
dCSR: A Memory-Efficient Sparse Matrix Representation for Parallel Neural Network Inference

Reducing the memory footprint of neural networks is a crucial prerequisite for deploying them in small and low-cost embedded devices. Network parameters can often be reduced significantly through pruning. We discuss how to best represent the indexing overhead of sparse networks for the coming generation of Single Instruction, Multiple Data (SIMD)-capable microcontrollers. From this, we develop Delta-Compressed Storage Row (dCSR), a storage format for sparse matrices that allows for both low overhead storage and fast inference on embedded systems with wide SIMD units. We demonstrate our method on an ARM Cortex-M55 MCU prototype with M-Profile Vector Extension (MVE). A comparison of memory consumption and throughput shows that our method achieves competitive compression ratios and increases throughput over dense methods by up to $2.9\times$ for sparse matrix-vector multiplication (SpMV)-based kernels and $1.06\times$ for sparse matrix-matrix multiplication (SpMM). This is accomplished through handling the generation of index information directly in the SIMD unit, leading to an increase in effective memory bandwidth.
Proposed Framework for State-Based, Data-Driven Models for Improved Transportation Operations

Numerous transportation analysis and modeling tools have been developed over the last 30 years, and the data collection for these models was a personnel-intensive process requiring significant resources. As a result, there typically were insufficient amounts of data to verify transportation models under diverse operating conditions. However, with the proliferation of sensors to support intelligent transportation systems, researchers now find themselves in a data glut and an information shortage. With this data overload, there is a need to shift the modeling focus toward a state-based modeling approach. Fundamental to this is the need to explore the question of what constitutes a parsimonious state definition that effectively captures the dynamics of a large-scale, regional transportation system. Discussed are a proposed framework for a state-based modeling paradigm and, specifically, two key concepts to this new approach: case-based reasoning and simulation for model generation.
Donnan equilibrium revisited: Coupling between ion concentrations, osmotic pressure, and donnan potential

This paper discusses the little-known fact that Donnan’s equilibrium criteria established over 100 years ago neglected the coupling between ion concentrations and the osmotic pressure. Such coupling can be treated based on general thermodynamic considerations including the solvent equilibrium, leading to a membrane potential that consists of not only the classical Donnan potential term but also an additional term due to the osmotic pressure, and the existence of a membrane potential even when the impermeable species are not charged. This coupled treatment conflicts with the well-established Poisson–Boltzmann equation and Nernst–Planck equation, but is consistent with the extension of these equations including the solvent effects by Freise and Schlogl, enables us to view the electrical double layer equilibrium as Donnan equilibrium.
Completing correlation matrices

We describe a way to complete a correlation matrix that is not fully specified. Such matrices often arise in financial applications when the number of stochastic variables becomes large or when several smaller models are combined in a larger model. We argue that the proper completion to consider is the matrix that maximizes the entropy of the distribution described by the matrix. We then give a way to construct this matrix starting from the graph associated with the incomplete matrix. If this graph is chordal our construction will result in a proper correlation matrix. We give a detailed description of the construction for a cross-currency model with six stochastic variables and describe extensions to larger models involving more currencies.
Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing

Deep learning based virtual try-on system has achieved some encouraging progress recently, but there still remain several big challenges that need to be solved, such as trying on arbitrary clothes of all types, trying on the clothes from one category to another and generating image-realistic results with few artifacts. To handle this issue, we in this paper first collect a new dataset with all types of clothes, i.e., tops, bottoms, and whole clothes, each one has multiple categories with rich information of clothing characteristics such as patterns, logos, and other details. Based on this dataset, we then propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for all-type clothes, which can synthesize realistic try-on images by preserving and trading off characteristics of the target clothes and the reference person. Our approach includes three modules: 1) Limbs Prediction Module, which is utilized for predicting the human body parts by preserving the characteristics of the reference person. This is especially good for handling cross-category try-on task (e.g., long sleeves ↔ short sleeves or long pants ↔ skirts, etc.), where the exposed arms or legs with the skin colors and details can be reasonably predicted; 2) Improved Geometric Matching Module, which is designed to warp clothes according to the geometry of the target person. We improve the TPS based warping method with a compactly supported radial function (Wendland’s Ψ-function); 3) Trade-Off Fusion Module, which is to trade off the characteristics of the warped clothes and the reference person. This module is to make the generated try-on images look more natural and realistic based on a fine-tune symmetry of the network structure. Extensive simulations are conducted and our approach can achieve better performance compared with the state-of-the-art virtual try-on methods.
Portfolio Optimisation With Options

