Reviewerly/exHarmony huggingface dataset:
Realistic non-Gaussian-operation scheme in parity-detection-based Mach-Zehnder quantum interferometry

We theoretically analyze phase sensitivity using parity detection based Mach Zehnder interferometer (MZI) with the input states generated by performing non-Gaussian operations, viz., photon subtraction, photon addition, and photon catalysis on a two-mode squeezed vacuum (TMSV) state. Since these non-Gaussian operations are probabilistic, it is of utmost importance to take the success probability into account. To this end, we consider the realistic model of photon subtraction, addition, and catalysis and derive a single expression of the Wigner function for photon subtracted, added, and catalyzed TMSV state. The Wigner function is used to evaluate the lower bound on the phase sensitivity via quantum Cramer-Rao bound and parity detection based phase sensitivity in MZI. We identify the ranges of squeezing and transmissivity parameters where the non-Gaussian states provide better phase sensitivity than the TMSV state. On qualitatively taking the success probability into account, it turns out that the photon addition is the most advantageous non-Gaussian operation. We hope that the generalized Wigner function derived in this work will be useful in various quantum information protocols and state characterization.
GSV-Cities: Toward appropriate supervised visual place recognition

This paper aims to investigate representation learning for large scale visual place recognition, which consists of determining the location depicted in a query image by referring to a database of reference images. This is a challenging task due to the large-scale environmental changes that can occur over time (i.e., weather, illumination, season, traffic, occlusion). Progress is currently challenged by the lack of large databases with accurate ground truth. To address this challenge, we introduce GSV-Cities, a new image dataset providing the widest geographic coverage to date with highly accurate ground truth, covering more than 40 cities across all continents over a 14-year period. We subsequently explore the full potential of recent advances in deep metric learning to train networks specifically for place recognition, and evaluate how different loss functions influence performance. In addition, we show that performance of existing methods substantially improves when trained on GSV-Cities. Finally, we introduce a new fully convolutional aggregation layer that outperforms existing techniques, including GeM, NetVLAD and CosPlace, and establish a new state-of-the-art on large-scale benchmarks, such as Pittsburgh, Mapillary-SLS, SPED and Nordland. The dataset and code are available for research purposes at https://github.com/amaralibey/gsv-cities.
Trajectory and Velocity Planning Method of Emergency Rescue Vehicle Based on Segmented Three-Dimensional Quartic Bezier Curve

In order to ensure the driving safety, comfort, stability and high mobility of emergency rescue vehicle, and considering the actual characteristics of emergency rescue vehicles like large vehicle width, high center of gravity and large turning radius, a trajectory and velocity planning method for collision avoidance based on segmented three-dimensional quartic Bezier curve is proposed. The velocity information is regarded as the vertical coordinates of corresponding trajectory coordinates, and the three-dimensional Bezier curve is used for vehicle trajectory planning, so as to make the velocity information and vehicle trajectory coordinates correspond one by one in planning results. In addition, in order to realize the effective fit between the multiple optimization objectives and the actual needs in intelligent driving, the segmented Bezier curve is used to design the vehicle trajectory, and the vehicle trajectory is divided into collision-avoidance process and lane-alignment process, so as to realize the dynamic optimization of trajectory design objectives. The results show that the proposed trajectory and velocity planning method based on segmented three-dimensional quartic Bezier curve can effectively improve the mobility of trajectory planning results, improve lane-changing efficiency and reduce lane-changing time while ensuring the design objectives of vehicle safety, comfort and stability.
Convolutional Networks and Transformers for Mammography Classification: An Experimental Study

