bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical simulation of biomagnetic pulsatile flow through a channel
Authors: Kaustubh  , Maniyeri Ranjith
Published: December 2023

The study of biological fluids in the presence of a magnetic field is known as biomagnetic fluid dynamics (BFD). The research work in BFD has been rapidly growing due to its applications in developing magnetic devices used for cell separation, targeted drug delivery and cancer tumor treatment. This study aims to examine the biomagnetic fluid flow with pulsatile conditions through a channel when subjected to a magnetic field that varies in space. The nondimensional continuity and momentum equations are solved with the effect of the magnetic field added as a body force. A two-dimensional computational model is developed using the finite volume method and is implemented on a staggered grid system with the help of the semi-implicit fractional step method. The code is written using MATLAB. Numerical simulations are performed by varying the Magnetic, Reynolds and Womersley numbers. Pulsatile flow results indicate the periodic growth and decay of vortices near the source of the magnetic field. With an increase in the magnetic number from 100 to 150, 250 and 500, the maximum vorticity increases by 48.04%, 149.84% and 402.68%. A similar relation is found when varying the Reynolds number, while almost no change is found when varying the Womersley number.
Exploring the features of Von-Karman flow of tangent hyperbolic fluid over a radially stretching disk subject to heating due to porous media and viscous heating
Authors: Bartwal Priya , Upreti Himanshu , Mishra S. R. , Pandey Alok Kumar
Published: December 2023

The fluid flow over rotating disk has various applications in the field of medical sciences, science and engineering i.e. medical equipment, gas turbine rotors, rheometers, oceanic circulation, and computer storage devices. Keeping this in mind, the task of this study is to observe the tangent hyperbolic fluid flow behaviors through a rotating disk in the presence of Ohmic heating, thermal radiation, viscous dissipation and heating due to porous media. The bvp4c numerical method is applied to solve the transformed governing equations. The impact of acting parameters i.e. magnetic field, porosity parameter, radiation, Weissenberg number and Eckert number on the velocities (radial, azimuthal and axial) and temperature distributions are revealed through graphs for the case of Newtonian and non-Newtonian fluids by considering no rotation ([Formula: see text]) and rotation parameter ([Formula: see text]). From the results, it is noticed that the resistivity offered by the increasing porosity increases the rate of heat transfer in magnitude for the case of no rotation while in case of rotation, it retards significantly. For the validation of this study, a comparison of our results with previous published work is conducted.
Neural Network-Based Algorithm for Identification of Recaptured Images
Authors: Liu Changming , Sun Yanjun , Deng Lin , Sun Yan
Published: December 2023

With the improvement of digital image display technology, the “secondary imaging” caused by digital cameras is also gradually popularized, and the quality of the recaptured image formed by this imaging is also getting higher and higher, and this kind of high-quality fake image has caused great threat to digital images security. We propose a neural network-based recaptured image identification algorithm and use the difference between two types of images to build the identification algorithm in the frequency domain. The algorithm uses filtering to obtain the feature images which are the high-frequency and low-frequency filtering images, in order to further distinguish the image differences, the direction of the filtered image obtained from high-frequency images, each direction of the filtered image contains high-frequency information at different angles, and the low-frequency image is downsampled. At the same time, the low-frequency image is downsampled to obtain a multi-scale filtered image. The algorithm extracts the features from previous images as the feature values for classification, and finally uses neural networks for classification to obtain the classification results, and these prove that the algorithm presented is able to differentiate the recaptured images effectively in this paper.
Saliency and Depth-Aware Full Reference 360-Degree Image Quality Assessment
Authors: Wei Xuekai , Huang Qunyue , Fang Bin , Ouyang Lei , Xian Weizhi , Luo Jun , Pu Huayan , Xu Xueyong , Lu Chang , Nan Hao , Liu Xu , Li Yachao , Zhou Mingliang
Published: December 2023

With the widespread adoption of virtual reality and 360-degree video, there is a pressing need for objective metrics to assess quality in this immersive panoramic format reliably. However, existing image quality assessment models developed for traditional fixed-viewpoint content do not fully consider the specific perceptual issues involved in 360-degree viewing. This paper proposes a 360-degree image full-reference quality assessment (FR-IQA) methodology based on a multi-channel architecture. The proposed 360-degree FR-IQA method further optimizes and identifies the distorted image quality using two easily obtained useful saliency and depth-aware image features. The convolutional neural network (CNN) is designed for training. Furthermore, the proposed method accounts for predicting user viewing behaviors within 360-degree images, which will further benefit the multi-channel CNN architecture and enable the weighted average pooling of the predicted FR-IQA scores. The performance is evaluated on publicly available databases to demonstrate the advantages brought by the proposed multi-channel model in performance evaluation and cross-database evaluation experiments, where it outperforms other state-of-the-art ones. Moreover, an ablation study exhibits good generalization ability and robustness.
Analysis and Improvement of Channel Equalization for Underwater Acoustic Communication Based on Variational Mode Decomposition and CNN-BiLSTM
Authors: Jiang Yuanjie , Xing Xuefeng
Published: December 2023

