Miracle-dz/UniFAR-FTUs huggingface dataset:
N-glycans mannosylation controls T-cell development by reprogramming thymocyte commitment, fate and repertoire

T-cell development is a major physiological process occurring in complex organisms, that ensures the formation of T-cell receptors diverse repertoire, to recognize antigens throughout the life of the organism. The thymus offers a microenvironment for efficient development, where progenitor populations go through maturation steps, which, when not regulated, are associated with disease onset, including autoimmune disorders and cancer. Glycosylation is a major post-translational modification that occurs in virtually all cells, including T lymphocytes, regulating receptor-turnover, affinity and signaling. However, there is a missing knowledge on how glycans regulate lymphocyte development and their impact in T-cell functions. We discovered stage-specific glycosylation profiles in human and murine thymocyte populations. Thereafter, we generated two glycoengineered mouse models displaying N-glycosylation pathway deficiencies, at early DN stages, lacking the Mgat1 or Mgat2 genes. We demonstrated remarkable defects in key T-cell developmental stages, such as {beta}- and DP-selection, natural regulatory T-cell generation, {gamma}{delta} T development/differentiation and thymic egression, in Mgat1-deficient thymocytes, indicating a pathogenic role of mannose N-glycans in development. We also demonstrated that a single N-glycan antenna (modelled in Mgat2-deficient thymocytes) is sufficient to rescue key developmental processes. In conclusion, we demonstrated that mannosylated thymocytes render a dysregulation in T-cell development, associated with disease.
Design Practitioner Perception in Determining the Terminology of Biomimicry, Anthropomorphised and Semantic (BAS) in Design Application

This paper focuses on the perception of the design practitioner about the terminology design of Biomimicry, Anthropomorphism and Semantic (BAS). This paper also shows the level of understanding design practitioner in evaluating and reviewing the terminology in design. In the world of design, the various elements incorporated into each design to meet the demand and give effect in developing products. For creative designer, ideas, creativity and in-depth knowledge of design can help them in producing designs and elements needed. As a designer, they have own perception in the interpretation of a design. This paper also discusses the level of understanding of the design practitioners in evaluating and reviewing the terms of design. The analysis results also clearly show the level of understanding of the design practitioner in identifying terms BAS.
Learning Representation from Neural Fisher Kernel with Low-rank Approximation

In this paper, we study the representation of neural networks from the view of kernels. We first define the Neural Fisher Kernel (NFK), which is the Fisher Kernel applied to neural networks. We show that NFK can be computed for both supervised and unsupervised learning models, which can serve as a unified tool for representation extraction. Furthermore, we show that practical NFKs exhibit low-rank structures. We then propose an efficient algorithm that computes a low rank approximation of NFK, which scales to large datasets and networks. We show that the low-rank approximation of NFKs derived from unsupervised generative models and supervised learning models gives rise to high-quality compact representations of data, achieving competitive results on a variety of machine learning tasks.
A Practical Guide to the Numerical Implementation of Tensor Networks I: Contractions, Decompositions, and Gauge Freedom

We present an overview of the key ideas and skills necessary to begin implementing tensor network methods numerically, which is intended to facilitate the practical application of tensor network methods for researchers that are already versed with their theoretical foundations. These skills include an introduction to the contraction of tensor networks, to optimal tensor decompositions, and to the manipulation of gauge degrees of freedom in tensor networks. The topics presented are of key importance to many common tensor network algorithms such as DMRG, TEBD, TRG, PEPS, and MERA.
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD

This paper compares three feature representation techniques used to represent resting state functional magnetic resonance (fMRI) scans. The proposed models of feature representation consider the time averaged fMRI scans as raw representation of image data. The effectiveness of the representation is evaluated by using these features for classification of Attention Deficit Hyperactivity Disorder (ADHD) patients from healthy controls. The dimensionality reduction methods used for feature representation are maximum-variance unfolding, locally linear embedding and auto-encoders. The classifiers tested for classification purpose were neural net and support vector machine. Using auto-encoders with four hidden layers along with a support vector machine classifier yielded a classification accuracy of 61.25% along with 65.69% sensitivity and 52.20% specificity.
Learning from a Learning User for Optimal Recommendations