Convolutional Neural Networks (CNN) have received a large share of research in mammography image analysis due to their capability of extracting hierarchical features directly from raw data. Recently, Vision Transformers are emerging as viable alternative to CNNs in medical imaging, in some cases performing on par or better than their convolutional counterparts. In this work, we conduct an extensive experimental study to compare the most recent CNN and Vision Transformer architectures for whole mammograms classification. We selected, trained and tested 33 different models, 19 convolutional- and 14 transformer-based, on the largest publicly available mammography image database OMI-DB. We also performed an analysis of the performance at eight different image resolutions and considering all the individual lesion categories in isolation (masses, calcifications, focal asymmetries, architectural distortions). Our findings confirm the potential of visual transformers, which performed on par with traditional CNNs like ResNet, but at the same time show a superiority of modern convolutional networks like EfficientNet.
GRLC: Graph Representation Learning With Constraints

Contrastive learning has been successfully applied in unsupervised representation learning. However, the generalization ability of representation learning is limited by the fact that the loss of downstream tasks (e.g., classification) is rarely taken into account while designing contrastive methods. In this article, we propose a new contrastive-based unsupervised graph representation learning (UGRL) framework by 1) maximizing the mutual information (MI) between the semantic information and the structural information of the data and 2) designing three constraints to simultaneously consider the downstream tasks and the representation learning. As a result, our proposed method outputs robust low-dimensional representations. Experimental results on 11 public datasets demonstrate that our proposed method is superior over recent state-of-the-art methods in terms of different downstream tasks. Our code is available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LarryUESTC/GRLC.</i>
Moving Toward Findable, Accessible, Interoperable, Reusable Practices in Epidemiologic Research

Abstract Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible (“Cloud”) data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community.
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders

Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models (PLM) to encode item text into item representations. Despite the promising transferability, the binding between item text and item representations might be too tight, leading to potential problems such as over-emphasizing the effect of text features and exaggerating the negative impact of domain gap. To address this issue, this paper proposes VQ-Rec, a novel approach to learning Vector-Quantized item representations for transferable sequential Recommenders. The main novelty of our approach lies in the new item representation scheme: it first maps item text into a vector of discrete indices (called item code), and then employs these indices to lookup the code embedding table for deriving item representations. Such a scheme can be denoted as "text ⟹ code ⟹ representation". Based on this representation scheme, we further propose an enhanced contrastive pre-training approach, using semi-synthetic and mixed-domain code representations as hard negatives. Furthermore, we design a new cross-domain fine-tuning method based on a differentiable permutation-based network. Extensive experiments conducted on six public benchmarks demonstrate the effectiveness of the proposed approach, in both cross-domain and cross-platform settings. Code and pre-trained model are available at: https://github.com/RUCAIBox/VQ-Rec.
Cloud Computing—Security, Issues, and Solutions

Security is the most concerned aspect of cloud computing because data is located in different places around the globe and new threats are arising day by day. Data privacy and security protection are the most important concerns in cloud computing technology and are related to both hardware and software. This paper gives an overview of security issues, challenges, and proposed solutions. A very clear and classified overview is presented in this paper with respect to cloud security. Today’s cloud computing provides the best and most efficient solutions to the Information and Communication Technology (ICT) industry, but the security problems are like nightmare for the cloud service providers as well as for the customers. We have also described various service and deployment models and identified major issues and challenges. This paper has also proposed some of the vital solutions with respect to privacy and security and also focus on various vulnerabilities and known security threats and attacks.
C-system of a module over a Jf-relative monad

Let F be the category with the set of objects N and morphisms given by the functions between the standard finite sets of the corresponding cardinalities. Let Jf:F→Sets(U) be the obvious functor from this category to the category of sets in a given Grothendieck universe U. In this paper we construct, for any Jf-relative monad RR and any left RR-module LM, a C-system C(RR,LM) and explicitly compute the action of the four B-system operations on its B-sets. In the introduction we explain in detail the relevance of this result to the construction of the term C-systems of type theories.
FIRST-ORDER POSSIBILITY MODELS AND FINITARY COMPLETENESS PROOFS