Neural networks, including conventional neural networks (CNNs) and long- and short-term memory (LSTM) networks, have gained considerable attention in recent years due to their remarkable ability to recognize signals, which make them an ideal option for communication applications in aquatic environments. Despite the promising results achieved through the integration of various algorithms and CNN-LSTM networks, the utilization of these networks necessitates a significant amount of data for training which results in increased complexity of recognition systems. Therefore, it is crucial to explore new solutions that can mitigate these issues while also enhancing the overall performance of underwater acoustic communication systems. Therefore, this paper proposes a VCB framework that integrates the variational modal decomposition (VMD) algorithm, CNN and Bidirectional LSTM (BiLSTM) networks. This framework employs the VMD algorithm to extract energy characteristics from signals that are modulated by filter bank multi-carrier (FBMC) technologies. One-dimensional convolution (Conv1D) is then employed to extract the abstract features, which are subsequently fed into LSTM for time correlation analysis, from the energy values at each time step. Experimental results indicate that the neural network framework utilized in this study significantly diminishes data volume and enhances the recognition rate of FBMC signals, resulting in a lower bit error rate (BER).
Vocal Tract Acoustic Measurements for Detection of Pathological Voice Disorders
Authors: Mishra Jyoti , Sharma R. K.
Published: December 2023

Human voice is an important signal that, if analyzed cautiously, will reveal various disorders and diseases of the human body. Vocal profiling is emerging as a new technique for assessment of voice disorders. This work has focused mainly on vocal tract acoustic measurements to detect voice disorders, especially dysphonia. Dysphonia is a communication disorder that, if not detected at an early stage, may lead to serious complications like laryngeal cancer thus affecting health and quality of life. The voice recordings of 52 subjects (26 dysphonic and 26 healthy) were taken from Saarbrucken Voice Database, 28 live samples (14 dysphonic and 14 healthy) were recorded using CSL 4500 tool and 169 subjects (111 dysphonic and 58 healthy) were taken from VOICED Database. These voice samples were used to extract five acoustic parameters: fundamental frequency, jitter, shimmer, pitch, and formants. The results obtained had an accuracy of around 85% which confirms the potentiality of these fundamental parameters in the successful detection of dysphonia as well as other voice disorders.
Discrete Rössler Oscillators: Maps and Their Ensembles
Authors: Kuznetsov Alexander P. , Sedova Yuliya V. , Stankevich Nataliya V.
Published: December 2023

We study the complex dynamics of a discrete analogue of the classical flow dynamical system — Rössler oscillator. Minimal ensembles of two and three coupled discrete oscillators with different topologies are considered. As the main research tool we used the method of Lyapunov exponents charts. For coupled systems, the possibility of two-, three- and four-frequency quasi-periodicity is revealed. Illustrations in the form of Fourier spectra are presented. Doublings of invariant curves, two- and three-dimensional tori are found. The transition from two-dimensional tori to three-dimensional ones occurs through a quasi-periodic saddle-node bifurcation of invariant tori or through a quasi-periodic Hopf bifurcation. A discrete version of the hyperchaotic Rössler oscillator is also discussed. It exhibits dynamical behavior close to a flow system in some measure.
Dynamic Behaviors in a Discrete Model for Predator–Prey Interactions Involving Hibernating Vertebrates
Authors: Al-Kaff Mohammed O. , El-Metwally Hamdy A. , Elabbasy El-Metwally M. , Elsadany Abd-Elalim A.
Published: December 2023

This paper presents a discrete predator–prey interaction model involving hibernating vertebrates, with detailed analysis and simulation. Hibernation contributes to the survival and reproduction of organisms and species in the ecosystem as a whole. In addition, it also constitutes a wise sharing of time, space, and resources with others. We have created a new predator–prey model by integrating the two species, Holling-III and Holling-I, which have a bifurcation within a specified parameter range. We discovered that this system possesses the stability of fixed points as well as several bifurcation behaviors. To accomplish this, the center manifold theorem and bifurcation theory are applied to create existence conditions for period-doubling bifurcations and Neimark–Sacker bifurcations, which are depicted in the graph as distinct structures. Examples of numerical simulations include bifurcation diagrams, maximum Lyapunov exponents, and phase portraits, which demonstrate not just the validity of theoretical analysis but also complex dynamical behaviors and biological processes. Finally, the Ott–Grebogi–Yorke (OGY) method and phases of chaos control bifurcation were used to control the chaos of predator–prey model in hibernating vertebrates.
n-Bridge braids and the braid index
Authors: Gollero Dane , Krishna Siddhi , Loving Marissa , Neri Viridiana , Tahir Izah , White Len
Published: December 2023

In this work, we find a closed form formula for the braid index of an [Formula: see text]-bridge braid, a class of positive braid knots which simultaneously generalizes torus knots, 1-bridge braids, and twisted torus knots. Our proof is elementary, effective, and self-contained, and partially recovers work of Birman–Kofman. Along the way, we show that the disparate definitions of twisted torus knots in the literature agree.
Study the Optical Properties of Noble Metal (Au, Ag, and Cu) Thin Film Biosensors Based on Surface Plasmon Resonance Performance by a Simulation Program
Authors: Ahmad Omar A. , Alrashid Saeed Naif Turki , Khalaf Mohammed K.
Published: December 2023

