Miracle-dz/UniFAR-FTUs huggingface dataset:
Industry applications of neutral-atom quantum computing solving independent set problems

Architectures for quantum computing based on neutral atoms have risen to prominence as candidates for both near and long-term applications. These devices are particularly well suited to solve independent set problems, as the combinatorial constraints can be naturally encoded in the low-energy Hilbert space due to the Rydberg blockade mechanism. Here, we approach this connection with a focus on a particular device architecture and explore the ubiquity and utility of independent set problems by providing examples of real-world applications. After a pedagogical introduction of basic graph theory concepts of relevance, we briefly discuss how to encode independent set problems in Rydberg Hamiltonians. We then outline the major classes of independent set problems and include associated example applications with industry and social relevance. We determine a wide range of sectors that could benefit from efficient solutions of independent set problems -- from telecommunications and logistics to finance and strategic planning -- and display some general strategies for efficient problem encoding and implementation on neutral-atom platforms.
Nodal current of complexiﬁed horocyclic eigenfunctions

We study horocyclic eigenfunctions on Lobachevsky plane. These are functions u : C + = { z ∈ C : ℑ z > 0 } → C such that (cid:16) − y 2 (cid:16) ∂ 2 ∂x 2 + ∂ 2 ∂y 2 (cid:17) + 2 iτy ∂∂x (cid:17) u ( x + iy ) = s 2 u ( x + iy ) , x + iy ∈ C + , with τ, s ∈ R , τ large and s/τ small. In other words, we study eigenfunctions of magnetic quantum Hamiltonian on hyperbolic plane. Asymptotic behavior of such functions is related to horocycle ﬂow on T C + by semiclassical correspondence. If a sequence of horocyclic functions possesses microlocal quantum unique ergodicity at the admissible energy level (with ~ = 1 /τ ) then we may ﬁnd asymptotic distribution of divisor of u analytically continued to the complexiﬁed Lobachevsky plane. by the Russian Science Foundation grant 19-71-30002.
Conditional Visual Servoing for Multi-Step Tasks

Visual Servoing has been effectively used to move a robot into specific target locations or to track a recorded demonstration. It does not require manual programming, but it is typically limited to settings where one demonstration maps to one environment state. We propose a modular approach to extend visual servoing to scenarios with multiple demonstration sequences. We call this conditional servoing, as we choose the next demonstration conditioned on the observation of the robot. This method presents an appealing strategy to tackle multi-step problems, as individual demonstrations can be combined flexibly into a control policy. We propose different selection functions and compare them on a shape-sorting task in simulation. With the reprojection error yielding the best overall results, we implement this selection function on a real robot and show the efficacy of the proposed conditional servoing. For videos of our experiments, please check out our project page: https://lmb.informatik. uni-freiburg.de/projects/conditional_servoing/
ColonFormer: An Efficient Transformer Based Method for Colon Polyp Segmentation

Identifying polyps is challenging for automatic analysis of endoscopic images in computer-aided clinical support systems. Models based on convolutional networks (CNN), transformers, and their combinations have been proposed to segment polyps with promising results. However, those approaches have limitations either in modeling the local appearance of the polyps only or lack of multi-level feature representation for spatial dependency in the decoding process. This paper proposes a novel network, namely ColonFormer, to address these limitations. ColonFormer is an encoder-decoder architecture capable of modeling long-range semantic information at both encoder and decoder branches. The encoder is a lightweight architecture based on transformers for modeling global semantic relations at multi scales. The decoder is a hierarchical network structure designed for learning multi-level features to enrich feature representation. Besides, a refinement module is added with a new skip connection technique to refine the boundary of polyp objects in the global map for accurate segmentation. Extensive experiments have been conducted on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-Larib. Experimental results show that our ColonFormer outperforms other state-of-the-art methods on all benchmark datasets. Our code is available at: https://github.com/ducnt9907/ColonFormer.
Demystifying the Data Need of ML-surrogates for CFD Simulations

Computational ﬂuid dynamics (CFD) simulations, a critical tool in various engineering applications, often require signiﬁcant time and compute power to predict ﬂow properties. The high computational cost associated with CFD simulations signiﬁcantly restricts the scope of design space explo- ration and limits their use in planning and operational control. To address this issue, machine learning (ML) based sur- rogate models have been proposed as a computationally ef-ﬁcient tool to accelerate CFD simulations. However, a lack of clarity about CFD data requirements often challenges the widespread adoption of ML-based surrogates among design engineers and CFD practitioners. In this work, we propose an ML-based surrogate model to predict the temperature dis- tribution inside the cabin of a passenger vehicle under various operating conditions and use it to demonstrate the trade- off between prediction performance and training dataset size. Our results show that the prediction accuracy is high and sta- ble even when the training size is gradually reduced from 2000 to 200. The ML-based surrogates also reduce the com- pute time from ∼ 30 minutes to around ∼ 9 milliseconds. Moreover, even when only 50 CFD simulations are used for training, the temperature trend (e.g., locations of hot/cold re-gions) predicted by the ML-surrogate matches quite well with the results from CFD simulations.
An Evaluation Framework for Legal Document Summarization

