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Quantum network protocols depend on the availability of shared entanglement. Given that entanglement generation and distribution are affected by noise, characterization of the shared entangled states is essential to bound the errors of the…

Quantum Physics · Physics 2025-10-31 Noah Kaufmann , Maria Quadeer , David Elkouss

Many engineering systems require accurate simulations of complex physical systems. Yet, analytical solutions are only available for simple problems, necessitating numerical approximations such as the Finite Element Method (FEM). The cost…

Efficient probability density estimation is a core challenge in statistical machine learning. Tensor-based probabilistic graph methods address interpretability and stability concerns encountered in neural network approaches. However, a…

Machine Learning · Computer Science 2023-12-14 Ruituo Wu , Jiani Liu , Ce Zhu , Anh-Huy Phan , Ivan V. Oseledets , Yipeng Liu

Local distinguishability of sets of generalized Bell states (GBSs) is investigated. We first clarify the conditions such that a set of GBSs can be locally transformed to a certain type of GBS set that is easily distinguishable within local…

Quantum Physics · Physics 2021-06-02 T. Hashimoto , M. Horibe , A. Hayashi

Error-correcting codes are usually envisioned to counter errors by operating unitary corrections depending on the projective measurement results of some syndrome observables. We here propose a way to use them in a more integrated way, where…

Quantum Physics · Physics 2020-10-07 Vincent Martin , Alain Sarlette

Diversity is an essential concept associated with communication reliability in multipath channels since it determines the slope of bit error rate performance in the medium to high signal-to-noise ratio regions. However, most of the existing…

Signal Processing · Electrical Eng. & Systems 2026-01-21 Xuehan Wang , Jinhong Yuan , Jintao Wang , Kehan Huang

We investigate the dependence of the bit-error-rate (BER) caused by amplifier noise in a linear optical fiber telecommunication system on the fiber birefringence. We show that the probability distribution function (PDF) of BER obtained by…

Achievable information rates of bipolar 4- and 8-ary constellations are experimentally compared to those of intensity modulation (IM) when using an oversampled direct detection receiver. The bipolar constellations gain up to 1.8 dB over…

In this correspondence, first-tier indirect (direct) discernible constellation expansions are defined for generalized orthogonal designs. The expanded signal constellation, leading to so-called super-orthogonal codes, allows the achievement…

Information Theory · Computer Science 2011-09-06 Dumitru Mihai Ionescu , Zhiyuan Yan

Learning structured representations of visual scenes is currently a major bottleneck to bridging perception with reasoning. While there has been exciting progress with slot-based models, which learn to segment scenes into sets of objects,…

Machine Learning · Computer Science 2021-07-26 James C. R. Whittington , Rishabh Kabra , Loic Matthey , Christopher P. Burgess , Alexander Lerchner

Facial expression recognition (FER) models are typically trained on datasets with a fixed number of seven basic classes. However, recent research works point out that there are far more expressions than the basic ones. Thus, when these…

Computer Vision and Pattern Recognition · Computer Science 2024-01-24 Yuhang Zhang , Yue Yao , Xuannan Liu , Lixiong Qin , Wenjing Wang , Weihong Deng

The performance of bit-interleaved coded modulation (BICM) with bit shaping (i.e., non-equiprobable bit probabilities in the underlying binary code) is studied. For the Gaussian channel, the rates achievable with BICM and bit shaping are…

Information Theory · Computer Science 2010-01-19 Albert Guillen i Fabregas , Alfonso Martinez

The cost and accuracy of simulating complex physical systems using the Finite Element Method (FEM) scales with the resolution of the underlying mesh. Adaptive meshes improve computational efficiency by refining resolution in critical…

The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate atmospheric parameters, a fundamental task on stellar research. In this work we…

Instrumentation and Methods for Astrophysics · Physics 2022-06-27 Miguel Flores R. , Luis J. Corral , Celia R. Fierro-Santillán

Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors,…

Computer Vision and Pattern Recognition · Computer Science 2018-10-03 S. Hamid Rezatofighi , Roman Kaskman , Farbod T. Motlagh , Qinfeng Shi , Daniel Cremers , Laura Leal-Taixé , Ian Reid

This paper focuses on the non-coherent detection in ambient backscatter communication, which is highly appealing for systems where the trade-off between signaling overhead and the actual data transmission is very critical. Modeling the…

Information Theory · Computer Science 2021-04-28 J. Kartheek Devineni , Harpreet S. Dhillon

We will amalgamate the Rash model (for rectangular binary tables) and the newly introduced $\alpha$-$\beta$ models (for random undirected graphs) in the framework of a semiparametric probabilistic graph model. Our purpose is to give a…

Methodology · Statistics 2015-03-12 Marianna Bolla , Ahmed Elbanna

Bell-state measurement (BSM) on entangled states shared between quantum repeaters is the fundamental operation used to route entanglement in quantum networks. Performing BSMs on Werner states shared between repeaters leads to exponential…

Quantum Physics · Physics 2026-03-27 Ashlesha Patil , Michele Pacenti , Bane Vasić , Saikat Guha , Narayanan Rengaswamy

Several phenomena are available representing market activity: volumes, number of trades, durations between trades or quotes, volatility - however measured - all share the feature to be represented as positive valued time series. When…

Statistical Finance · Quantitative Finance 2021-07-14 Fabrizio Cipollini , Giampiero M. Gallo

The bias-variance decomposition is a central result in statistics and machine learning, but is typically presented only for the squared error. We present a generalization of the bias-variance decomposition where the prediction error is a…

Machine Learning · Computer Science 2025-11-13 David Pfau