In this work, we studied the effect noble metal thicknesses, namely gold (Au), silver (Ag), and copper (Cu) on the surface plasmon resonance (SPR) characteristics for sensitive optical sensor and biosensor applications. SPR spectra for Au, Ag and Cu were widely studied in the Fresnel equation to calculate the total reflection, the wavelength ([Formula: see text] range from 500 nm to 700 nm with the angle of incidence ([Formula: see text] ranging from 0∘ to 90∘ and thickness of the film ([Formula: see text] reaching from 30 nm to 70 nm. Analysis simulation (in MATLAB) has been made for SPR of three metals placed on the N_LASF9 glass prism and the sensing medium is the air. The properties of the angle of resonance [Formula: see text] have been calculated from plotted reflectance via incident angle [Formula: see text]. The results give efficient detection in a change of reflection ([Formula: see text] and full half width maximum FWHM. The minimum reflection was obtained (0.003 a.u.) at wavelength 700 nm and thickness [Formula: see text]= 50 nm for gold Au at [Formula: see text] = 0.0. The best FWHM was recorded at the wavelength (500 nm) at thickness (70 nm) for silver at [Formula: see text] = 0.0, which was equal (0.1 nm).
Constraining Rastall parameter for baryon asymmetry factor using isotropic and anisotropic universe models
Authors: Saleem Rabia , Saleem Aqsa
Published: December 2023

This paper investigates the mystery of baryogenesis for isotropic and anisotropic universe models in the context of Rastall’s theory of gravity (RTG). In the first part, we utilize the Rastall field equations (FEs) for the flat Friedmann Robertson Walker (FRW) metric to find the solutions of density [Formula: see text] and Hubble parameter [Formula: see text]. Additionally, Rastall parameter plays a crucial role to discuss baryogenesis and fulfilling its criteria on the observational ground. It is found that very large positive/negative values of Rastall parameter like [Formula: see text] (for [Formula: see text]) and [Formula: see text] (for [Formula: see text]) are required to satisfy the observational bounds on baryon asymmetry factor (BAF), see Fig. 1. In the second part, we develop the FEs for RTG using the Locally Rotationally Symmetric (LRS) Bianchi type-1 line-element. Then we find the solutions of the FEs and use them to find the BAF using Rastall parameter as [Formula: see text] and anisotropic parameter [Formula: see text] to meet the consistency with the experimental limit, see Fig. 3. For [Formula: see text], the anisotropic model reduces to the isotropic FRW model, where we have a zero BAF. Finally, we conclude that RTG with both universe models is a good candidate to study baryogenesis in the early universe and the variable [Formula: see text] has a considerable impact on the BAF.
Rota–Baxter mock-Lie bialgebras and related structures
Authors: Laraiedh Ismail
Published: December 2023

The aim of this paper is to introduce the notion of Rota–Baxter mock-Lie bialgebras [Formula: see text] and their admissibility conditions in terms of dual representations [Formula: see text]. Next, we show that Rota–Baxter mock-Lie bialgebras are characterized by matched pairs and Manin triples of Rota–Baxter mock-Lie algebras. Furthermore, the coboundary case leads to the introduction of the admissible mock-Lie Yang–Baxter equation in Rota–Baxter mock-Lie algebras, whose skew-symmetric solutions are used to construct Rota–Baxter mock-Lie bialgebras.
A possibility of Klein paradox in quaternionic (3 + 1) frame
Authors: Pathak Geetanjali , Chanyal B. C.
Published: December 2023

In light of the significance of non-commutative quaternionic algebra in modern physics, this study proposes the existence of the Klein paradox in the quaternionic (3+1)-dimensional space-time structure. By introducing quaternionic wave function, we rewrite the Klein–Gordon equation in extended quaternionic form that includes scalar and the vector fields. Because quaternionic fields are non-commutative, the quaternionic Klein–Gordon equation provides three separate sets of the probability density and probability current density of relativistic particles. We explore the significance of these probability densities by determining the reflection and transmission coefficients for the quaternionic relativistic step potential. Furthermore, we also discuss the quaternionic version of the oscillatory, tunnelling, and Klein zones for the quaternionic step potential. The Klein paradox occurs only in the Klein zone when the impacted particle’s kinetic energy is less than [Formula: see text]. Therefore, it is emphasized that for the quaternionic Klein paradox, the quaternionic reflection coefficient becomes exclusively higher than value one while the quaternionic transmission coefficient becomes lower than zero.
Z2[u]Z2[u]-Additive codes
Authors: Shashirekha G. , Vadiraja Bhatta G. R. , Poojary Prasanna
Published: December 2023

In this paper, we study mixed alphabet codes over two alphabets [Formula: see text], and its extension [Formula: see text], [Formula: see text]. We define new Gray maps, using which we study cyclic codes and obtain the properties of their generators. Further, we construct constacyclic codes, and linear codes, and observe the useful properties of their generators. Finally, we present some examples of cyclic codes and binary linear codes.
Some bounds for the Aα-spectral radius of a digraph
Authors: Carmona Juan R. , Ganie Hilal A.
Published: December 2023