We develop a new analysis for portfolio optimisation with options, tackling the three fundamental issues with this problem: asymmetric options' distributions, high dimensionality and dependence structure. To do so, we propose a new dependency matrix, built upon conditional probabilities between options' payoffs, and show how it can be computed in closed form given a copula structure of the underlying asset prices. The empirical evidence we provide highlights that this approach is efficient, fast and easily scalable to large portfolios of (mixed) options.
Solution group representations as quantum symmetries of graphs

In 2019, Aterias et al. constructed pairs of quantum isomorphic, non‐isomorphic graphs from linear constraint systems. This article deals with quantum automorphisms and quantum isomorphisms of colored versions of those graphs. We show that the quantum automorphism group of such a colored graph is the dual of the homogeneous solution group of the underlying linear constraint system. Given a vertex‐ and edge‐colored graph with certain properties, we construct an uncolored graph that has the same quantum automorphism group as the colored graph we started with. Using those results, we obtain the first‐known example of a graph that has quantum symmetry and finite quantum automorphism group. Furthermore, we construct a pair of quantum isomorphic, non‐isomorphic graphs that both have no quantum symmetry.
Dictionary-Based Low-Rank Approximations and the Mixed Sparse Coding Problem

Constrained tensor and matrix factorization models allow to extract interpretable patterns from multiway data. Therefore crafting efficient algorithms for constrained low-rank approximations is nowadays an important research topic. This work deals with columns of factor matrices of a low-rank approximation being sparse in a known and possibly overcomplete basis, a model coined as Dictionary-based Low-Rank Approximation (DLRA). While earlier contributions focused on finding factor columns inside a dictionary of candidate columns, i.e., one-sparse approximations, this work is the first to tackle DLRA with sparsity larger than one. I propose to focus on the sparse-coding subproblem coined Mixed Sparse-Coding (MSC) that emerges when solving DLRA with an alternating optimization strategy. Several algorithms based on sparse-coding heuristics (greedy methods, convex relaxations) are provided to solve MSC. The performance of these heuristics is evaluated on simulated data. Then, I show how to adapt an efficient MSC solver based on the LASSO to compute Dictionary-based Matrix Factorization and Canonical Polyadic Decomposition in the context of hyperspectral image processing and chemometrics. These experiments suggest that DLRA extends the modeling capabilities of low-rank approximations, helps reducing estimation variance and enhances the identifiability and interpretability of estimated factors.
From Kepler to Newton: the Role of Explainable AI in Science Discovery

The research paradigm of the Observation–Hypothesis–Prediction–Experimentation loop has been practiced by researchers for years towards scientiﬁc discovery. However, with the data explosion in both mega-scale and milli-scale scientiﬁc research, it has been sometimes very difﬁcult to manually analyze the data and propose new hypothesis to drive the cycle for scientiﬁc discovery. In this paper, we introduce an Explainable AI-assisted paradigm for science discovery. The key is to use Explainable AI (XAI) to help derive data or model interpretations and science discoveries. We show how computational and data-intensive methodology—together with experimental and theoretical methodology—can be seamlessly integrated for scientiﬁc research. To demonstrate the AI-assisted science discovery process, and to pay our respect to some of the greatest minds in human history, we show how Kepler’s laws of planetary motion and Newton’s law of universal gravitation can be rediscovered by (explainable) AI based on Tycho Brahe’s astronomical observation data, whose works were leading the scientiﬁc revolution in the 16-17th century. This work also highlights the importance of Explainable AI (as compared to black-box AI) in science discovery to help humans prevent or better prepare for the possible technological singularity which may happen in the future.
Attention-Based Dual-Stream Vision Transformer for Radar Gait Recognition

Radar gait recognition is robust to light variations and less infringement on privacy. Previous studies often utilize either spectrograms or cadence velocity diagrams. While the former shows the time-frequency patterns, the latter encodes the repetitive frequency patterns. In this work, a dual-stream net-work with attention-based fusion is proposed to fully aggregate the discriminant information from these two representations. Both streams are analyzed through the Vision Trans-former, which well captures the gait characteristics embedded in these representations. The proposed method is validated on a large benchmark dataset for radar gait recognition, showing that it significantly outperforms state-of-the-art solutions.
Modelo de regressão Weibull para estudar dados de falha de equipamentos de sub-superfície em poços petrolíferos