Abstract This article builds on Humberstone’s idea of defining models of propositional modal logic where total possible worlds are replaced by partial possibilities. We follow a suggestion of Humberstone by introducing possibility models for quantified modal logic. We show that a simple quantified modal logic is sound and complete for our semantics. Although Holliday showed that for many propositional modal logics, it is possible to give a completeness proof using a canonical model construction where every possibility consists of finitely many formulas, we show that this is impossible to do in the first-order case. However, one can still construct a canonical model where every possibility consists of a computable set of formulas and thus still of finitely much information.
Opinion Analysis of Bi-Lingual Event Data from Social Networks

Social media platforms have become the go-to medium for connecting people in this era of the internet. Twitter has emerged as a popular platform that allowsusers to share their views on current events and political organizations, providing a wealth of political information. The aim of this study is to utilize natural language processing techniques to analyze a dataset extracted from Twitter. This involves retrieving data from Twitter, performing sentiment analysis using deeplearning approaches, and creating a Python library that classifiesinput texts as either positive or negative. The training data used in this study included the Roman-Urdu language, comprising 89793 entries. Various classification models were employed to categorize emotions, with the ensemble technique ultimately used to determine the results. The LSTM classifier achieved an accuracy of 87%, while the Bert model performed the best with 90% accuracy.
Storage Sharing Optimization Under Constraints of SLO Compliance and Performance Variability

SLO enforcement with the required strong SLO compliance and the desired low level of performance variability is necessary to ensure QoS for user applications with precisely differentiated service levels. However, for the cloud consolidating a large number of VMs rented by users, it is a great challenge to formulate an IO capacity allocation among consolidated VMs under the user-customized QoS constraints of SLO compliance and performance fluctuation for consolidated VMs. To address this challenge, we propose SASLO, an end-to-end VM-oriented control framework that supports users in customizing SLO targets and QoS constraints for each VM. SASLO can dynamically coordinate the throughput target and IO size limit for each VM adapting to the status of SLO enforcement so as to maximize the IO capacity allocation among consolidated VMs under QoS constraints. To accurately enforce time-varying throughput target, SASLO establishes a proportional-integral IO controller for each individual VM to converge the actual throughput to the target with an expected settling time. Our extensive evaluation driven by representative benchmarks demonstrates that SASLO is able to formulate a satisfactory IO capacity allocation plan for consolidated VMs under the constraints of SLO compliance and performance variability.
Plastic letters

This article examines the complex intersection of language ideologies shaping alphabetic choices in Ukrainian outdoor advertising and shop signs, focusing on alphabet mixing through the insertion of Latin letters into Cyrillic texts and the juxtaposition of parallel or alternating texts using both of these writing systems. Drawing upon ethnographic data from work with graphic designers and consumers as well as analysis of language use in signs, I argue that while alphabet mixing is often characterized as “faddish” or “youth-oriented” these practices also reflect Soviet-era ideological stances towards both Latin typefaces, seen as “plastic” letters associated with Western capitalism, and Cyrillic typefaces, seen as “rigid” forms subject to strong central control by the Soviet state. The increasing availability of personal computers with word-processing and graphic design software in Ukraine has both increased access by individuals to print technology, and promoted a new typographic aesthetic through the dissemination of Cyrillic fonts based on Latin, not Soviet or pre-Soviet Cyrillic, models.
On the Temporal Intuitionistic Fuzzy Sets

The Temporal Intuitionistic Fuzzy Sets (TIFSs) are one of the essential extensions of the ordinary Intuitionistic Fuzzy Sets (IFSs). For them different operations, relations and operators (from modal, topological and level-types) have been defined and their basic properties have been studied. In all the research by this moment, TIFSs have been defined over one time-scale, or, whenever they have been defined over different time-scales the formulas of the operations over them have been described in the simplest form. In the present paper, essentially more detailed formulas for the operations over TIFSs with different time-scales are introduced. Moreover, ideas for some new operators that can be defined over TIFSs are introduced and their basic properties are established. Some possible applications of the TIFSs are discussed.
Compilation, transcription, markup and annotation of spoken corpora