Let [Formula: see text] be a digraph of order [Formula: see text]. Let [Formula: see text] and [Formula: see text] be respectively, the adjacency matrix and the diagonal matrix of vertex out-degrees of [Formula: see text] The generalized adjacency matrix [Formula: see text] of [Formula: see text] is defined as [Formula: see text] The spectral radius of the matrix [Formula: see text] is called the [Formula: see text]-spectral radius or the generalized adjacency spectral radius of [Formula: see text]. In this paper, we obtain some sharp upper and lower bounds for the [Formula: see text]-spectral radius, in terms of the number [Formula: see text] of vertices, the vertex out-degrees, the average [Formula: see text]-out-degrees, the average [Formula: see text]-in-degrees of the vertices of [Formula: see text] and the parameter [Formula: see text]. We characterize the extremal digraphs attaining these bounds. We highlight the importance of our results by means of some examples and conclude that the bounds obtained are incomparable.
Approximation algorithm for min sweep-period sweep cover with distance constraint
Authors: Zhang Yidong , Zhang Zhao
Published: December 2023

In a sweep cover problem, points of interest (PoIs) are required to be visited by mobile sensors periodically. The time span between two consecutive visits of a PoI is the sweep-period of that PoI. In this paper, we study the min sweep-period sweep cover problem with a distance constraint (MinSPSC[Formula: see text]). Given [Formula: see text] mobile sensors with speed [Formula: see text], [Formula: see text] base stations and a distance constraint [Formula: see text], the goal of MinSPSC[Formula: see text] is to design routes for the mobile sensors such that the maximum sweep-period is minimized under the condition that every mobile sensor has to visit a base station before traveling distance [Formula: see text]. We present an [Formula: see text]-approximation algorithm for MinSPSC[Formula: see text], where [Formula: see text] is the maximum distance between a PoI and a base station.
Total Wrist Replacement Outcomes in the Asian Pacific Population – A Single-Centre Result
Authors: WAN Raymond Chung-Wai , MAK Michael Chu-Kay , TSE Wing-Lim , CHAU Wai-Wang , HO Pak-Cheong
Published: December 2023

Background: Total wrist replacement (TWR) is rarely done in the Asia–Pacific region. The aim of this study is to report the surgical outcomes and experience of TWR in patients with advanced arthritis.

Methods: This is a retrospective review of all TWR patients in the Department of Orthopaedics and Traumatology, Prince of Wales Hospital, Hong Kong, which is a university tertiary centre, from January 2004 to March 2023. Recorded demographic parameters include gender, age upon surgery, pathology, types of implants and follow-up period. The surgical outcome parameters include range of motion, grip strength, wrist function assessment, radiological and clinical complications and any related secondary operations. Postoperative X-ray and clinical notes were reviewed. All wrist function assessments were performed by specialised occupational therapists according to protocol.

Results: The study included a total of 12 wrists of 10 patients, all Chinese–Asian, with a mean age of 61.4 years at surgery. Larsen grade V arthritis constituted 50% and grade IV 16.7% of the patients, amongst which 33% had volar subluxation. The mean follow-up period was 97.4 months (21–205 months). The mean grip strength was 64.2% of the unaffected side. The mean postoperative Disabilities of Arm, Shoulder and Hand (DASH) score was 41.12% and patient-rated wrist/hand evaluation (PRWE) score 18.0. Complication incidence was 16.67% for loosening, 8.3% for metallosis and 8.3% for infection. One patient required conversion to total wrist arthrodesis due to metallosis. No patient suffered from dislocation, periprosthetic fracture and infection.

Conclusions: TWR is an effective and safe alternative to total wrist arthrodesis with comparable outcomes. Our series outcomes are satisfactory and in line with literature. With meticulous soft tissue release and balancing, volar subluxation can also be corrected and may not be a contraindication.

Level of Evidence: Level IV (Therapeutic)
ChainKV: A Semantics-Aware Key-Value Store for Ethereum System
Authors: Chen Zehao , Li Bingzhe , Cai Xiaojun , Jia Zhiping , Ju Lei , Shao Zili , Shen Zhaoyan
Published: December 2023

The Log-Structure Merged tree (LSM-tree) based key-value (KV) store has been widely adopted as the storage engine for blockchain systems, such as Ethereum, in which blockchain data are uniformly transformed into randomly distributed KV items for persistence. However, blockchain semantics are ignored during this process, making the blockchain storage suffer from heavy read/write amplification problems. Moreover, as the Ethereum network scales up, tremendous data further exacerbates its storage burden. Until now, most studies have focused on sharding, data archiving, decentralized distributed storage, etc., to mitigate the burden of the storage layer. However, the incompatibility between Ethereum semantics and the characteristics of the storage engine is ignored.

