Hasil untuk "General Works"

J. B. Hayfron-Acquah, MS Nixon, J. Carter

B. Derrida, M. Evans, V. Hakim et al.

O. Jesorsky, K. Kirchberg, Robert Frischholz

K. Broberg

Xiao Yuan, Suguru Endo, Qi Zhao et al.

The variational method is a versatile tool for classical simulation of a variety of quantum systems. Great efforts have recently been devoted to its extension to quantum computing for efficiently solving static many-body problems and simulating real and imaginary time dynamics. In this work, we first review the conventional variational principles, including the Rayleigh-Ritz method for solving static problems, and the Dirac and Frenkel variational principle, the McLachlan's variational principle, and the time-dependent variational principle, for simulating real time dynamics. We focus on the simulation of dynamics and discuss the connections of the three variational principles. Previous works mainly focus on the unitary evolution of pure states. In this work, we introduce variational quantum simulation of mixed states under general stochastic evolution. We show how the results can be reduced to the pure state case with a correction term that takes accounts of global phase alignment. For variational simulation of imaginary time evolution, we also extend it to the mixed state scenario and discuss variational Gibbs state preparation. We further elaborate on the design of ansatz that is compatible with post-selection measurement and the implementation of the generalised variational algorithms with quantum circuits. Our work completes the theory of variational quantum simulation of general real and imaginary time evolution and it is applicable to near-term quantum hardware.

B. Cheng, Zhongkui Li

This paper considers the distributed event-triggered consensus problem for general linear multiagent networks. Both the leaderless and leader–follower consensus problems are considered. Based on the local sampled state information, distributed adaptive event-triggered protocols are designed, which can ensure that the consensus of the agents is achieved, and the Zeno behavior is excluded by showing that the interval between any two triggering events is lower-bounded by a strictly positive value. Compared with the previous related works, our main contribution is that the proposed adaptive event-based protocols are fully distributed and scalable, which do not rely on any global information of the network graph and are independent of the network's scale. In these event-based protocols, continuous communications are not required for either control laws updation or triggering functions monitoring.

Gernot Riegler, V. Koltun

We present a method for novel view synthesis from input images that are freely distributed around a scene. Our method does not rely on a regular arrangement of input views, can synthesize images for free camera movement through the scene, and works for general scenes with unconstrained geometric layouts. We calibrate the input images via SfM and erect a coarse geometric scaffold via MVS. This scaffold is used to create a proxy depth map for a novel view of the scene. Based on this depth map, a recurrent encoder-decoder network processes reprojected features from nearby views and synthesizes the new view. Our network does not need to be optimized for a given scene. After training on a dataset, it works in previously unseen environments with no fine-tuning or per-scene optimization. We evaluate the presented approach on challenging real-world datasets, including Tanks and Temples, where we demonstrate successful view synthesis for the first time and substantially outperform prior and concurrent work.

Aaron S. Jackson, Adrian Bulat, V. Argyriou et al.

3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspondences across large facial poses, expressions, and non-uniform illumination. In general these methods require complex and inefficient pipelines for model building and fitting. In this work, we propose to address many of these limitations by training a Convolutional Neural Network (CNN) on an appropriate dataset consisting of 2D images and 3D facial models or scans. Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model. We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality, especially for the cases of large poses and facial expressions. Code and models will be made available at http://aaronsplace.co.uk

Stephen Becker, E. Candès, Michael C. Grant

This paper develops a general framework for solving a variety of convex cone problems that frequently arise in signal processing, machine learning, statistics, and other fields. The approach works as follows: first, determine a conic formulation of the problem; second, determine its dual; third, apply smoothing; and fourth, solve using an optimal first-order method. A merit of this approach is its flexibility: for example, all compressed sensing problems can be solved via this approach. These include models with objective functionals such as the total-variation norm, ||Wx||1 where W is arbitrary, or a combination thereof. In addition, the paper introduces a number of technical contributions such as a novel continuation scheme and a novel approach for controlling the step size, and applies results showing that the smooth and unsmoothed problems are sometimes formally equivalent. Combined with our framework, these lead to novel, stable and computationally efficient algorithms. For instance, our general implementation is competitive with state-of-the-art methods for solving intensively studied problems such as the LASSO. Further, numerical experiments show that one can solve the Dantzig selector problem, for which no efficient large-scale solvers exist, in a few hundred iterations. Finally, the paper is accompanied with a software release. This software is not a single, monolithic solver; rather, it is a suite of programs and routines designed to serve as building blocks for constructing complete algorithms.

Marin Orsic, Ivan Kreso, Petra Bevandic et al.

