Miracle-dz/UniFAR-FTUs huggingface dataset:
On the validity of the bipolaron model for PEDOT, with and without AlCl4- anions

The conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) is one of the most researched materials. The 1980s bipolaron model remains the dominant interpretation of the electronic structure of PEDOT. Recent theoretical studies have provided updated definitions of key concepts such as bipolarons or polaron pairs, but these have not yet become widely known. In this work, we use density functional theory to investigate the electronic structure of PEDOT oligomers, with and without co-located AlCl4- anions. By considering the influence of oligomer length, oxidation or anion concentration and spin state, we find no evidence for self-localisation of positive charges in PEDOT as predicted by the bipolaron model at the hybrid functional level. Our results show distortions that exhibit either a single or a double peak in bond length alternations and charge density. Either can occur at any oxidation or anion concentration. We note that other distortion shapes are also possible. Rather than representing bipolarons or polaron pairs in the original model, these are electron distributions driven by a range of factors. Localisation of distortions does occur with anions, and distortions can span an arbitrary number of nearby anions. Conductivity in conducting polymers has been observed to reduce at anion concentrations above 0.5. We show at high anion concentrations, the energy of the localised, non-bonding anionic orbitals approaches that of the system HOMO due to Coulombic repulsion between anions. We hypothesize that with nucleic motion in the macropolymer, these orbitals will interfere with the hopping of charge carriers between sites of similar energy, lowering conductivity.
Linear Stability Analysis of a Delay Differential Integral Equation Motivated by Genetic Networks

This work presents a computational study on the linear stability analysis of a genetic network with time delay. The network is modeled as a continuous system and takes the form of a nonlinear delay differential integral equation coupled to an ordinary differential equation. Analysis of the stable equilibria shows the existence of a critical time delay beyond which limit cycle oscillations are born in a Hopf bifurcation. We confirm our results by discretizing the continuous model into an N-dimensional system and showing that the associated critical time delays for the discrete system approach the critical time delay for the continuous system as N becomes large.
Tunneling time and Faraday/Kerr effects in systems

We review the generalization of tunneling time and anomalous behaviour of Faraday and Kerr rotation angles in parity and time systems. Similarities of two phenomena are discussed, both exhibit a phase transition-like anomalous behaviour in a certain range of model parameters. Anomalous behaviour of tunneling time and Faraday/Kerr angles in systems is caused by the motion of poles of scattering amplitudes in the energy/frequency complex plane.
The Engine of Reason, the Seat of the Soul

In a fast-paced, entertaining narrative, replete with examples and numerous explanatory illustrations, Churchland brings together an exceptionally broad range of intellectual issues. He summarizes new results from neuroscience and recent work with artificial neural networks that together suggest a unified set of answers to questions about how the brain actually works; how it sustains a thinking, feeling, dreaming self; and how it sustains a self-conscious person. Churchland first explains the science—the powerful role of vector coding in sensory representation and pattern recognition, artificial neural networks that imitate parts of the brain, recurrent networks, neural representation of the social world, and diagnostic technologies and therapies for the brain in trouble. He then explores the far-reaching consequences of the current neurocomputational understanding of mind for our philosophical convictions, and for our social, moral, legal, medical, and personal lives. Churchland's wry wit and skillful teaching style are evident throughout. He introduces the remarkable representational power of a single human brain, for instance, via a captivating brain/World-Trade-Tower TV screen analogy. "Who can be watching this pixilated show? " Churchland queries; the answer is a provocative "no one. " And he has included a folded stereoscopic viewer, attached to the inside back cover of the book, that readers can use to participate directly in several revealing experiments concerning stereo vision. Bradford Books imprint
Neural-Singular-Hessian: Implicit Neural Representation of Unoriented Point Clouds by Enforcing Singular Hessian

Neural implicit representation is a promising approach for reconstructing surfaces from point clouds. Existing methods combine various regularization terms, such as the Eikonal and Laplacian energy terms, to enforce the learned neural function to possess the properties of a Signed Distance Function (SDF). However, inferring the actual topology and geometry of the underlying surface from poor-quality unoriented point clouds remains challenging. In accordance with Differential Geometry, the Hessian of the SDF is singular for points within the differential thin-shell space surrounding the surface. Our approach enforces the Hessian of the neural implicit function to have a zero determinant for points near the surface. This technique aligns the gradients for a near-surface point and its on-surface projection point, producing a rough but faithful shape within just a few iterations. By annealing the weight of the singular-Hessian term, our approach ultimately produces a high-fidelity reconstruction result. Extensive experimental results demonstrate that our approach effectively suppresses ghost geometry and recovers details from unoriented point clouds with better expressiveness than existing fitting-based methods.
Nonnegative Ricci curvature, nilpotency, and Hausdorff dimension