In this paper, we present ChainKV, a new semantics-aware storage paradigm to improve the storage management performance for the Ethereum system. Firstly, based on Ethereum blockchain semantics, ChainKV separately stores different types of data in multiple storage zones in the KV store to mitigate the read/write amplification problem. Secondly, following the mechanism of the verification process in the authenticated data structure (ADS), a new ADS data transformer is proposed to exploit the data locality when persisting ADS. Moreover, a new space gaming caching policy is adopted to coordinate the cache space management for two independent storage zones. Finally, we propose an optional lightweight node crash recovery mechanism to eliminate functional redundancy between the Ethereum protocol and the storage engine. The experimental results indicate that ChainKV outperforms the prior Ethereum systems by up to 1.99× and 4.20× for synchronization and query operations, respectively
ALP: Adaptive Lossless floating-Point Compression
Authors: Afroozeh Azim , Kuffo Leonardo X. , Boncz Peter
Published: December 2023

IEEE 754 doubles do not exactly represent most real values, introducing rounding errors in computations and [de]serialization to text. These rounding errors inhibit the use of existing lightweight compression schemes such as Delta and Frame Of Reference (FOR), but recently new schemes were proposed: Gorilla, Chimp128, PseudoDecimals (PDE), Elf and Patas. However, their compression ratios are not better than those of general-purpose compressors such as Zstd; while [de]compression is much slower than Delta and FOR.

We propose and evaluate ALP, that significantly improves these previous schemes in both speed and compression ratio (Figure 1). We created ALP after carefully studying the datasets used to evaluate the previous schemes. To obtain speed, ALP is designed to fit vectorized execution. This turned out to be key for also improving the compression ratio, as we found in-vector commonalities to create compression opportunities. ALP is an adaptive scheme that uses a strongly enhanced version of PseudoDecimals [31] to losslessly encode doubles as integers if they originated as decimals, and otherwise uses vectorized compression of the doubles' front bits. Its high speeds stem from our implementation in scalar code that auto-vectorizes, using building blocks provided by our FastLanes library [6], and an efficient two-stage compression algorithm that first samples row-groups and then vectors.
Efficient Approximation Framework for Attribute Recommendation
Authors: Chen Xingguang , Zhang Fangyuan , Huang Jinchao , Wang Sibo
Published: December 2023

Trend analysis is a fundamental type of analytical query in online analytical processing (OLAP) systems. In trend analysis, a key step is to identify k valuable attributes whose distributions in two subsets under different predicates significantly differ for further investigation, where the difference is measured by metric functions. However, the exact solution that involves scanning all records is prohibitively expensive, particularly when handling large datasets in the era of big data. To minimize unnecessary data access, the existing state-of-the-art solution TopKAttr adopts sampling to avoid the expensive data scan. However, their solution still has two main drawbacks. Firstly, their solution is tailored only for two limited metric functions: the Earth Mover distance and Euclidean distance, and cannot be generalized to more complicated metric functions. Besides, their solution still aims to return the exact top-k answers via the sampling method, which still causes high running costs as shown in our experiment.

Motivated by these limitations, we propose a general approximation framework for attribute recommendation that efficiently returns the top-k attributes with theoretical guarantees while supporting an extensive range of metric functions, such as the Kolmogorov-Smirnov test (KS-test), Chebyshev distance, the Earth Mover distance, Euclidean distance, and with the potential to more metrics. The key to our framework is a new bound estimation strategy that can be applied to a wide spectrum of metrics, as we listed above. Based on our estimation framework, we further devise an efficient approximation algorithm with theoretical guarantees to answer the top-k queries, which is widely used in attribute recommendation. Extensive experiments on four real large datasets show that our framework gains up to an order of magnitude speed-up and consistently high accuracy compared to TopKAttr, providing a promising alternative for attribute recommendation in OLAP systems.
F3KM: Federated, Fair, and Fast k-means
Authors: Zhu Shengkun , Xu Quanqing , Zeng Jinshan , Wang Sheng , Sun Yuan , Yang Zhifeng , Yang Chuanhui , Peng Zhiyong
Published: December 2023

This paper proposes a federated, fair, and fast k-means algorithm (F3KM) to solve the fair clustering problem efficiently in scenarios where data cannot be shared among different parties. The proposed algorithm decomposes the fair k-means problem into multiple subproblems and assigns each subproblem to a client for local computation. Our algorithm allows each client to possess multiple sensitive attributes (or have no sensitive attributes). We propose an in-processing method that employs the alternating direction method of multipliers (ADMM) to solve each subproblem. During the procedure of solving subproblems, only the computation results are exchanged between the server and the clients, without exchanging the raw data. Our theoretical analysis shows that F3KM is efficient in terms of both communication and computation complexities. Specifically, it achieves a better trade-off between utility and communication complexity, and reduces the computation complexity to linear with respect to the dataset size. Our experiments show that F3KM achieves a better trade-off between utility and fairness than other methods. Moreover, F3KM is able to cluster five million points in one hour, highlighting its impressive efficiency.
Generation of Training Examples for Tabular Natural Language Inference
Authors: Bussotti Jean-Flavien , Veltri Enzo , Santoro Donatello , Papotti Paolo
Published: December 2023