In real-world recommendation problems, especially those with a formidably large item space, users have to gradually learn to estimate the utility of any fresh recommendations from their experience about previously consumed items. This in turn affects their interaction dynamics with the system and can invalidate previous algorithms built on the omniscient user assumption. In this paper, we formalize a model to capture such"learning users"and design an efficient system-side learning solution, coined Noise-Robust Active Ellipsoid Search (RAES), to confront the challenges brought by the non-stationary feedback from such a learning user. Interestingly, we prove that the regret of RAES deteriorates gracefully as the convergence rate of user learning becomes worse, until reaching linear regret when the user's learning fails to converge. Experiments on synthetic datasets demonstrate the strength of RAES for such a contemporaneous system-user learning problem. Our study provides a novel perspective on modeling the feedback loop in recommendation problems.
Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space

We demonstrate for the first time that ill-conditioned, non-smooth, constrained distributions in very high dimension, upwards of 100,000, can be sampled efficiently $\textit{in practice}$. Our algorithm incorporates constraints into the Riemannian version of Hamiltonian Monte Carlo and maintains sparsity. This allows us to achieve a mixing rate independent of smoothness and condition numbers. On benchmark data sets in systems biology and linear programming, our algorithm outperforms existing packages by orders of magnitude. In particular, we achieve a 1,000-fold speed-up for sampling from the largest published human metabolic network (RECON3D). Our package has been incorporated into the COBRA toolbox.
Graphical criteria for the identification of marginal causal effects in continuous-time survival and event-history analyses

A BSTRACT . We consider continuous-time survival or more general event-history settings, where the aim is to infer the causal effect of a time-dependent treatment process. This is formalised as the effect on the outcome event of a (possibly hypothetical) intervention on the intensity of the treatment process, i.e. a stochastic intervention. To establish whether valid inference about the interventional situation can be drawn from typical observational, i.e. non-experimental, data we propose graphical rules indicating whether the observed information is sufﬁcient to identify the desired causal effect by suitable re-weighting. In analogy to the well-known causal directed acyclic graphs, the corresponding dynamic graphs combine causal semantics with local independence models for multivariate counting processes. Importantly, we highlight that causal inference from censored data requires structural assumptions on the censoring process beyond the usual independent censoring assumption, which can be represented and veriﬁed graphically. Our results establish general non-parametric identiﬁability and do not rely on particular survival models. We illustrate our proposal with a data example on HPV-testing for cervical cancer screening, where the desired effect is estimated by re-weighted cumulative incidence curves.
Rethinking Explainability as a Dialogue: A Practitioner's Perspective

As practitioners increasingly deploy machine learning models in critical domains such as health care, finance, and policy, it becomes vital to ensure that domain experts function effectively alongside these models. Explainability is one way to bridge the gap between human decision-makers and machine learning models. However, most of the existing work on explainability focuses on one-off, static explanations like feature importances or rule lists. These sorts of explanations may not be sufficient for many use cases that require dynamic, continuous discovery from stakeholders. In the literature, few works ask decision-makers about the utility of existing explanations and other desiderata they would like to see in an explanation going forward. In this work, we address this gap and carry out a study where we interview doctors, healthcare professionals, and policymakers about their needs and desires for explanations. Our study indicates that decision-makers would strongly prefer interactive explanations in the form of natural language dialogues. Domain experts wish to treat machine learning models as"another colleague", i.e., one who can be held accountable by asking why they made a particular decision through expressive and accessible natural language interactions. Considering these needs, we outline a set of five principles researchers should follow when designing interactive explanations as a starting place for future work. Further, we show why natural language dialogues satisfy these principles and are a desirable way to build interactive explanations. Next, we provide a design of a dialogue system for explainability and discuss the risks, trade-offs, and research opportunities of building these systems. Overall, we hope our work serves as a starting place for researchers and engineers to design interactive explainability systems.
Weighted Isolation and Random Cut Forest Algorithms for Anomaly Detection

