Miracle-dz/UniFAR-FTUs huggingface dataset:
Power and Performance Analysis of Persistent Key-Value Stores

With the current rate of data growth, processing needs are becoming difficult to fulfill due to CPU power and energy limitations. Data serving systems and especially persistent key-value stores have become a substantial part of data processing stacks in the data center, providing access to massive amounts of data for applications and services. Key-value stores exhibit high CPU and I/O overheads because of their constant need to reorganize data on the devices. In this paper, we examine the efficiency of two key-value stores on four servers of different generations and with different CPU architectures. We use RocksDB, a key-value that is deployed widely, e.g. in Facebook, and Kreon, a research key-value store that has been designed to reduce CPU overhead. We evaluate their behavior and overheads on an ARM-based microserver and three different generations of x86 servers. Our findings show that microservers have better power efficiency in the range of 0.68-3.6x with a comparable tail latency.
Functionalism as a Species of Reduction

This is the first of four papers prompted by a recent literature about a doctrine dubbed spacetime functionalism. This paper gives our general framework for discussing functionalism. Following Lewis, we take it as a species of reduction. We start by expounding reduction in a broadly Nagelian sense. Then we argue that Lewis’ functionalism is an improvement on Nagelian reduction. This paper thereby sets the scene for the other papers, which will apply our framework to theories of space and time. (So those papers address the space and time literature: both recent and older, and physical as well as philosophical literature. But the four papers can be read independently.) Overall, we come to praise spacetime functionalism, not to bury it. But we criticize the recent philosophical literature for failing to stress: (i) functionalism’s being a species of reduction (in particular: reduction of chrono- geometry to the physics of matter and radiation); (ii) functionalism’s idea, not just of specifying a concept by its functional role, but of specifying several concepts simultaneously by their roles; (iii) functionalism’s providing bridge laws that are mandatory, not optional: they are statements of identity (or co-extension) that are conclusions of a deductive argument, rather than contingent guesses or verbal stipulations; and once we infer them, we have a reduction in a Nagelian sense. On the other hand, some of the older philosophical literature, and the mathematical physics literature, is faithful to these ideas (i) to (iii)—as are Torretti’s writings. (But of course, the word ‘functionalism’ is not used; and themes like simultaneous unique definition are not articulated.) Thus in various papers, falling under various research programmes, the unique definability of a chrono-geometric concept (or concepts) in terms of matter and radiation, and a corresponding bridge law and reduction, is secured by a precise theorem. Hence our desire to celebrate these results as rigorous renditions of spacetime functionalism.
Quantum information processing with bosonic qubits in circuit QED

The unique features of quantum theory offer a powerful new paradigm for information processing. Translating these mathematical abstractions into useful algorithms and applications requires quantum systems with significant complexity and sufficiently low error rates. Such quantum systems must be made from robust hardware that can coherently store, process, and extract the encoded information, as well as possess effective quantum error correction (QEC) protocols to detect and correct errors. Circuit quantum electrodynamics (cQED) provides a promising hardware platform for implementing robust quantum devices. In particular, bosonic encodings in cQED that use multi-photon states of superconducting cavities to encode information have shown success in realizing hardware-efficient QEC. Here, we review recent developments in the theory and implementation of QEC with bosonic codes and report the progress made toward realizing fault-tolerant quantum information processing with cQED devices.
Data Sanitisation Protocols for the Privacy Funnel with Differential Privacy Guarantees

In the Open Data approach, governments and other public organisations want to share their datasets with the public, for accountability and to support participation. Data must be opened in such a way that individual privacy is safeguarded. The Privacy Funnel is a mathematical approach that produces a sanitised database that does not leak private data beyond a chosen threshold. The downsides to this approach are that it does not give worst-case privacy guarantees, and that finding optimal sanitisation protocols can be computationally prohibitive. We tackle these problems by using differential privacy metrics, and by considering local protocols which operate on one entry at a time. We show that under both the Local Differential Privacy and Local Information Privacy leakage metrics, one can efficiently obtain optimal protocols. Furthermore, Local Information Privacy is both more closely aligned to the privacy requirements of the Privacy Funnel scenario, and more efficiently computable. We also consider the scenario where each user has multiple attributes, for which we define Side-channel Resistant Local Information Privacy, and we give efficient methods to find protocols satisfying this criterion while still offering good utility. Finally, we introduce Conditional Reporting, an explicit LIP protocol that can be used when the optimal protocol is infeasible to compute, and we test this protocol on real-world and synthetic data. Experiments on real-world and synthetic data confirm the validity of these methods.
A note on the Weingarten function