This work presents a reliability study conducted among on-shore oil fields in the Potiguar Basin (RN/CE) belonging to PETROBRAS, Brazil. The main study objective was to verify the existence of a relationship between oil pump lifetime and some characteristics such as the elevation method, the amount of water produced in the well (BSW), the gas/oil ratio (RGO), the depth of the production pump, the oil field operational unit, among others. The study was based on a retrospective sample of 450 oil columns from all that were functioning between 2000 and 2006. Statistical hypothesis tests under a Weibull regression model fitted to the first failure data allowed the selection of some covariates in the set considered to explain the failure time in the oil pumps.
A unity power factor multiple isolated outputs switching mode power supply using a single switch

The authors present an offline switching power supply with multiple isolated outputs and unity power factor with the use of only one power processing stage, based on the DC-DC SEPIC (single ended primary inductance converter) modulated by variable hysteresis current control. The principle of operation, the theoretical analysis, the design procedure, an example, and simulation results are presented. A laboratory prototype, rated at 160 W, operating at a maximum switching frequency of 100 kHz, with isolated outputs rated at +5 V/15 A, -5 V/1 A, +12 V/6 A and -12 V/1 A, has been built given an input power factor near unity. <<ETX>>
Sustainability in the built environment using embedded technology

Innovation of sustainable products and solutions in the built environment using embedded technology in Constructions, is from various earlier investigations shown to increase the value both by reducing emissions of green-house gasses from buildings and by optimising the comfort of living condition for the end-user. Based on a project on User-driven Innovation and Embedded Technology in Construction, this paper presents different potential products and solutions for sustainability. This covers a variety of areas such as recycling, energy efficiency, as well as a new concept of sustainable products designated Open Built Source products (OBS), by applying two principle: compatibility and reuse of building products with embedded technology. The project is carried out by DI and SBi and involves 30 firms namely building materials manufacturers and IT developer companies. The methods used in the User-driven Innovation will be presented, with focus on user engagement, interest and acceptance of the ideas arising from the process. This will be exemplified by a developed pilot project involving embedded technology in a building material. Sustainability is categorised in the three dimensions environmental, social and economic sustainability. In this paper a fourth sustainability field will be introduced; The adoption of sustainability. User adoption of sustainable solution is not only achieved by developing the solutions, the user also needs to adopt the solution, before it may be implemented in practise. Several barriers need to be taken into consideration, such as usability, functionality and value for the user, technology fear, cultural and social backgrounds, when developing sustainable solutions. The paper presents a new approach in dealing with User-driven innovation as a mean for developing sustainable solutions. The methodology will be analysed, exemplified by innovated products and show how sustainable solutions may be implemented much more efficiently, by applying this new approach.
Respiratory disorders of Parkinson's Disease.

Parkinson's disease (PD) is characterized by the progressive loss of dopaminergic neurons in the substantia nigra, mainly affecting people over 60 years of age. Patients develop both classic symptoms (tremors, muscle rigidity, bradykinesia and postural instability) and nonclassical symptoms (orthostatic hypotension, neuropsychiatric deficiency, sleep disturbances and respiratory disorders). Thus, patients with PD can have a significantly impaired quality of life, especially when they do not have multi-modality therapeutic follow-up. The respiratory alterations associated with this syndrome are the main cause of mortality in PD. They can be classified as peripheral when caused by disorders of the upper airways or muscles involved in breathing and as central when triggered by functional deficits of important neurons located in the brainstem and involved in respiratory control. Currently, there is little research describing these disorders, and therefore, there is no well-established knowledge about the subject, making the treatment of patients with respiratory symptoms difficult. In this review, the history of the pathology and data about the respiratory changes in PD obtained thus far will be addressed.
Equipoise, drug development, and biliary cancer

INTRODUCTION Clinical equipoise describes a “state of genuine uncertainty” in the community of providers regarding superior treatment for a given clinical scenario and the clinician’s ability to ethically investigate potential therapies as a result of that uncertainty. Equipoise becomes particularly relevant in the case of new and emerging treatment options in lifethreatening disease, where the demand for something that is better, albeit unproven, is paramount. This is especially true for many solid tumors for which standard of care therapy has limited efficacy. Current investigational therapy for biliary cancers including cholangiocarcinoma and gallbladder cancer provides profound insight into the complexities of equipoise.