Random cut forest (RCF) algorithms have been developed for anomaly detection, particularly in time series data. The RCF algorithm is an improved version of the isolation forest (IF) algorithm. Unlike the IF algorithm, the RCF algorithm can determine whether real-time input contains an anomaly by inserting the input into the constructed tree network. Various RCF algorithms, including Robust RCF (RRCF), have been developed, where the cutting procedure is adaptively chosen probabilistically. The RRCF algorithm demonstrates better performance than the IF algorithm, as dimension cuts are decided based on the geometric range of the data, whereas the IF algorithm randomly chooses dimension cuts. However, the overall data structure is not considered in both IF and RRCF, given that split values are chosen randomly. In this paper, we propose new IF and RCF algorithms, referred to as the weighted IF (WIF) and weighted RCF (WRCF) algorithms, respectively. Their split values are determined by considering the density of the given data. To introduce the WIF and WRCF, we first present a new geometric measure, a density measure, which is crucial for constructing the WIF and WRCF. We provide various mathematical properties of the density measure, accompanied by theorems that support and validate our claims through numerical examples.
Complex-to-Real Random Features for Polynomial Kernels

Kernel methods are ubiquitous in statistical modeling due to their theoretical guarantees as well as their competitive empirical performance. Polynomial kernels are of particular importance as their feature maps model the interactions between the dimensions of the input data. However, the construction time of explicit feature maps scales exponentially with the polynomial degree and a naive application of the kernel trick does not scale to large datasets. In this work, we propose Complex-to-Real (CtR) random features for polynomial kernels that leverage intermediate complex random projections and can yield kernel estimates with much lower variances than their real-valued analogs. The resulting features are real-valued, simple to construct and have the following advantages over the state-of-the-art: 1) shorter construction times, 2) lower kernel approximation errors for commonly used degrees, 3) they enable us to obtain a closed-form expression for their variance.
Danish Airs and Grounds: A Dataset for Aerial-to-Street-Level Place Recognition and Localization

Place recognition and visual localization are particularly challenging in wide baseline configurations. In this letter, we contribute with the Danish Airs and Grounds (DAG) dataset, a large collection of street-level and aerial images targeting such cases. Its main challenge lies in the extreme viewing-angle difference between query and reference images with consequent changes in illumination and perspective. The dataset is larger and more diverse than current publicly available data, including more than 50 km of roads in urban, suburban and rural areas. All images are associated with accurate 6-DoF metadata that allows the benchmarking of visual localization methods. Additionally, we validate our data by presenting the results of a simple map-to-image re-localization baseline. that first estimates a dense 3D reconstruction from the aerial images and then matches query street-level images to street-level renderings of the 3D model. The dataset can be downloaded at:.
Deep End-to-end Causal Inference

Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making. However, research on causal discovery has evolved separately from inference methods, preventing straight-forward combination of methods from both fields. In this work, we develop Deep End-to-end Causal Inference (DECI), a single flow-based non-linear additive noise model that takes in observational data and can perform both causal discovery and inference, including conditional average treatment effect (CATE) estimation. We provide a theoretical guarantee that DECI can recover the ground truth causal graph under standard causal discovery assumptions. Motivated by application impact, we extend this model to heterogeneous, mixed-type data with missing values, allowing for both continuous and discrete treatment decisions. Our results show the competitive performance of DECI when compared to relevant baselines for both causal discovery and (C)ATE estimation in over a thousand experiments on both synthetic datasets and causal machine learning benchmarks across data-types and levels of missingness.
Structure Property in Cu crystallization

Abstract Phase transition is a central topic in condensed matter physics, all the time. In this paper, as a general representative of phase transition, the Cu crystallization is discussed. And some physical quantities is defined to quantificationally describe the structure property in Cu crystallization, such as diffusion property and symmetry so on. As a result, it is indicated that there are some interesting changes of structure property in Cu crystallization.
Energy-Efficient High-Accuracy Spiking Neural Network Inference Using Time-Domain Neurons

Due to the limitations of realizing artificial neural networks on prevalent von Neumann architectures, recent studies have presented neuromorphic systems based on spiking neural networks (SNNs) to reduce power and computational cost. However, conventional analog voltage-domain integrate-and-fire (I&F) neuron circuits, based on either current mirrors or op-amps, pose serious issues such as nonlinearity or high power consumption, thereby degrading either inference accuracy or energy efficiency of the SNN. To achieve excellent energy efficiency and high accuracy simultaneously, this paper presents a low-power highly linear time-domain I&F neuron circuit. Designed and simulated in a 28nm CMOS process, the proposed neuron leads to more than 4.3x lower error rate on the MNIST inference over the conventional current-mirror-based neurons. In addition, the power consumed by the proposed neuron circuit is simulated to be $0.230\mu \mathrm{W}$ per neuron, which is orders of magnitude lower than the existing voltage-domain neurons.
An Overview of Ultra-WideBand (UWB) Standards(IEEE 802.15.4, FiRa, Apple): Interoperability Aspects and Future Research Directions