Recent success of semantic segmentation approaches on demanding road driving datasets has spurred interest in many related application fields. Many of these applications involve real-time prediction on mobile platforms such as cars, drones and various kinds of robots. Real-time setup is challenging due to extraordinary computational complexity involved. Many previous works address the challenge with custom lightweight architectures which decrease computational complexity by reducing depth, width and layer capacity with respect to general purpose architectures. We propose an alternative approach which achieves a significantly better performance across a wide range of computing budgets. First, we rely on a light-weight general purpose architecture as the main recognition engine. Then, we leverage light-weight upsampling with lateral connections as the most cost-effective solution to restore the prediction resolution. Finally, we propose to enlarge the receptive field by fusing shared features at multiple resolutions in a novel fashion. Experiments on several road driving datasets show a substantial advantage of the proposed approach, either with ImageNet pre-trained parameters or when we learn from scratch. Our Cityscapes test submission entitled SwiftNetRN-18 delivers 75.5% MIoU and achieves 39.9 Hz on 1024×2048 images on GTX1080Ti.

V. Koltchinskii, A. Tsybakov, Karim Lounici

This paper deals with the trace regression model where $n$ entries or linear combinations of entries of an unknown $m_1\times m_2$ matrix $A_0$ corrupted by noise are observed. We propose a new nuclear norm penalized estimator of $A_0$ and establish a general sharp oracle inequality for this estimator for arbitrary values of $n,m_1,m_2$ under the condition of isometry in expectation. Then this method is applied to the matrix completion problem. In this case, the estimator admits a simple explicit form and we prove that it satisfies oracle inequalities with faster rates of convergence than in the previous works. They are valid, in particular, in the high-dimensional setting $m_1m_2\gg n$. We show that the obtained rates are optimal up to logarithmic factors in a minimax sense and also derive, for any fixed matrix $A_0$, a non-minimax lower bound on the rate of convergence of our estimator, which coincides with the upper bound up to a constant factor. Finally, we show that our procedure provides an exact recovery of the rank of $A_0$ with probability close to 1. We also discuss the statistical learning setting where there is no underlying model determined by $A_0$ and the aim is to find the best trace regression model approximating the data.

O. Sauter, C. Angioni, Y. Lin-Liu

P. Samuelson

Description Ongoing lawsuits could affect everyone who uses generative AI Generative artificial intelligence (AI) is a disruptive technology that is widely adopted by members of the general public as well as scientists and technologists who are enthusiastic about the potential to accelerate research in a wide variety of fields. But some professional artists, writers, and programmers fiercely object to the use of their creations as training data for generative AI systems and to outputs that may compete with or displace their works (1, 2). Lack of attribution and compensation for use of their original creations are other sources of aggravation to critics of generative AI. Copyright lawsuits that are now underway in the United States have substantial implications for the future of generative AI systems. If the plaintiffs prevail, the only generative AI systems that may be lawful in the United States would be those trained on public domain works or under licenses, which will affect everyone who deploys generative AI, integrates it into their products, and uses it for scientific research.

R. Benadjila, E. Prouff, Rémi Strullu et al.

Mariana Aristizábal

The COVID-19 pandemic shocked the world in 2020, altering almost every aspect of daily life. One of the areas that suffered the most during the pandemic was the education system. As urban areas transitioned to online platforms and software, rural towns lacked the technological resources to handle internet connectivity challenges that deepened the crisis. One such example is the city of Leticia, the capital of the Amazonas Department in Colombia. Located in the southern part of the country, Leticia can only be accessed by flight or boat. In 2020, Leticia was already facing significant educational inequalities, and teachers and students alike struggled with remote learning due to the limited access to technology and internet connectivity. Established offline teaching practices were barely modified for remote learning and the crisis was aggravated when the rapid spread of COVID-19 impacted entire families and communities. Unable to work and already facing financial issues that hindered access to food and services, residents witnessed the death of loved ones and community leaders before the arrival of vaccinations. Now that in-person classes have resumed, it is worthwhile to analyze the implications of the last year´s gap in students’ learning process, the role of administrators through government initiatives, and the current challenges that teachers and students face in their new classroom reality. This article provides valuable information to understand the urgent needs of the educational community in Leticia in the Post-COVID scenario.

