Multiplexed Streaming Codes for Messages With Different Decoding Delays in Channel with Burst and Random Erasures

In a real-time transmission scenario, messages are transmitted through a channel that is subject to packet loss. The destination must recover the messages within the required deadline. In this paper, we consider a setup where two different types of messages with distinct decoding deadlines are transmitted through a channel that can introduce burst erasures of a length at most $B$, or $N$ random erasures. The message with a short decoding deadline $T_u$ is referred to as an urgent message, while the other one with a decoding deadline $T_v$ ($T_v > T_u$) is referred to as a less urgent message. We propose a merging method to encode two message streams of different urgency levels into a single flow. We consider the scenario where $T_v > T_u + B$. We establish that any coding strategy based on this merging approach has a closed-form upper limit on its achievable sum rate. Moreover, we present explicit constructions within a finite field that scales quadratically with the imposed delay, ensuring adherence to the upper bound. In a given parameter configuration, we rigorously demonstrate that the sum rate of our proposed streaming codes consistently surpasses that of separate encoding, which serves as a baseline for comparison

Privacy-Preserving Polynomial Computing Over Distributed Data

In this letter, we delve into a scenario where a user aims to compute polynomial functions using their own data as well as data obtained from distributed sources. To accomplish this, the user enlists the assistance of $N$ distributed workers, thereby defining a problem we refer to as privacy-preserving polynomial computing over distributed data. To address this challenge, we propose an approach founded upon Lagrange encoding. Our method not only possesses the ability to withstand the presence of stragglers and byzantine workers but also ensures the preservation of security. Specifically, even if a coalition of $X$ workers collude, they are unable to acquire any knowledge pertaining to the data originating from the distributed sources or the user

Towards milli-Hertz laser frequency noise on a chip

Narrow-linewidth lasers are important to many applications spanning precision metrology to sensing systems. Their miniaturization in the form of on-chip lasers is receiving increasing attention. Here, a noise level that is consistent with a fundamental frequency noise of 9 mHz⋅Hz/Hz linewidth (60 mHz linewidth) is measured in a Brillouin laser. The results leverage ultra-high-Q silica-on-silicon resonators and point towards a new performance target for chip-based laser platforms

Kernel-SSL: Kernel KL Divergence for Self-Supervised Learning

Contrastive learning usually compares one positive anchor sample with lots of negative samples to perform Self-Supervised Learning (SSL). Alternatively, non-contrastive learning, as exemplified by methods like BYOL, SimSiam, and Barlow Twins, accomplishes SSL without the explicit use of negative samples. Inspired by the existing analysis for contrastive learning, we provide a reproducing kernel Hilbert space (RKHS) understanding of many existing non-contrastive learning methods. Subsequently, we propose a novel loss function, Kernel-SSL, which directly optimizes the mean embedding and the covariance operator within the RKHS. In experiments, our method Kernel-SSL outperforms state-of-the-art methods by a large margin on ImageNet datasets under the linear evaluation settings. Specifically, when performing 100 epochs pre-training, our method outperforms SimCLR by 4.6%

Towards milli-Hertz laser frequency noise on a chip

Narrow-linewidth lasers are important to many applications spanning precision metrology to sensing systems. Their miniaturization in the form of on-chip lasers is receiving increasing attention. Here, a noise level that is consistent with a fundamental frequency noise of 9 mHz⋅Hz/Hz linewidth (60 mHz linewidth) is measured in a Brillouin laser. The results leverage ultra-high-Q silica-on-silicon resonators and point towards a new performance target for chip-based laser platforms

Contrastive Learning Is Spectral Clustering On Similarity Graph

Contrastive learning is a powerful self-supervised learning method, but we have a limited theoretical understanding of how it works and why it works. In this paper, we prove that contrastive learning with the standard InfoNCE loss is equivalent to spectral clustering on the similarity graph. Using this equivalence as the building block, we extend our analysis to the CLIP model and rigorously characterize how similar multi-modal objects are embedded together. Motivated by our theoretical insights, we introduce the kernel mixture loss, incorporating novel kernel functions that outperform the standard Gaussian kernel on several vision datasets.Comment: We express our gratitude to the anonymous reviewers for their valuable feedbac

RelationMatch: Matching In-batch Relationships for Semi-supervised Learning

Semi-supervised learning has achieved notable success by leveraging very few labeled data and exploiting the wealth of information derived from unlabeled data. However, existing algorithms usually focus on aligning predictions on paired data points augmented from an identical source, and overlook the inter-point relationships within each batch. This paper introduces a novel method, RelationMatch, which exploits in-batch relationships with a matrix cross-entropy (MCE) loss function. Through the application of MCE, our proposed method consistently surpasses the performance of established state-of-the-art methods, such as FixMatch and FlexMatch, across a variety of vision datasets. Notably, we observed a substantial enhancement of 15.21% in accuracy over FlexMatch on the STL-10 dataset using only 40 labels. Moreover, we apply MCE to supervised learning scenarios, and observe consistent improvements as well

Information Flow in Self-Supervised Learning

In this paper, we provide a comprehensive toolbox for understanding and enhancing self-supervised learning (SSL) methods through the lens of matrix information theory. Specifically, by leveraging the principles of matrix mutual information and joint entropy, we offer a unified analysis for both contrastive and feature decorrelation based methods. Furthermore, we propose the matrix variational masked auto-encoder (M-MAE) method, grounded in matrix information theory, as an enhancement to masked image modeling. The empirical evaluations underscore the effectiveness of M-MAE compared with the state-of-the-art methods, including a 3.9% improvement in linear probing ViT-Base, and a 1% improvement in fine-tuning ViT-Large, both on ImageNet

Linewidth enhancement factor in a microcavity Brillouin laser

The linewidth of regenerative oscillators is enhanced by amplitude–phase coupling of the oscillator field [Phys. Rev. 160, 290 (1967)]. In laser oscillators, this effect is well known for its impact on semiconductor laser performance. Here, this coupling is studied in Brillouin lasers. Because their gain is parametric, the coupling and linewidth enhancement are shown to originate from phase mismatch. The theory is confirmed by measurement of linewidth in a microcavity Brillouin laser, and enhancements as large as 50× are measured. The results show that pump wavelength and device temperature should be carefully selected and controlled to minimize linewidth. More generally, this work provides a new perspective on the linewidth enhancement effect

A cost-effective software testing strategy employing online feedback information

An online partitioning strategy is presented, in which test cases are selected based on feedback information collected during the testing process. The strategy differs from con- ventional approaches because the partitioning is performed online rather than off-line and because the partitioning is not based on program code or specifications. It can, therefore, be implemented in the absence of the source code or specification of the program under test. The cost-effectiveness of the proposed strategy has been empirically investigated with a set of subject programs, namely, SPACE, SED, GREP, and the Siemens Suite of Programs. The results demonstrate that the proposed strategy constantly achieves large savings in terms of the total number of test case executions needed to detect all faults