A law practitioner has to go through numerous lengthy legal case proceedings for their practices of various categories, such as land dispute, corruption, etc. Hence, it is important to summarize these documents, and ensure that summaries contain phrases with intent matching the category of the case. To the best of our knowledge, there is no evaluation metric that evaluates a summary based on its intent. We propose an automated intent-based summarization metric, which shows a better agreement with human evaluation as compared to other automated metrics like BLEU, ROUGE-L etc. in terms of human satisfaction. We also curate a dataset by annotating intent phrases in legal documents, and show a proof of concept as to how this system can be automated.
Scheduling in Wireless Networks using Whittle Index Theory

We consider the problem of scheduling packet transmissions in a wireless network of users while minimizing the energy consumed and the transmission delay. A challenge is that transmissions of users that are close to each other mutually interfere, while users that are far apart can transmit simultaneously without much interference. Each user has a queue of packets that are transmitted on a single channel and mutually non interfering users reuse the spectrum. Using the theory of Whittle index for cost minimizing restless bandits, we design four index-based policies and compare their performance with that of the well-known policies: Slotted ALOHA, maximum weight scheduling, quadratic Lyapunov drift, Cella and Cesa Bianchi algorithm, and two Whittle index based policies from a recently published paper. We make the code used to perform our simulations publicly available, so that it can be used for future work by the research community at large.
Supervised Learning for Coverage-Directed Test Selection in Simulation-Based Verification

Constrained random test generation is one of the most widely adopted methods for generating stimuli for simulation-based verification. Randomness leads to test diversity, but tests tend to repeatedly exercise the same design logic. Constraints are written (typically manually) to bias random tests towards interesting, hard-to-reach, and yet-untested logic. However, as verification progresses, most constrained random tests yield little to no effect on functional coverage. If stimuli generation consumes significantly less resources than simulation, then a better approach involves randomly generating a large number of tests, selecting the most effective subset, and only simulating that subset. In this paper, we introduce a novel method for automatic constraint extraction and test selection. This method, which we call coverage-directed test selection, is based on supervised learning from coverage feedback. Our method biases selection towards tests that have a high probability of increasing functional coverage, and prioritises them for simulation. We show how coverage-directed test selection can reduce manual constraint writing, prioritise effective tests, reduce verification resource consumption, and accelerate coverage closure on a large, real-life industrial hardware design.
Speciﬁc versus non-speciﬁc interactions of indole in water revealed by x-ray photoemission spectroscopy

Solvent interactions and speciﬁcally hydration are of utmost importance in chemical and biochemical systems. Model systems enable us to unravel the relevance of microscopic details of these interactions. Here, we characterized the electronic structure of the prototypical biomolecular chromophore indole (C 8 H 7 N) in aqueous solution and disentangled the speciﬁc and non-speciﬁc eﬀects of the solvent on indole’s electronic structure. The complete photoelectron-emission spectrum of indole aq in a liquid microjet was measured using 600 eV synchrotron radiation. The ﬁrst valence photoelectron peak corresponds to the ionization from the HOMO and HOMO − 1 orbitals, for which we assigned the binding energies to 7.38 and 7.93 eV. The solvent shifts for these peaks indicate the presence of simultaneous speciﬁc and non-speciﬁc eﬀects of the aqueous environment. The valence photoemission data were also compared to available data to determine the reorganization energy of aqueous-phase indole associated with its ionization. The core-electron binding energies for nitrogen and carbon demonstrated a distinct interaction of the water solvent with these atoms: a strong N-H · · · OH 2 hydrogen bond and an unstructured interaction of the water solvent with the carbon atoms. Auger-electron contributions to the spectra are also reported and discussed. The experimental data were interpreted with the aid of extensive ab initio modeling. The combination of the maximum-overlap method with the non-equilibrium polarizable-continuum model was demonstrated as an eﬃcient and accurate technique for a modeling of both the valence and core peaks in the spectra. The two-hole electron-population analysis is shown to provide a quantitative theoretical description of Auger spectra.
“What makes a question inquisitive?” A Study on Type-Controlled Inquisitive Question Generation