Let $M$ be an open (complete and non-compact) manifold with $\mathrm{Ric}\ge 0$ and escape rate not $1/2$. It is known that under these conditions, the fundamental group $\pi_1(M)$ has a finitely generated torsion-free nilpotent subgroup $\mathcal{N}$ of finite index, as long as $\pi_1(M)$ is an infinite group. We show that the nilpotency step of $\mathcal{N}$ must be reflected in the asymptotic geometry of the universal cover $\widetilde{M}$, in terms of the Hausdorff dimension of an isometric $\mathbb{R}$-orbit: there exist an asymptotic cone $(Y,y)$ of $\widetilde{M}$ and a closed $\mathbb{R}$-subgroup $L$ of the isometry group of $Y$ such that its orbit $Ly$ has Hausdorff dimension at least the nilpotency step of $\mathcal{N}$. This resolves a question raised by Wei and the author, and extends previous results on virtual abelianness by the author.
Stabilize to Act: Learning to Coordinate for Bimanual Manipulation

Key to rich, dexterous manipulation in the real world is the ability to coordinate control across two hands. However, while the promise afforded by bimanual robotic systems is immense, constructing control policies for dual arm autonomous systems brings inherent difficulties. One such difficulty is the high-dimensionality of the bimanual action space, which adds complexity to both model-based and data-driven methods. We counteract this challenge by drawing inspiration from humans to propose a novel role assignment framework: a stabilizing arm holds an object in place to simplify the environment while an acting arm executes the task. We instantiate this framework with BimanUal Dexterity from Stabilization (BUDS), which uses a learned restabilizing classifier to alternate between updating a learned stabilization position to keep the environment unchanged, and accomplishing the task with an acting policy learned from demonstrations. We evaluate BUDS on four bimanual tasks of varying complexities on real-world robots, such as zipping jackets and cutting vegetables. Given only 20 demonstrations, BUDS achieves 76.9% task success across our task suite, and generalizes to out-of-distribution objects within a class with a 52.7% success rate. BUDS is 56.0% more successful than an unstructured baseline that instead learns a BC stabilizing policy due to the precision required of these complex tasks. Supplementary material and videos can be found at https://sites.google. com/view/stabilizetoact .
Universal Normalization Enhanced Graph Representation Learning for Gene Network Prediction

Effective gene network representation learning is of great importance in bioinformatics to predict/understand the relation of gene profiles and disease phenotypes. Though graph neural networks (GNNs) have been the dominant architecture for analyzing various graph-structured data like social networks, their predicting on gene networks often exhibits subpar performance. In this paper, we formally investigate the gene network representation learning problem and characterize a notion of \textit{universal graph normalization}, where graph normalization can be applied in an universal manner to maximize the expressive power of GNNs while maintaining the stability. We propose a novel UNGNN (Universal Normalized GNN) framework, which leverages universal graph normalization in both the message passing phase and readout layer to enhance the performance of a base GNN. UNGNN has a plug-and-play property and can be combined with any GNN backbone in practice. A comprehensive set of experiments on gene-network-based bioinformatical tasks demonstrates that our UNGNN model significantly outperforms popular GNN benchmarks and provides an overall performance improvement of 16 $\%$ on average compared to previous state-of-the-art (SOTA) baselines. Furthermore, we also evaluate our theoretical findings on other graph datasets where the universal graph normalization is solvable, and we observe that UNGNN consistently achieves the superior performance.
Sharp sub-Gaussian upper bounds for subsolutions of Trudinger's equation on Riemannian manifolds

We consider on Riemannian manifolds the nonlinear evolution equation \begin{equation*} \partial _{t}u=\Delta _{p}(u^{1/(p-1)}), \end{equation*}% where $p>1$. This equation is also known as a doubly non-linear parabolic equation or Trudinger's equation. We prove that weak subsolutions of this equation have a sub-Gaussian upper bound and prove that this upper bound is sharp for a specific class of manifolds including $\mathbb{R}^{n}$.
Mutual Witness Proximity Drawings of Isomorphic Trees