Preview this article: Compilation, transcription, markup and annotation of spoken corpora, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/ijcl.21.3.001int-1.gif
Game semantics of Martin-Löf type theory

Abstract This work presents game semantics of Martin-Löf type theory (MLTT) equipped with the One, the Zero, the N, Pi, Sigma and Id types. Game semantics interprets a wide range of logic and computation, even the polymorphic $\lambda$ -calculus; however, it has remained a well-known challenge in the past 25 years to achieve game semantics of dependent type theories such as MLTT, and past attempts lack directness or generality. For instance, the approach taken by Abramsky et al. interprets Sigma types indirectly by formal lists, not by games, making an interpretation of universes hopeless, and it is limited to a very specific class of dependent types. The difficulty of this challenge comes from a conflict between the extensionality of dependent types and the intensionality of game semantics. We overcome the challenge by inventing a novel variant of games, while we keep strategies unchanged, in such a way that this variant inherits the strong points of conventional game semantics. Also, our method enables an interpretation of subtyping on dependent types for the first time as game semantics. We finally give a new, game-semantic proof of the independence of Markov’s principle from MLTT. This proof illustrates an advantage of our intensional model over extensional ones such as Hyland’s effective topos.
A Hybrid Approach for Optimizing Carbon Footprint in InterCloud Environment

This paper focuses on the problem of workload placement in an InterCloud with the view of minimizing the carbon footprint of such a computing environment. In order to reduce the ecological impact of the data center Greenhouse Gas (GhG) emissions, this paper addresses the problem as a whole, by proposing a global mathematical formulation, based on the joint optimization of the Virtual Machine (VM) placement and their related traffics, along with a workload consolidation method and a cooling maximization technique that considers the dynamic behavior of the cooling fans. As the Virtual Machine Placement Problem (VMPP) is classified as an NP-hard problem, with the addition of the traffic embedding, the problem becomes more complex and stays NP-hard. Therefore, we propose a hybrid approach, for solving such problem and find good feasible solutions in a polynomial time. The results obtained from comparing with the exact method and other reference approaches help in assessing the efficiency of the proposed algorithm, as the carbon footprint costs are relatively close to the lower bound, with an average gap of about 3 percent, and found within a reasonable amount of time.
Towards resolving Keller’s cube tiling conjecture in dimension seven