Feng Yan, Kaiwen Sun, Subhadra Gupta

Saeed Naser Aḥmed Al Sariḥ

هدفت الدراسة إلى بيان قادح فساد الاعتبار، والوقوف على جملة من الأقيسة التي تعقبها الإمام ابن حجر بأنها فاسدة الاعتبار، ومحاولة الجواب عنها. وقد جعلت الدراسة في مقدمة ومبحثين: الأول في التعريف بقادح فساد الاعتبار وأدلة اعتباره والجواب عنه، والثاني في تطبيقاته الفقهية من كتاب فتح الباري. واعتمدت في البحث المنهج الوصفي التحليلي لنماذج من الأقيسة التي تعقبها الإمام ابن حجر، وذكرت من الأقوال والأدلة ما يظهر معه هذا القادح وتأثيره في الخلاف الفقهي. ثم ختمت البحث بنتائج كان من أهمها: التأكيد على أن اجتهاد الأئمة الأعلام -رحمهم الله- دائرٌ مع النص الشرعي، متى ظهر لهم الدليل اتبعوه وعدلوا عن النظر، وقد سبق من كلام العلماء ما يدل على هذا المعنى. وغالب الأقيسة المذكورة -هنا- التي اُعترض عليها بأنها مخالفة للنص عند التدقيق يتبين للناظر فيها وجود أدلة أخرى غير القياس استندوا إليها؛ فيكون القياس معتضدًا بالأدلة النقلية، وحينئذٍ يُنظر في تلك الأدلة فإن صحت تقوّى بها القياس وكان ذلك جوابًا يندفع معه الاعتراض، وإن لم يصح الاستدلال بها -ثبوتًا أو دلالة- سقط القياس؛ لبقائه وحيدًا أمام النص، والقياس لا يقوى على معارضته.

Peeyush Agarwal, Harsh Agarwal, Akshat Rana

Purpose: The rapid integration of artificial intelligence (AI) systems like ChatGPT, Claude AI, etc., has a deep impact on how work is done. Predicting how AI will reshape work requires understanding not just its capabilities, but how it is actually being adopted. This study investigates which intrinsic task characteristics drive users' decisions to delegate work to AI systems. Methodology: This study utilizes the Anthropic Economic Index dataset of four million Claude AI interactions mapped to O*NET tasks. We systematically scored each task across seven key dimensions: Routine, Cognitive, Social Intelligence, Creativity, Domain Knowledge, Complexity, and Decision Making using 35 parameters. We then employed multivariate techniques to identify latent task archetypes and analyzed their relationship with AI usage. Findings: Tasks requiring high creativity, complexity, and cognitive demand, but low routineness, attracted the most AI engagement. Furthermore, we identified three task archetypes: Dynamic Problem Solving, Procedural & Analytical Work, and Standardized Operational Tasks, demonstrating that AI applicability is best predicted by a combination of task characteristics, over individual factors. Our analysis revealed highly concentrated AI usage patterns, with just 5% of tasks accounting for 59% of all interactions. Originality: This research provides the first systematic evidence linking real-world generative AI usage to a comprehensive, multi-dimensional framework of intrinsic task characteristics. It introduces a data-driven classification of work archetypes that offers a new framework for analyzing the emerging human-AI division of labor.

Badr Al Faiya, Stephen McArthur, Ivana Kockar

Distribution networks will experience more installations of distributed generation (DG) that is unpredictable and stochastic in nature. Greater distributed control and intelligence will allow challenges such as voltage control to be handled effectively. The partitioning of power networks into smaller clusters provides a method to split the control problem into manageable sub-problems. This paper presents a community detection-based partitioning technique for distribution networks considering local DGs, allowing them to be grouped and controlled in a distributed manner by using local signals and measurements. This method also allows each community to control the voltage using only neighboring DGs, and for each community to self-organize to reflect varying DG conditions and to maintain stable control. Simulations demonstrate that the partitioning of the large distribution network is effective, and each community is able to self-organize and to regulate the voltage independently using only its local DGs.

Tom Benhamou, James Cummings, Gabriel Goldberg et al.

We introduce a new class of ultrafilters which generalizes the well-known class of simple $P$-point ultrafilters. We prove that for any well-founded $σ$-directed partial order $\mathbb{D}$ there is a mild forcing extension where there is an ultrafilter $U$ on $ω$ with a base $\mathcal{B}$ such that $(\mathcal{B},\supseteq^*)\cong \mathbb{D}$. On a measurable cardinal we prove a similar result: relative to a supercompact cardinal, it is consistent that $κ$ is supercompact, and for a $κ^+$-directed well-founded poset $\mathbb{D}$, there is a ${<}κ$-directed closed $κ^+$-cc forcing extension where there is a \emph{normal} ultrafilter $U$ on $κ$ with a base $\mathcal{B}$ such that $(\mathcal{B},\supseteq^*)\cong \mathbb{D}$. These are optimal results in the class of $P$-points and realize every potential structure of a $P$-point. We apply our constructions to obtain ultrafilters with controlled Tukey-type, in particular, an ultrafilter with non-convex Tukey and depth spectra is presented, answering questions from \cite{Benhamou_2024}. Our construction also provides new models where $\mathfrak{u}_κ<2^κ$, answering questions from \cite{Benhamou_Goldberg2025}.