A pair $\langle G_0, G_1 \rangle$ of graphs admits a mutual witness proximity drawing $\langle \Gamma_0, \Gamma_1 \rangle$ when: (i) $\Gamma_i$ represents $G_i$, and (ii) there is an edge $(u,v)$ in $\Gamma_i$ if and only if there is no vertex $w$ in $\Gamma_{1-i}$ that is ``too close'' to both $u$ and $v$ ($i=0,1$). In this paper, we consider infinitely many definitions of closeness by adopting the $\beta$-proximity rule for any $\beta \in [1,\infty]$ and study pairs of isomorphic trees that admit a mutual witness $\beta$-proximity drawing. Specifically, we show that every two isomorphic trees admit a mutual witness $\beta$-proximity drawing for any $\beta \in [1,\infty]$. The constructive technique can be made ``robust'': For some tree pairs we can suitably prune linearly many leaves from one of the two trees and still retain their mutual witness $\beta$-proximity drawability. Notably, in the special case of isomorphic caterpillars and $\beta=1$, we construct linearly separable mutual witness Gabriel drawings.
AI driven B-cell Immunotherapy Design

Antibodies, a prominent class of approved biologics, play a crucial role in detecting foreign antigens. The effectiveness of antigen neutralisation and elimination hinges upon the strength, sensitivity, and specificity of the paratope-epitope interaction, which demands resource-intensive experimental techniques for characterisation. In recent years, artificial intelligence and machine learning methods have made significant strides, revolutionising the prediction of protein structures and their complexes. The past decade has also witnessed the evolution of computational approaches aiming to support immunotherapy design. This review focuses on the progress of machine learning-based tools and their frameworks in the domain of B-cell immunotherapy design, encompassing linear and conformational epitope prediction, paratope prediction, and antibody design. We mapped the most commonly used data sources, evaluation metrics, and method availability and thoroughly assessed their significance and limitations, discussing the main challenges ahead.
Extreme first passage times for populations of identical rare events

A collection of identical and independent rare event first passage times is considered. The problem of finding the fastest out of $N$ such events to occur is called an extreme first passage time. The rare event times are singular and limit to infinity as a positive parameter scaling the noise magnitude is reduced to zero. In contrast, previous work has shown that the mean of the fastest event time goes to zero in the limit of an infinite number of walkers. The combined limit is studied. In particular, the mean time and the most likely path taken by the fastest random walker are investigated. Using techniques from large deviation theory, it is shown that there is a distinguished limit where the mean time for the fastest walker can take any positive value, depending on a single proportionality constant. Furthermore, it is shown that the mean time and most likely path can be approximated using the solution to a variational problem related to the single-walker rare event.
The Fiber of the Principal Minor Map

This paper explores the fibers of the principal minor map over a general field. The principal minor map is the map that assigns to each $n\times n$ matrix the $2^n$-vector of its principal minors. In $1984$, Hartfiel and Loewy proposed a condition that was sufficient to ensure that the fiber of the principal minor map is a single point up to diagonal equivalence. Loewy later improved upon this condition in $1986$. In this paper, we provide a necessary and sufficient condition for the fiber to be a point up to diagonal equivalence. Additionally, we establish a connection between the reducibility of a matrix and the reducibility of its determinantal representation. Using this connection, we fully characterize the fiber of symmetric and Hermitian matrices in the space of $n\times n$ matrices over any field $\mathbb{F}$. We also use these techniques to answer a question of Borcea, Br\"and\'en, and Liggett concerning real stable matrices.
State‐Independent Geometric Quantum Gates via Nonadiabatic and Noncyclic Evolution

Geometric phases are robust to local noises and the nonadiabatic ones can reduce the evolution time, thus nonadiabatic geometric gates have strong robustness and can approach high fidelity. However, the advantage of geometric phase has not been fully explored in previous investigations. Here,a scheme is proposed for universal quantum gates with pure nonadiabatic and noncyclic geometric phases from smooth evolution paths. In the scheme, only geometric phase can be accumulated in a fast way, and thus it not only fully utilizes the local noise resistant property of geometric phase but also reduces the difficulty in experimental realization. Numerical results show that the implemented geometric gates have stronger robustness than dynamical gates and the geometric scheme with cyclic path. Furthermore, it proposes to construct universal quantum gate on superconducting circuits, with the fidelities of single‐qubit gate and nontrivial two‐qubit gate can achieve 99.97% and 99.87%, respectively. Therefore, these high‐fidelity quantum gates are promising for large‐scale fault‐tolerant quantum computation.
ExMobileViT: Lightweight Classifier Extension for Mobile Vision Transformer