We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and finetune models for type-controlled question generation. Empirical results demonstrate that we can generate a variety of questions that adhere to specific types while drawing from the source texts. We also investigate strategies for selecting a single question from a generated set, considering both an informative vs. inquisitive question classifier and a pairwise ranker trained from a small set of expert annotations. Question selection using the pairwise ranker yields strong results in automatic and manual evaluation. Our human evaluation assesses multiple aspects of the generated questions, finding that the ranker chooses questions with the best syntax (4.59), semantics (4.37), and inquisitiveness (3.92) on a scale of 1-5, even rivaling the performance of human-written questions.
Continual learning on 3D point clouds with random compressed rehearsal

Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype for precise modeling of three-dimensional environments, but effective processing of this type of data proves to be challenging. In the world of large, heavily-parameterized network architectures and continuously-streamed data, there is an increasing need for machine learning models that can be trained on additional data. Unfortunately, currently available models cannot fully leverage training on additional data without losing their past knowledge. Combating this phenomenon, called catastrophic forgetting, is one of the main objectives of continual learning. Continual learning for deep neural networks has been an active field of research, primarily in 2D computer vision, natural language processing, reinforcement learning, and robotics. However, in 3D computer vision, there are hardly any continual learning solutions specifically designed to take advantage of point cloud structure. This work proposes a novel neural network architecture capable of continual learning on 3D point cloud data. We utilize point cloud structure properties for preserving a heavily compressed set of past data. By using rehearsal and reconstruction as regularization methods of the learning process, our approach achieves a significant decrease of catastrophic forgetting compared to the existing solutions on several most popular point cloud datasets considering two continual learning settings: when a task is known beforehand, and in the challenging scenario of when task information is unknown to the model.
Cubic magneto-optic Kerr effect in Ni(111) thin films with and without twinning

In most studies utilizing the magneto-optic Kerr effect (MOKE), the detected change of polarized light upon reflection from a magnetized sample is supposed to be proportional to the magnetization $\boldsymbol{M}$. However, MOKE signatures quadratic in $\boldsymbol{M}$ have also been identified and utilized, e.g., to sense the structural order in Heusler compounds, to detect spin-orbit torques or to image antiferromagnetic domains. In our study, we observe a strong anisotropic MOKE contribution of third order in $\boldsymbol{M}$ in Ni(111) thin films, attributed to a cubic magneto-optic tensor $\propto $ $\boldsymbol{M}^3$. We further show that the angular dependence of cubic MOKE (CMOKE) is affected by the amount of structural domain twinning in the sample. Our detailed study on CMOKE for two selected photon energies will open up new opportunities for CMOKE applications with sensitivity to twinning properties of thin films, e.g. CMOKE spectroscopy and microscopy or time-resolved CMOKE.
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review

The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms. The optimal bidding strategy and dispatching methodology under these new paradigms are prioritized concerns for both market participants and power system operators, with obstacles of uncertain characteristics, computational efficiency, as well as requirements of hyperopic decision-making. To tackle these problems, the Reinforcement Learning (RL), as an emerging machine learning technique with advantages compared with conventional optimization tools, is playing an increasingly significant role in both academia and industry. This paper presents a comprehensive review of RL applications in deregulated power market operation including bidding and dispatching strategy optimization, based on more than 150 carefully selected literatures. For each application, apart from a paradigmatic summary of generalized methodology, in-depth discussions of applicability and obstacles while deploying RL techniques are also provided. Finally, some RL techniques that have great potentiality to be deployed in bidding and dispatching problems are recommended and discussed.
Privacy Preserving Machine Learning for Electric Vehicles: A Survey

In the recent years, the interest of individual users in modern electric vehicles (EVs) has grown exponentially. An EV has two major components, which make it different from traditional vehicles, first is its environment friendly nature because of being electric, and second is the interconnection ability of these vehicles because of modern information and communication technologies (ICTs). Both of these features are playing a key role in the development of EVs, and both academia and industry personals are working towards development of modern protocols for EV networks. All these interactions, whether from energy perspective or from communication perspective, both are generating a tremendous amount of data every day. In order to get most out of this data collected from EVs, research works have highlighted the use of machine/deep learning techniques for various EV applications. This interaction is quite fruitful, but it also comes with a critical concern of privacy leakage during collection, storage, and training of vehicular data. Therefore, alongside developing machine/deep learning techniques for EVs, it is also critical to ensure that they are resilient to private information leakage and attacks. In this paper, we begin with the discussion about essential background on EVs and privacy preservation techniques, followed by a brief overview of privacy preservation in EVs using machine learning techniques. Particularly, we also focus on an in-depth review of the integration of privacy techniques in EVs and highlighted different application scenarios in EVs. Alongside this, we provide a a very detailed survey of current works on privacy preserving machine/deep learning techniques used for modern EVs. Finally, we present the certain research issues, critical challenges, and future directions of research for researchers working in privacy preservation in EVs.
Randomized Augmented Kaczmarz Methods For Linear Systems & Linear Feasibility Problems