The aim of this note is to compare work of Formanek \cite{formanek2} on a certain construction of central polynomials with that of Collins \cite{Coll} on integration on unitary groups. These two quite disjoint topics share the construction of the same function on the symmetric group, which the second author calls {\em Weingarten function}. By joining these two approaches we succeed in giving a simplified and {\em very natural} presentation of both Formanek and Collins's Theory.
Two-stage building energy consumption clustering based on temporal and peak demand patterns

Analyzing smart meter data to understand energy consumption patterns helps utilities and energy providers perform customized demand response operations. Existing energy consumption segmentation techniques use assumptions that could result in reduced quality of clusters in representing their members. We address this limitation by introducing a two-stage clustering method that more accurately captures load shape temporal patterns and peak demands. In the first stage, load shapes are clustered by allowing a large number of clusters to accurately capture variations in energy use patterns and cluster centroids are extracted by accounting for shape misalignments. In the second stage, clusters of similar centroid and power magnitude range are merged by using Dynamic Time Warping. We used three datasets consisting of ~250 households (~15000 profiles) to demonstrate the performance improvement, compared to baseline methods, and discuss the impact on energy management.
On Multiple-Access in Queue-Length Sensitive Systems

We consider transmission of packets over queue-length sensitive unreliable links, where packets are randomly corrupted through a noisy channel whose transition probabilities are modulated by the queue-length. The goal is to characterize the capacity of this channel. We particularly consider multiple-access systems, where transmitters dispatch encoded symbols over a system that is a superposition of continuous-time <inline-formula> <tex-math notation="LaTeX">$\mathsf {GI}_{k}/\mathsf {GI}/1$ </tex-math></inline-formula> queues. A server receives and processes symbols in order of arrivals with queue-length dependent noise. We first determine the capacity of single-user queue-length dependent channels. Further, we characterize the best and worst dispatch processes for <inline-formula> <tex-math notation="LaTeX">$\mathsf {GI}/\mathsf {M}/1$ </tex-math></inline-formula> queues and the best and worst service processes for <inline-formula> <tex-math notation="LaTeX">$\mathsf {M}/\mathsf {GI}/1$ </tex-math></inline-formula> queues. Then, the multiple-access channel capacity is obtained using point processes. When the number of transmitters is large and each arrival process is sparse, the superposition of arrivals approaches a Poisson point process. In characterizing the Poisson approximation, we show that the capacity of the multiple-access system converges to that of a single-user <inline-formula> <tex-math notation="LaTeX">$\mathsf {M}/\mathsf {GI}/1$ </tex-math></inline-formula> queue-length dependent system, and an upper bound on the convergence rate is obtained. This implies that the best and worst server behaviors of single-user <inline-formula> <tex-math notation="LaTeX">$\mathsf {M}/\mathsf {GI}/1$ </tex-math></inline-formula> queues are preserved in the sparse multiple-access case.
Contextual extensions of quantum gravity

We present a simple way of incorporating the structure of contextual extensions into quantum gravity models. The contextual extensions of $C^*$-algebras, originally proposed for contextual hidden variables, are generalized to the cones indexed by the contexts and their limit in a category. By abstracting the quantum gravity models as functors, we study the contextual extensions as the categorical limits of these functors in several quantum gravity models. Such contextual extensions of quantum gravity models are useful for building topos-theoretic models of quantum gravity.
K-way p-spectral clustering on Grassmann manifolds

Spectral methods have gained a lot of recent attention due to the simplicity of their implementation and their solid mathematical background. We revisit spectral graph clustering, and reformulate in the $p$-norm the continuous problem of minimizing the graph Laplacian Rayleigh quotient. The value of $p \in (1,2]$ is reduced, promoting sparser solution vectors that correspond to optimal clusters as $p$ approaches one. The computation of multiple $p$-eigenvectors of the graph $p$-Laplacian, a nonlinear generalization of the standard graph Laplacian, is achieved by the minimization of our objective function on the Grassmann manifold, hence ensuring the enforcement of the orthogonality constraint between them. Our approach attempts to bridge the fields of graph clustering and nonlinear numerical optimization, and employs a robust algorithm to obtain clusters of high quality. The benefits of the suggested method are demonstrated in a plethora of artificial and real-world graphs. Our results are compared against standard spectral clustering methods and the current state-of-the-art algorithm for clustering using the graph $p$-Laplacian variant.
On symbolic factors of $\cS$-adic subshifts of finite alphabet rank

In this article, we prove two results about minimal $\cS$-adic subshifts with finite alphabet rank: 1. they have finite topological rank, and 2. symbolic factors between systems of this kind are always almost constant-to-1. Both results are consequence of some rigidity properties of the underlying combinatorics of words in this class of subshifts. As a corollary of the first result, we answer a question of Donoso, Durand, Maass and Petite.
Online Search With a Hint