The paper proposes an efficient structure for enhancing the performance of mobile-friendly vision transformer with small computational overhead. The vision transformer (ViT) is very attractive in that it reaches outperforming results in image classification, compared to conventional convolutional neural networks (CNNs). Due to its need of high computational resources, MobileNet-based ViT models such as MobileViT-S have been developed. However, their performance cannot reach the original ViT model. The proposed structure relieves the above weakness by storing the information from early attention stages and reusing it in the final classifier. This paper is motivated by the idea that the data itself from early attention stages can have important meaning for the final classification. In order to reuse the early information in attention stages, the average pooling results of various scaled features from early attention stages are used to expand channels in the fully-connected layer of the final classifier. It is expected that the inductive bias introduced by the averaged features can enhance the final performance. Because the proposed structure only needs the average pooling of features from the attention stages and channel expansions in the final classifier, its computational and storage overheads are very small, keeping the benefits of low-cost MobileNet-based ViT (MobileViT). Compared with the original MobileViTs on the ImageNet dataset, the proposed ExMobileViT has noticeable accuracy enhancements, having only about 5% additional parameters.
A Survey for Graphic Design Intelligence

Graphic design is an effective language for visual communication. Using complex composition of visual elements (e.g., shape, color, font) guided by design principles and aesthetics, design helps produce more visually-appealing content. The creation of a harmonious design requires carefully selecting and combining different visual elements, which can be challenging and time-consuming. To expedite the design process, emerging AI techniques have been proposed to automatize tedious tasks and facilitate human creativity. However, most current works only focus on specific tasks targeting at different scenarios without a high-level abstraction. This paper aims to provide a systematic overview of graphic design intelligence and summarize literature in the taxonomy of representation, understanding and generation. Specifically we consider related works for individual visual elements as well as the overall design composition. Furthermore, we highlight some of the potential directions for future explorations.
Real Time Vehicle Identification: A Synchronous-Transmission Based Approach

Identification of the vehicles passing over the roads is a very important component of traffic monitoring/surveillance. There have been many attempts to design and develop efficient strategies to carry out the job. However, from the point of view of practical usefulness and real-time operation, most of them do not score well. In the current work, we perceive the problem as efficient real-time communication and data-sharing between the units in charge of recording the identities of the vehicles, i.e., Vehicle Recorders (VR), and the Vehicles (VE). We propose a strategy to address the issue with the help of Synchronous-Transmission (ST), which is a newer paradigm of communication compared to the traditional paradigm based on Asynchronous-Transmission (AT). First, we theoretically show that the presence of the physical layer phenomena called Capture-Effect in ST brings a significant benefit. Next, we also implement the strategy in a well-known IoT-Operating System Contiki, and compare its performance with the existing best-known strategy.
Aggregating Correlated Estimations with (Almost) no Training

Many decision problems cannot be solved exactly and use several estimation algorithms that assign scores to the different available options. The estimation errors can have various correlations, from low (e.g. between two very different approaches) to high (e.g. when using a given algorithm with different hyperparameters). Most aggregation rules would suffer from this diversity of correlations. In this article, we propose different aggregation rules that take correlations into account, and we compare them to naive rules in various experiments based on synthetic data. Our results show that when sufficient information is known about the correlations between errors, a maximum likelihood aggregation should be preferred. Otherwise, typically with limited training data, we recommend a method that we call Embedded Voting (EV).
Corgi^2: A Hybrid Offline-Online Approach To Storage-Aware Data Shuffling For SGD

When using Stochastic Gradient Descent (SGD) for training machine learning models, it is often crucial to provide the model with examples sampled at random from the dataset. However, for large datasets stored in the cloud, random access to individual examples is often costly and inefficient. A recent work \cite{corgi}, proposed an online shuffling algorithm called CorgiPile, which greatly improves efficiency of data access, at the cost some performance loss, which is particularly apparent for large datasets stored in homogeneous shards (e.g., video datasets). In this paper, we introduce a novel two-step partial data shuffling strategy for SGD which combines an offline iteration of the CorgiPile method with a subsequent online iteration. Our approach enjoys the best of both worlds: it performs similarly to SGD with random access (even for homogenous data) without compromising the data access efficiency of CorgiPile. We provide a comprehensive theoretical analysis of the convergence properties of our method and demonstrate its practical advantages through experimental results.
Digital Twins and Blockchain for IoT Management

Security and privacy are primary concerns in IoT management. Security breaches in IoT resources, such as smart sensors, can leak sensitive data and compromise the privacy of individuals. Effective IoT management requires a comprehensive approach to prioritize access security and data privacy protection. Digital twins create virtual representations of IoT resources. Blockchain adds decentralization, transparency, and reliability to IoT systems. This research integrates digital twins and blockchain to manage access to IoT data streaming. Digital twins are used to encapsulate data access and view configurations. Access is enabled on digital twins, not on IoT resources directly. Trust structures programmed as smart contracts are the ones that manage access to digital twins. Consequently, IoT resources are not exposed to third parties, and access security breaches can be prevented. Blockchain has been used to validate digital twins and store their configuration. The research presented in this paper enables multitenant access and customization of data streaming views and abstracts the complexity of data access management. This approach provides access and configuration security and data privacy protection.