Abstract A cube tiling of ℝ d is a family of pairwise disjoint cubes [0, 1) d + T = {[0, 1) d + t : t ∈ T } such that ∪ t ∈ T ([0, 1) d + t ) = ℝ d . Two cubes [0, 1) d + t , [0, 1) d + s are called a twin pair if | t j − s j | = 1 for some j ∈ [ d ] = {1, ⋅, d } and t i = s i for every i ∈ [ d ]∖{ j }. In 1930, Keller conjectured that in every cube tiling of ℝ d there is a twin pair. For x ∈ ℝ d and i ∈ [ d ], let L ( T , x , i ) be the set of all i th coordinates t i of vectors t ∈ T such that ([0, 1) d + t )∩([0, 1] d + x )≠∅ and t i ≤ x i . Let <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mi>r</m:mi> <m:mo>−</m:mo> </m:msup> <m:mo stretchy="false">(</m:mo> <m:mi>T</m:mi> <m:mo stretchy="false">)</m:mo> <m:mo>=</m:mo> <m:mrow> <m:munder> <m:mo form="prefix">min</m:mo> <m:mrow> <m:mi>x</m:mi> <m:mo>∈</m:mo> <m:msup> <m:mrow> <m:mi mathvariant="double-struck">R</m:mi> </m:mrow> <m:mi>d</m:mi> </m:msup> </m:mrow> </m:munder> </m:mrow> <m:mrow> <m:munder> <m:mo form="prefix" movablelimits="true">max</m:mo> <m:mrow> <m:mn>1</m:mn> <m:mo>≤</m:mo> <m:mi>i</m:mi> <m:mo>≤</m:mo> <m:mi>d</m:mi> </m:mrow> </m:munder> </m:mrow> <m:mrow> <m:mo>|</m:mo> </m:mrow> <m:mi>L</m:mi> <m:mo stretchy="false">(</m:mo> <m:mi>T</m:mi> <m:mo>,</m:mo> <m:mi>x</m:mi> <m:mo>,</m:mo> <m:mi>i</m:mi> <m:mo stretchy="false">)</m:mo> <m:mrow> <m:mo>|</m:mo> </m:mrow> </m:math> $r^-(T)=\min_{x\in \mathbb{R}^d} \max_{1\leq i\leq d}|L(T,x,i)|$ and <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mi>r</m:mi> <m:mo>+</m:mo> </m:msup> <m:mo stretchy="false">(</m:mo> <m:mi>T</m:mi> <m:mo stretchy="false">)</m:mo> <m:mo>=</m:mo> <m:mrow> <m:munder> <m:mo form="prefix" movablelimits="true">max</m:mo> <m:mrow> <m:mi>x</m:mi> <m:mo>∈</m:mo> <m:msup> <m:mrow> <m:mi mathvariant="double-struck">R</m:mi> </m:mrow> <m:mi>d</m:mi> </m:msup> </m:mrow> </m:munder> </m:mrow> <m:mrow> <m:munder> <m:mo form="prefix" movablelimits="true">max</m:mo> <m:mrow> <m:mn>1</m:mn> <m:mo>≤</m:mo> <m:mi>i</m:mi> <m:mo>≤</m:mo> <m:mi>d</m:mi> </m:mrow> </m:munder> </m:mrow> <m:mrow> <m:mo>|</m:mo> </m:mrow> <m:mi>L</m:mi> <m:mo stretchy="false">(</m:mo> <m:mi>T</m:mi> <m:mo>,</m:mo> <m:mi>x</m:mi> <m:mo>,</m:mo> <m:mi>i</m:mi> <m:mo stretchy="false">)</m:mo> <m:mrow> <m:mo>|</m:mo> </m:mrow> </m:math> $r^ + (T)=\max_{x\in \mathbb{R}^d} \max_{1\leq i\leq d}|L(T,x,i)|$ . Before 2019 it was known that Keller’s conjecture is true for dimensions d ≤ 6 and false for all dimensions d = 8. Moreover, in dimension 7 it was known to be true if r − ( T ) ≤ 2 or r + ( T ) = 5. The present paper resolves the case r + ( T ) = 4. At the end of 2019, when the paper was still under review, Brakensiek et al. resolved the cases r + ( T ) ∈ {3, 4, 6}, proving thereby Keller’s conjecture in dimension 7.
AGATE: Adaptive Gray Area-Based TEchnique to Cluster Virtual Machines with Similar Behavior

As cloud computing data centers grow in size and complexity to accommodate an increasing number of virtual machines, the scalability of monitoring and management processes becomes a major challenge. Recent research studies show that automatically clustering virtual machines that are similar in terms of resource usage may address the scalability issues of IaaS clouds. Existing solutions provide high clustering accuracy at the cost of very long observation periods, that are not compatible with dynamic cloud scenarios where VMs may frequently join and leave. We propose a novel technique, namely Adaptive Gray Area-based TEchnique (AGATE), that provides accurate clustering results for a subset of VMs after a very short time. This result is achieved by introducing elements of fuzzy logic into the clustering process to identify the VMs with undecided clustering assignment (the so-called gray area), that should be monitored for longer periods. To evaluate the performance of the proposed solution, we apply the technique to multiple case studies with real and synthetic workloads. We demonstrate that our solution can correctly identify the behavior of a high percentage of VMs after few hours of observations, and significantly reduce the data required for monitoring with respect to state-of-the-art solutions.