22 papers · ranked by Valyu relevance
Yuxin Jiang, Wenqin Zhang, Lele Wang
Gradient coding is a distributed computing technique aiming to provide robustness against slow or non-responsive computing nodes, known as stragglers, while balancing the computational load for responsive computing nodes. Among existing gradient codes, a construction based on combinatorial designs, called BIBD gradient…
Sinong Wang, Jiashang Liu, Ness B. Shroff
It has been established that when the gradient coding problem is distributed among n servers, the computation load (number of stored data partitions) of each worker is at least s + 1 in order to resists s stragglers [1]. This scheme incurs a large overhead when the number of stragglers s is large. In this paper, we…
Ari S. Benjamin, Ling-Qi Zhang, Cheng Qiu, Alan Stocker + 1 more
Animal sensory systems are more sensitive to common features in the environment than uncommon features. For example, small deviations from the more frequently encountered horizontal orientations can be more easily detected than small deviations from the less frequent diagonal ones. Here we find that artificial neural…
Qiqi Zheng, Yongfeng Li, Jieqiu Zhang, Hua Ma + 7 more
'Yongqiang Pang' 'Yajuan Han' 'Sai Sui' 'Yang Shen' 'Hongya Chen' 'Shaobo Qu'] A new concept of the coding phase gradient metasurface (CPGM) is proposed, which is constructed using the phase gradient metasurface as the coding elements. Different from the previous coding metasurface (CM), both the coding sequences and…
Muhammet Balcılar, Bharath Bhushan Damodaran, Karam Naser, Franck Galpin + 1 more
'Franck Galpin' 'Pierre Hellier'] End-to-end image/video codecs are getting competitive compared to traditional compression techniques that have been developed through decades of manual engineering efforts. These trainable codecs have many advantages over traditional techniques such as easy adaptation on perceptual…
Tadashi Wadayama, Lantian Wei
—This paper presents the Gradient Flow (GF) decoding for LDPC codes. GF decoding, a continuous-time methodology based on gradient flow, employs a potential energy function associated with bipolar codewords of LDPC codes. The decoding process of the GF decoding is concisely defined by an ordinary differential equation…
Julio A. Peraza, Taylor Salo, Michael C. Riedel, Katherine L. Bottenhorn + 14 more
Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end, and transmodal regions associated with the…
Bingjun Guo, Yazhi Liu, Chunyang Zhang, Francesco Marcelloni
Running Deep Neural Networks (DNNs) in distributed Internet of Things (IoT) nodes is a promising scheme to enhance the performance of IoT systems. However, due to the limited computing and communication resources of the IoT nodes, the communication efficiency of the distributed DNN training strategy is a problem…
Reinder Vos de Wael, Oualid Benkarim, Casey Paquola, Sara Lariviere + 10 more
Understanding how higher order cognitive function emerges from the underlying brain structure depends on quantifying how the behaviour of discrete regions are integrated within the broader cortical landscape. Recent work has established that this macroscale brain organization and function can be quantified in a compact…
Xing Xu, Jieying Zhang, Qi Zhu, Tiansheng Xia
Gradient colors are widely used in product design. The variation of gradient colors muting a color as a series of steps from bright to dull creates a soft and gradual impression while also affecting people's perceptions. This study manipulates the types of gradient colors to explore the relationship between color…
MOHAMED HASSAN, ALEKSANDAR VAKANSKI, BOYU ZHANG, MIN XIAN
The generalization performance of deep neural networks (DNNs) is a critical factor in achieving robust model behavior on unseen data. Recent studies have highlighted the importance of sharpness-based measures in promoting generalization by encouraging convergence to flatter minima. Among these approaches…
Sorinel A. Oprisan, Ana Oprisan, Xiangjie Kong
This study presents a novel analytical framework for understanding the relationship between the image gradients and the symmetries of the Gray Level Co-occurrence Matrix (GLCM). Analytical expression for four key features-sum average (SA), sum variance (SV), difference variance (DV), and entropy-were derived to capture…
Tony Shaska
We introduce the Graded Transformer framework, embedding algebraic inductive biases via grading transformations on vector spaces. Extending Graded Neural Networks (GNNs), we propose the Linearly Graded Transformer (LGT) and Exponentially Graded Transformer (EGT), which apply parameterized scaling—via fixed or learnable…
Leonard Schmiester, Daniel Weindl, Jan Hasenauer
Unknown parameters of dynamical models are commonly estimated from experimental data. However, while various efficient optimization and uncertainty analysis methods have been proposed for quantitative data, methods for qualitative data are rare and suffer from bad scaling and convergence. Here, we propose an efficient…
Il Memming Park, Jonathan W. Pillow
The efficient coding hypothesis, which proposes that neurons are optimized to maximize information about the environment, has provided a guiding theoretical framework for sensory and systems neuroscience. More recently, a theory known as the Bayesian Brain hypothesis has focused on the brain’s ability to integrate…
Sarah Rutan, Kathryn Cash, Dwight Stoll II
The present work describes a re-parameterization of the Neue Kuss (NK) model for describing retention in liquid chromatography, and this re-parameterized model is used fit a large set of isocratic retention measurements with improved convergence properties relative to the original parameterization of the model. Next…
Authors not listed
This work provides a rigorous theoretical investigation of selective error correction strategies for variational quantum algorithms, with focus on understanding the interplay between error suppression, circuit trainability, and computational resource requirements. We develop a mathematical framework that characterizes…
Hyunmin Cho, Jaejun Yoo, Kyong Hwan Jin
We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveals that sinusoidal activations induce a harmonic line spectrum, providing a spectral account of how recurrent unrolling enriches the effective spectral support. We…
Wanghuai Zhou, Deping Hu, Arkajit Mandal, Pengfei Huo
We derive a rigorous nuclear gradient for a molecule-cavity hybrid system using the Quantum Electrodynamics Hamiltonian. We treat the electronic-photonic DOFs as the quantum subsystem, and the nuclei as the classical subsystem. Using the adiabatic basis for the electronic DOF and the Fock basis for the photonic DOF…
Tenzin Chan, De Wen Soh, Christopher Hillar, Kichun Lee + 1 more
'Boris Ryabko'] Oftentimes in a complex system it is observed that as a control parameter is varied, there are certain intervals during which the system undergoes dramatic change. In biology especially, these signatures of criticality are thought to be connected with efficient computation and information processing.…
Michael Hutcheon, Andrew Teale
Algorithms are presented for performing a topological analysis of an arbitrary function, evaluated on an arbitrary grid of points. These algorithms work strictly by post-processing the data and require no additional function evaluations. This is achieved by connecting the grid points with a neighbourhood graph…
Kevin Robben, Christopher Cheatum
We report a comprehensive study of the efficacy of least-squares fitting of multidimensional spectra to generalized Kubo lineshape models and introduce a novel least-squares fitting metric, termed the Scale Invariant Gradient Norm (SIGN), that enables a highly reliable and versatile algorithm. The precision of…