Tabular data is becoming increasingly important in Natural Language Processing (NLP) tasks, such as Tabular Natural Language Inference (TNLI). Given a table and a hypothesis expressed in NL text, the goal is to assess if the former structured data supports or refutes the latter. In this work, we focus on the role played by the annotated data in training the inference model. We introduce a system, Tenet, for the automatic augmentation and generation of training examples for TNLI. Given the tables, existing approaches are either based on human annotators, and thus expensive, or on methods that produce simple examples that lack data variety and complex reasoning. Instead, our approach is built around the intuition that SQL queries are the right tool to achieve variety in the generated examples, both in terms of data variety and reasoning complexity. The first is achieved by evidence-queries that identify cell values over tables according to different data patterns. Once the data for the example is identified, semantic-queries describe the different ways such data can be identified with standard SQL clauses. These rich descriptions are then verbalized as text to create the annotated examples for the TNLI task. The same approach is also extended to create counterfactual examples, i.e., examples where the hypothesis is false, with a method based on injecting errors in the original (clean) table. For all steps, we introduce generic generation algorithms that take as input only the tables. For our experimental study, we use three datasets from the TNLI literature and two crafted by us on more complex tables. Tenet generates human-like examples, which lead to the effective training of several inference models with results comparable to those obtained by training the same models with manually-written examples.
MirrorKV: An Efficient Key-Value Store on Hybrid Cloud Storage with Balanced Performance of Compaction and Querying
Authors: Wang Zhiqi , Shao Zili
Published: December 2023

LSM-based key-value stores have been leveraged in many state-of-the-art data-intensive applications as storage engines. As data volume scales up, a cost-efficient approach is to deploy these applications on hybrid cloud storage with hot/cold separation, which splits the LSM-tree into two parts and thus brings new challenges on how to split and how to close the significant performance gap between these two parts. Existing LSM-tree key-value stores mainly focus on the optimizations of local storage, which incurs sub-optimal performance when directly applied to hybrid storage.

In this paper, we present MirrorKV for efficient compaction and querying on hybrid cloud storage. First, based on the capacities of fast and slow cloud storage, MirrorKV vertically separates hot/cold data of different levels stored in different cloud storage with different compaction mechanisms. To avoid compaction in slow storage being the bottleneck of the write path, MirrorKV proposes a novel virtual split to only compact the metadata during the compaction, which postpones the actual compaction until it reaches deep enough levels. Second, to reduce accessing slow storage during querying, MirrorKV horizontally separates keys and values into two mirrored LSM-trees to differentiate caching priorities; the maintained tree structures preserve the data locality for efficient sequential reading without incurring the overhead of the traditional key-value separation solutions. Finally, MirrorKV leverages cached data to guide the compaction where the hot data is retained in the fast storage while the cold data is compacted to deeper levels in slow storage. Compared with RocksDB-cloud, MirrorKV achieves 2.4× higher random insertion throughput, 29% higher random read throughput, and 99% less compaction time.
NOCAP: Near-Optimal Correlation-Aware Partitioning Joins
Authors: Zhu Zichen , Hu Xiao , Athanassoulis Manos
Published: December 2023

Storage-based joins are still commonly used today because the memory budget does not always scale with the data size. One of the many join algorithms developed that has been widely deployed and proven to be efficient is the Hybrid Hash Join (HHJ), which is designed to exploit any available memory to maximize the data that is joined directly in memory. However, HHJ cannot fully exploit detailed knowledge of the join attribute correlation distribution.

In this paper, we show that given a correlation skew in the join attributes, HHJ partitions data in a suboptimal way. To do that, we derive the optimal partitioning using a new cost-based analysis of partitioning-based joins that is tailored for primary key - foreign key (PK-FK) joins, one of the most common join types. This optimal partitioning strategy has a high memory cost, thus, we further derive an approximate algorithm that has tunable memory cost and leads to near-optimal results. Our algorithm, termed NOCAP (Near-Optimal Correlation-Aware Partitioning) join, outperforms the state of the art for skewed correlations by up to 30%, and the textbook Grace Hash Join by up to 4×. Further, for a limited memory budget, NOCAP outperforms HHJ by up to 10%, even for uniform correlation. Overall, NOCAP dominates state-of-the-art algorithms and mimics the best algorithm for a memory budget varying from below √||relation|| to more than ||relation||.
SeeSaw: Interactive Ad-hoc Search Over Image Databases
Authors: Moll Oscar , Favela Manuel , Madden Samuel , Gadepally Vijay , Cafarella Michael
Published: December 2023

As image datasets become ubiquitous, the problem of ad-hoc searches over image data is increasingly important. Many high-level data tasks in machine learning, such as constructing datasets for training and testing object detectors, imply finding ad-hoc objects or scenes within large image datasets as a key sub-problem. New foundational visual-semantic embeddings trained on massive web datasets such as Contrastive Language-Image Pre-Training (CLIP) can help users start searches on their own data, but we find there is a long tail of queries where these models fall short in practice. Seesaw is a system for interactive ad-hoc searches on image datasets that integrates state-of-the-art embeddings like CLIP with user feedback in the form of box annotations to help users quickly locate images of interest in their data even in the long tail of harder queries. One key challenge for Seesaw is that, in practice, many sensible approaches to incorporating feedback into future results, including state-of-the-art active-learning algorithms, can worsen results compared to introducing no feedback, partly due to CLIP's high-average performance. Therefore, Seesaw includes several algorithms that empirically result in larger and also more consistent improvements. We compare Seesaw's accuracy to both using CLIP alone and to a state-of-the-art active-learning baseline and find Seesaw consistently helps improve results for users across four datasets and more than a thousand queries. Seesaw increases Average Precision (AP) on search tasks by an average of .08 on a wide benchmark (from a base of .72), and by a .27 on a subset of more difficult queries where CLIP alone performs poorly.
DProvDB: Differentially Private Query Processing with Multi-Analyst Provenance
Authors: Zhang Shufan , He Xi
Published: December 2023