In this work, we shed light on the so-called Kaczmarz method for solving Linear System (LS) and Linear Feasibility (LF) problems from a optimization point of view. We introduce well-known optimization approaches such as Lagrangian penalty and Augmented Lagrangian in the Randomized Kaczmarz (RK) method. In doing so, we propose two variants of the RK method namely the Randomized Penalty Kacmarz (RPK) method and Randomized Augmented Kacmarz (RAK) method. We carry out convergence analysis of the proposed methods and obtain linear convergence results.
Lossy Computing with Side Information via Multi-Hypergraphs

We consider a problem of coding for computing, where the decoder wishes to estimate a function of its local message and the source message at the encoder within a given distortion. We show that the rate-distortion function can be characterized through a characteristic multi-hypergraph, which simplifies the evaluation of the rate-distortion function.
Control Interface Remapping for Bias-Aware Assistive Teleoperation

Users of assistive devices vary in their extent of motor impairment, and hence their physical interaction with control interfaces can differ. There is the potential for improved utility if control interface actuation is mapped to assistive device control signals in a manner customized to each user. In this paper, we present (1) a method for creating a custom interface to assistive device control mapping based on the design of a user’s bias profile, (2) a procedure and virtual task for gathering interface actuation data from which to build the bias profile and map, and (3) an evaluation of our method on 6 participants with upper limb motor impairments. Our results show that custom interface remapping based on user bias profiles shows promise in providing assistance via an improvement in the reachability of the device control space. This effect was especially pronounced for individuals who had a more limited reachable space.
Computerized Tomography Pulmonary Angiography Image Simulation using Cycle Generative Adversarial Network from Chest CT imaging in Pulmonary Embolism Patients

The purpose of this research is to develop a system that generates simulated computed tomography pulmonary angiography (CTPA) images clinically for pulmonary embolism diagnoses. Nowadays, CTPA images are the gold standard computerized detection method to determine and identify the symptoms of pulmonary embolism (PE), although performing CTPA is harmful for patients and also expensive. Therefore, we aim to detect possible PE patients through CT images. The system will simulate CTPA images with deep learning models for the identification of PE patients' symptoms, providing physicians with another reference for determining PE patients. In this study, the simulated CTPA image generation system uses a generative antagonistic network to enhance the features of pulmonary vessels in the CT images to strengthen the reference value of the images and provide a basis for hospitals to judge PE patients. We used the CT images of 22 patients from National Cheng Kung University Hospital and the corresponding CTPA images as the training data for the task of simulating CTPA images and generated them using two sets of generative countermeasure networks. This study is expected to propose a new approach to the clinical diagnosis of pulmonary embolism, in which a deep learning network is used to assist in the complex screening process and to review the generated simulated CTPA images, allowing physicians to assess whether a patient needs to undergo detailed testing for CTPA, improving the speed of detection of pulmonary embolism and significantly reducing the number of undetected patients.
Initial non-invasive in vivo sensing of the lung using time domain diffuse optics (preprint)

Non-invasive in vivo sensing of the lung with light would help diagnose and monitor pulmonary disorders (caused by e.g. COVID-19, emphysema, immature lung tissue in infants). We investigated the possibility to probe the lung with time domain diffuse optics, taking advantage of the increased depth (few cm) reached by photons detected after a long (few ns) propagation time. An initial study on 5 healthy volunteers included time-resolved broadband diffuse optical spectroscopy measurements at 3 cm source-detector distance over the 600-1100 nm range, and long-distance (6-9 cm) measurements at 820 nm performed during a breathing protocol. The interpretation of the in vivo data with a simplified homogeneous model yielded a maximum probing depth of 2.6-3.9 cm, suitable to reach the lung. Also, signal changes related to the inspiration act were observed, especially at high photon propagation times. Yet, intra- and inter-subject variability and inconsistencies, possibly alluring to competing scattering and absorption effects, prevented a simple interpretation. Aspects to be further investigated to gain a deeper insight are discussed.