The linear search problem, informally known as the cow path problem, is one of the fundamental problems in search theory. In this problem, an immobile target is hidden at some unknown position on an unbounded line, and a mobile searcher, initially positioned at some specific point of the line called the root, must traverse the line so as to locate the target. The objective is to minimize the worst-case ratio of the distance traversed by the searcher to the distance of the target from the root, which is known as the competitive ratio of the search. In this work we study this problem in a setting in which the searcher has a hint concerning the target. We consider three settings in regards to the nature of the hint: i) the hint suggests the exact position of the target on the line; ii) the hint suggests the direction of the optimal search (i.e., to the left or the right of the root); and iii) the hint is a general k-bit string that encodes some information concerning the target. Our objective is to study the Pareto-efficiency of strategies in this model. Namely, we seek optimal, or near-optimal tradeoffs between the searcher's performance if the hint is correct (i.e., provided by a trusted source) and if the hint is incorrect (i.e., provided by an adversary).
MDCN: Multi-Scale Dense Cross Network for Image Super-Resolution

Convolutional neural networks have been proven to be of great benefit for single-image super-resolution (SISR). However, previous works do not make full use of multi-scale features and ignore the inter-scale correlation between different upsampling factors, resulting in sub-optimal performance. Instead of blindly increasing the depth of the network, we are committed to mining image features and learning the inter-scale correlation between different upsampling factors. To achieve this, we propose a Multi-scale Dense Cross Network (MDCN), which achieves great performance with fewer parameters and less execution time. MDCN consists of multi-scale dense cross blocks (MDCBs), hierarchical feature distillation block (HFDB), and dynamic reconstruction block (DRB). Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model. It is worth noting that all these modules can run independently. It means that these modules can be selectively plugged into any CNN model to improve model performance. Extensive experiments show that MDCN achieves competitive results in SISR, especially in the reconstruction task with multiple upsampling factors. The code is provided at https://github.com/MIVRC/MDCN-PyTorch.
SuSy: a blockchain-agnostic cross-chain asset transfer gateway protocol based on Gravity

This document is a specialized technical description of one of the potential implementations of a second layer protocol over Gravity, a blockchain-/token-agnostic decentralized oracle protocol. The SuSy protocol prescribes an implementation of cross-chain transfers of digital assets (tokens) in blockchain networks that support smart contracts, focused primarily on popular blockchains with varying architectures, consensuses and cryptography. SuSy is centered exclusively around technical implementation of transfers, without bringing any incentive models for cross-chain transfer providers. In addition, we describe the most popular inter-chain communication solutions such as Polkadot, Cosmos Hub, Rainbow and RenVM, as a backdrop for the new solution proposed in this paper.
Suppression of the optical linewidth and spin decoherence of a quantum spin center in a p–n diode

We present a quantitative theory of the suppression of the optical linewidth due to charge fluctuation noise in a ${p}$$-$${n}$ diode, recently observed in Anderson et al., Science 366, 1225 (2019). We connect the local electric field with the voltage across the diode, allowing for the identification of the defect depth from the experimental threshold voltage. Furthermore, we show that an accurate description of the decoherence of such spin centers requires a complete spin$-$1 formalism that yields a bi-exponential decoherence process, and predict how reduced charge fluctuation noise suppresses the spin center's decoherence rate.
Multi-View Spectral Clustering With High-Order Optimal Neighborhood Laplacian Matrix

Multi-view spectral clustering can effectively reveal the intrinsic clustering structure among data by performing clustering on the learned optimal embedding across views. Though demonstrating promising performance in various applications, most of existing methods usually linearly combine a group of pre-specified first-order Laplacian matrices to construct the optimal Laplacian matrix, which may result in limited representation capability and insufficient information exploitation. Also, storing and implementing complex operations on the <inline-formula><tex-math notation="LaTeX">$n\times n$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>n</mml:mi><mml:mo>×</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="liu-ieq1-3025100.gif"/></alternatives></inline-formula> Laplacian matrices incur intensive storage and computation complexity. To address these issues, this paper first proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix, and then extends it to the late fusion version for accurate and efficient multi-view clustering. Specifically, our proposed algorithm generates the optimal Laplacian matrix by searching the neighborhood of the linear combination of both the first-order and high-order base Laplacian matrices simultaneously. By this way, the representative capacity of the learned optimal Laplacian matrix is enhanced, which is helpful to better utilize the hidden high-order connection information among data, leading to improved clustering performance. We design an efficient algorithm with proved convergence to solve the resultant optimization problem. Extensive experimental results on nine datasets demonstrate the superiority of our algorithm against state-of-the-art methods, which verifies the effectiveness and advantages of the proposed algorithm.
Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning

In recent years, tremendous progress has been made on numerical algorithms for solving partial differential equations (PDEs) in a very high dimension, using ideas from either nonlinear (multilevel) Monte Carlo or deep learning. They are potentially free of the curse of dimensionality for many different applications and have been proven to be so in the case of some nonlinear Monte Carlo methods for nonlinear parabolic PDEs. In this paper, we review these numerical and theoretical advances. In addition to algorithms based on stochastic reformulations of the original problem, such as the multilevel Picard iteration and the deep backward stochastic differential equations method, we also discuss algorithms based on the more traditional Ritz, Galerkin, and least square formulations. We hope to demonstrate to the reader that studying PDEs as well as control and variational problems in very high dimensions might very well be among the most promising new directions in mathematics and scientific computing in the near future.
Laplacian Matrices for Extremely Balanced and Unbalanced Phylogenetic Trees

Phylogenetic trees are important tools in the study of evolutionary relationships between species. Measures such as the index of Sackin, Colless, and Total Cophenetic have been extensively used to quantify tree balance, one key property of phylogenies. Recently a new proposal has been introduced, based on the spectrum of the Laplacian matrix associated with the tree. In this work, we calculate the Laplacian matrix analytically for two extreme cases, corresponding to fully balanced and fully unbalanced trees. For maximally balanced trees no closed form for the Laplacian matrix was derived, but we present an algorithm to construct it. We show that Laplacian matrices of fully balanced trees display self-similar patterns that result in highly degenerated eigenvalues. Degeneracy is the main signature of this topology, since it is totally absent in fully unbalanced trees. We also establish some analytical and numerical results about the largest eigenvalue of Laplacian matrices for these topologies.
Review Regularized Neural Collaborative Filtering

In recent years, text-aware collaborative filtering methods have been proposed to address essential challenges in recommendations such as data sparsity, cold start problem, and long-tail distribution. However, many of these text-oriented methods rely heavily on the availability of text information for every user and item, which obviously does not hold in real-world scenarios. Furthermore, specially designed network structures for text processing are highly inefficient for on-line serving and are hard to integrate into current systems. In this paper, we propose a flexible neural recommendation framework, named Review Regularized Recommendation, short as R3. It consists of a neural collaborative filtering part that focuses on prediction output, and a text processing part that serves as a regularizer. This modular design incorporates text information as richer data sources in the training phase while being highly friendly for on-line serving as it needs no on-the-fly text processing in serving time. Our preliminary results show that by using a simple text processing approach, it could achieve better prediction performance than state-of-the-art text-aware methods.
APVAS: Reducing Memory Size of AS_PATH Validation by Using Aggregate Signatures

The \textit{BGPsec} protocol, which is an extension of the border gateway protocol (BGP), uses digital signatures to guarantee the validity of routing information. However, BGPsec's use of digital signatures in routing information causes a lack of memory in BGP routers and therefore creates a gaping security hole in today's Internet. This problem hinders the practical realization and implementation of BGPsec. In this paper, we present APVAS (AS path validation based on aggregate signatures), a new validation method that reduces memory consumption of BGPsec when validating paths in routing information. To do this, APVAS relies on a novel aggregate signature scheme that compresses individually generated signatures into a single signature in two ways, i.e., in sequential and interactive fashions. Furthermore, we implement a prototype of APVAS on \textit{BIRD Internet Routing Daemon} and demonstrate its efficiency on actual BGP connections. Our results show that APVAS can reduce memory consumption by 80\% in comparison with the conventional BGPsec.
Formation of Dust Filaments in the Diffuse Envelopes of Molecular Clouds

The path to understanding star formation processes begins with the study of the formation of molecular clouds. The outskirts of these clouds are characterized by low column densities that allow the penetration of ultraviolet radiation, resulting in a nonnegligible ionization fraction and the charging of the small dust grains that are mixed with the gas; this diffuse phase is then coupled to the ambient magnetic field. Despite the general assumption that dust and gas are tightly correlated, several observational and theoretical studies have reported variations in the dust-to-gas ratio toward diffuse and cold clouds. In this work, we present the implementation of a new charged particles module for analyzing the dust dynamics in molecular cloud envelopes. We study the evolution of a single population of small charged grains (0.05 μm) in the turbulent, magnetized molecular cloud envelope using this module. We show that variations in the dust-to-gas ratio arise due to the coupling of the grains with the magnetic field, forming elongated dust structures decoupled from the gas. This emphasizes the importance of considering the dynamics of charged dust when simulating the different phases of the interstellar medium, especially for star formation studies.