Recent years have witnessed the adoption of differential privacy (DP) in practical database systems like PINQ, FLEX, and PrivateSQL. Such systems allow data analysts to query sensitive data while providing a rigorous and provable privacy guarantee. However, the existing design of these systems does not distinguish data analysts of different privilege levels or trust levels. This design can have an unfair apportion of the privacy budget among the data analyst if treating them as a single entity, or waste the privacy budget if considering them as non-colluding parties and answering their queries independently. In this paper, we propose DProvDB, a fine-grained privacy provenance framework for the multi-analyst scenario that tracks the privacy loss to each single data analyst. Under this framework, when given a fixed privacy budget, we build algorithms that maximize the number of queries that could be answered accurately and apportion the privacy budget according to the privilege levels of the data analysts.
Rethinking Learned Cost Models: Why Start from Scratch?
Authors: Yang Jiani , Wu Sai , Zhang Dongxiang , Dai Jian , Li Feifei , Chen Gang
Published: December 2023

Recent work has applied learning-based approaches to replace the conventional cost model, but these approaches are expensive to train and result in high inference overheads. Furthermore, due to a lack of explainability, models trained for one database may not be easily transferred to another, requiring a complete re-training process. In this paper, we propose a new approach to tuning the conventional formula-based cost model for DBMS. Our approach involves identifying important parameters within the cost model rules and using a fast-learning model to adjust them for each specific hardware and software configuration of the DBMS deployment. We dynamically partition the search space of hardware and software configurations to gradually refine the cost model estimation. To apply our cost model to a new DBMS instance, we start with a rough estimation and progressively refine it with finer granularity. Our experiments with different hardware and software configurations show that our approach enables the conventional cost model to be quickly transferred to any database instance, achieving comparable results to a fine-tuned learning-based model. Overall, our approach provides a practical solution to tuning the conventional cost model for DBMS, with significant benefits in terms of reduced cost and improved performance.
On the Effectiveness of Sampled Softmax Loss for Item Recommendation
Authors: Wu Jiancan , Wang Xiang , Gao Xingyu , Chen Jiawei , Fu Hongcheng , Qiu Tianyu
Published: December 2023

The learning objective plays a fundamental role to build a recommender system. Most methods routinely adopt either pointwise (e.g., binary cross-entropy) or pairwise (e.g., BPR) loss to train the model parameters, while rarely pay attention to softmax loss, which assumes the probabilities of all classes sum up to 1, due to its computational complexity when scaling up to large datasets or intractability for streaming data where the complete item space is not always available. The sampled softmax (SSM) loss emerges as an efficient substitute for softmax loss. Its special case, InfoNCE loss, has been widely used in self-supervised learning and exhibited remarkable performance for contrastive learning. Nonetheless, limited recommendation work uses the SSM loss as the learning objective. Worse still, none of them explores its properties thoroughly and answers “Does SSM loss suit for item recommendation?” and “What are the conceptual advantages of SSM loss, as compared with the prevalent losses?”, to the best of our knowledge.

In this work, we aim at offering a better understanding of SSM for item recommendation. Specifically, we first theoretically reveal three model-agnostic advantages: (1) mitigating popularity bias, which is beneficial to long-tail recommendation; (2) mining hard negative samples, which offers informative gradients to optimize model parameters; and (3) maximizing the ranking metric, which facilitates top-
K
performance. However, based on our empirical studies, we recognize that the default choice of cosine similarity function in SSM limits its ability in learning the magnitudes of representation vectors. As such, the combinations of SSM with the models that also fall short in adjusting magnitudes (e.g., matrix factorization) may result in poor representations. One step further, we provide mathematical proof that message passing schemes in graph convolution networks can adjust representation magnitude according to node degree, which naturally compensates for the shortcoming of SSM. Extensive experiments on four benchmark datasets justify our analyses, demonstrating the superiority of SSM for item recommendation. Our implementations are available in both TensorFlow

1

and PyTorch.