—The increasing popularity of ultra-wideband (UWB) technology for location-based services such as access control and real-time indoor track&tracing, as well as UWB support in new consumer devices such as smartphones, has resulted in the availability of multiple new UWB radio chips. However, due to this increase in UWB device availability, the question of which (industry) standards and conﬁguration factors impact UWB interoperability and compatibility becomes increasingly important. In this paper, the fundamentals of UWB compatibility are investigated by ﬁrst giving an overview of different UWB radio chips on the market. After that, an overview of UWB standards and standardisation entities is given. Next, this overview is used to discuss the focus of these different standards and to identify the differences between them. We describe compatibility issues and associated interoperability aspects related to PHY, MAC, and upper layers. For the PHY layer, compatibility is possible for all UWB chips if the correct settings are conﬁgured. For the MAC layer, the implementation of the multiple access scheme as well as the localization technique is mostly proprietary. For the device discovery, several standards are currently being drafted. Finally, future challenges related to UWB interoperability are discussed.
Conjugation Matters. Bioctonionic Veronese Vectors and Cayley-Rosenfeld Planes

Motivated by the recent interest in Lie algebraic and geometric structures arising from tensor products of division algebras and their relevance to high energy theoretical physics, we analyze generalized bioctonionic projective and hyperbolic planes. After giving a Veronese representation of the complexification of the Cayley plane $\mathbb{O}P_{\mathbb{C}}^{2}$, we present a novel, explicit construction of the bioctonionic Cayley-Rosenfeld plane $\left( \mathbb{C}\otimes \mathbb{O}\right) P^{2}$, again by exploiting Veronese coordinates. We discuss the isometry groups of all generalized bioctonionic planes, recovering all complex and real forms of the exceptional groups $F_{4}$ and $E_{6}$, and characterizing such planes as symmetric and Hermitian symmetric spaces. We conclude by discussing some possible physical applications.
Effects of intercomponent couplings on the appearance of time-reversal symmetry breaking fermion-quadrupling states in two-component London models

A detection of bosonic metallic state that breaks the $Z_2$ time-reversal symmetry has been recently reported in Ba$_{1-x}$K$_x$Fe$_2$As$_2$ with a doping level $x \approx 0.8$. This is a metallic state of fermionic quadruplets that breaks time-reversal symmetry. As such, it has no condensate of Cooper pairs but has a long-range order between fermionic quartets. In the present manuscript, we investigate the emergence of this phase in a two-component London model via Monte Carlo simulations as a function of various intercomponent couplings.
A Markov Chain Surrogate Model for a Two-Dimensional Interacting Particle System with Internal Collisions

A probabilistic Markov Chain (MC) surrogate model for a two-dimensional system of interacting particles within a square domain having inherent symmetries is developed. Particles are assumed to be circular and identical, colliding with each other and with rigid domain walls via perfectly elastic collisions. Simulation results over many realizations are used to develop and evaluate the surrogate MC model. The stationary quantity of interest (QoI) is the number of particles within each of nine coarser subdomains, delineated via three internal geometric symmetries. The surrogate model is used to quantify QoI mean properties and uncertainty as the particle radius is varied.
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems

We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum with a fixed body centered tetragonal lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.
Unsupervised Learning Based Hybrid Beamforming with Low-Resolution Phase Shifters for MU-MIMO Systems

Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and beyond communications. Hybrid beamforming has been proposed for large-scale antenna systems in mmWave communications. Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption. In this paper, we propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs for multiuser multiple-input multiple-output (MU-MIMO) systems. We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture, termed the phase classification network (PCNet), capable of producing solutions of various PS resolutions. Simulation results demonstrate the superior sum-rate and complexity performance of the proposed scheme, as compared to state-of-the-art hybrid beamforming designs for the most commonly used low-resolution PS configurations.