2
Learning Offset Probability Distribution for Accurate Object Detection
Authors: Qiu Heqian , Li Hongliang , Wu Qingbo , Shi Hengcan , Wang Lanxiao , Meng Fanman , Xu Linfeng
Published: December 2023

Object detection combines object classification and object localization problems. Current object detection methods heavily depend on regression networks to locate objects, which are optimized with various regression loss functions to predict offsets between candidate boxes and objects. However, these regression losses are difficult to assign the appropriate penalties for samples with large offset errors, resulting in suboptimal regression networks and inaccurate object offsets. In this article, we consider object location as offset bin classification problem, and propose a distance-aware offset bin classification network optimized with multiple binary cross entropy losses to learn various offset probability distribution, including single label distribution and distance-aware label distribution. On one hand, it provides gradient contributions for different samples based on the bounded probability instead of previous incalculable offset error. On the other hand, it explores the distance correlations between discrete offset bins to facilitate network learning. Specifically, we discretize the continuous offset into a number of bins, and predict the probability of each offset bin, in which the probability should be higher for the offset bin closer to the target offsets, and vice versa. Furthermore, we propose an expectation-based offset prediction and a hierarchical focusing method to improve the precision of prediction. We conduct extensive experiments to evaluate the effectiveness of our method. In addition, our method can be conveniently and flexibly inserted into existing object detection methods, which consistently achieves a large gain based on popular anchor-based and anchor-free methods on the PASCAL VOC, MS-COCO, KITTI, and CrowdHuman datasets. Code will be released at:
https://github.com/QiuHeqian/DBC
.
WA-Zone: Wear-Aware Zone Management Optimization for LSM-Tree on ZNS SSDs
Authors: Long Linbo , He Shuiyong , Shen Jingcheng , Liu Renping , Tan Zhenhua , Gao Congming , Liu Duo , Zhong Kan , Jiang Yi
Published: December 2023

ZNS SSDs divide the storage space into sequential-write zones, reducing costs of DRAM utilization, garbage collection, and over-provisioning. The sequential-write feature of zones is well-suited for LSM-based databases, where random writes are organized into sequential writes to improve performance. However, the current compaction mechanism of LSM-tree results in widely varying access frequencies (i.e., hotness) of data and thus incurs an extreme imbalance in the distribution of erasure counts across zones. The imbalance significantly limits the lifetime of SSDs. Moreover, the current zone-reset method involves a large number of unnecessary erase operations on unused blocks, further shortening the SSD lifetime.

Considering the access pattern of LSM-tree, this article proposes a wear-aware zone-management technique, termed
WA-Zone
, to effectively balance inter- and intra-zone wear in ZNS SSDs. In WA-Zone, a wear-aware zone allocator is first proposed to dynamically allocate data with different hotness to zones with corresponding lifetimes, enabling an even distribution of the erasure counts across zones. Then, a partial-erase-based zone-reset method is presented to avoid unnecessary erase operations. Furthermore, because the novel zone-reset method might lead to an unbalanced distribution of erasure counts across blocks in a zone, a wear-aware block allocator is proposed. Experimental results based on the
FEMU
emulator demonstrate the proposed WA-Zone enhances the ZNS-SSD lifetime by 5.23×, compared with the baseline scheme.
A Novel Application of Polynomial Solvers in mmWave Analog Radio Beamforming
Authors: Bhayani Snehal , Susarla Praneeth , Bulusu S. S. Krishna Chaitanya , Silven Olli , Juntti Markku , Heikkila Janne
Published: December 2023

Beamforming is a signal processing technique where an array of antenna elements can be steered to transmit and receive radio signals in a specific direction. The usage of millimeter wave (mmWave) frequencies and multiple input multiple output (MIMO) beamforming are considered as the key innovations of 5
th
Generation (5G) and beyond communication systems. The mmWave radio waves enable high capacity and directive communication, but suffer from many challenges such as rapid channel variation, blockage effects, atmospheric attenuations, etc. The technique initially performs beam alignment procedure, followed by data transfer in the aligned directions between the transmitter and the receiver [1]. Traditionally, beam alignment involves periodical and exhaustive beam sweeping at both transmitter and the receiver, which is a slow process causing extra communication overhead with MIMO and massive MIMO radio units. In applications such as beam tracking, angular velocity, beam steering etc. [2], beam alignment procedure is optimized by estimating the beam directions using
first order polynomial approximations.
Recent learning-based SOTA strategies [3] for fast mmWave beam alignment also require exploration over exhaustive beam pairs during the training procedure, causing overhead to learning strategies for higher antenna configurations. Therefore, our goal is to optimize the beam alignment cost functions
e.g.
, data rate, to reduce the beam sweeping overhead by applying polynomial approximations of its partial derivatives which can then be solved as a system of polynomial equations. Specifically, we aim to reduce the beam search space by estimating approximate beam directions using the polynomial solvers. Here, we assume both transmitter (TX) and receiver (RX) to be equipped with uniform linear array (ULA) configuration, each having only one degree of freedom (d.o.f.) with

N
t

and

N
r

antennas, respectively.
Neurodevelopment of Attention, Learning, and Memory Systems in Infancy
Authors: Forest Tess Allegra , Amso Dima
Published: December 2023

Understanding how we come to make sense of our environments requires understanding both how we take in new information and how we flexibly process and store that information in memory for subsequent retrieval. In other words, infant cognitive development research is best served by studies that probe infant attention as well as infant learning and memory development. In this article, we first review what is known about infant attention and what is known about a selection of learning systems available in infancy. Then, we review what is known about the interactions between attention and these systems, focusing on infancy when possible but highlighting relevant child and adult literatures when infant research is yet scarce. Finally, we close by proposing a path forward, which we believe will result in a clearer understanding of the interactions between attention and memory that govern infant learning.