Miracle-dz/UniFAR-FTUs huggingface dataset:
An Unmanned Aerial Vehicles Navigation System on the Basis of Pattern Recognition Applications

In the presented paper, the authors substantiate the possibilities of creating a navigation system based on a pattern recognition algorithm. This system is exclusively based on the computing capabilities and memory of an onboard electronic system of an unmanned aerial vehicle. An analytical review and systematization are made of promising methods and algorithms for the implementation of pattern recognition applications based on alternative unmanned aerial vehicle navigation systems.
Hardware Acceleration of Compression and Encryption in SAP HANA

With the advent of cloud computing, where computational resources are expensive and data movement needs to be secured and minimized, database management systems need to reconsider their architecture to accommodate such requirements. In this paper, we present our analysis, design and evaluation of an FPGA-based hardware accelerator for offloading compression and encryption for SAP HANA, SAP's Software-as-a-Service (SaaS) in-memory database. Firstly, we identify expensive data-transformation operations in the I/O path. Then we present the design details of a system consisting of compression followed by different types of encryption to accommodate different security levels, and identify which combinations maximize performance. We also analyze the performance benefits of offloading decryption to the FPGA followed by decompression on the CPU. The experimental evaluation using SAP HANA traces shows that analytical engines can benefit from FPGA hardware offloading. The results identify a number of important trade-offs (e.g., the system can accommodate low-latency secured transactions to high-performance use cases or offer lower storage cost by also compressing payloads for less critical use cases), and provide valuable information to researchers and practitioners exploring the nascent space of hardware accelerators for database engines.
Probing thermally-induced structural evolution during the synthesis of layered Li-, Na-, or K-containing 3 d transition-metal oxides

(cid:1) In situ high-resolution HT-sXRD niques used to unveil the Li/Na/K-ion insertion induced structural evolu- tion during heating. (cid:1) batteries. A signi ﬁ cant challenge in the ﬁ eld of materials chemistry is understanding the dynamics of the chemical reactions between alkali-free precursors and alkali species during the synthesis of these compounds. In this study, in situ high-resolution synchrotron-based X-ray diffraction was applied to reveal the Li/Na/K-ion insertion-induced structural transformation mechanism during high-temperature solid-state reaction. The in situ diffraction results demonstrate that the chemical reaction pathway strongly depends on the alkali-free precursor type, which is a structural matrix enabling phase transi- tions. Quantitative phase analysis identi ﬁ es for the ﬁ rst time the decomposition of lithium sources as the most critical factor for the formation of metastable intermediates or impurities during the entire process of Li-rich layered Li[Li 0.2 Ni 0.2 Mn 0.6 ]O 2 formation. Since the alkali ions have different ionic radii, Na/K ions tend to be located on prismatic sites in the defective layered structure (Na 2/3- x [Ni 0.25 Mn 0.75 ]O 2 or K 2/3- x [Ni 0.25 Mn 0.75 ]O 2 ) during calcination, whereas the Li ions prefer to be localized on the tetrahedral and/or octahedral sites, forming O-type structures.
Isolation and characterization of microsatellite markers in Garcinia gummi-gutta by next-generation sequencing and cross-species ampliﬁcation

Garcinia gummi-gutta (L.) Roxb. (Clusiaceae) is an endemic, semidomesticated, fruit-yielding tree species distributed in the Western Ghats of India and Sri Lanka. Various bioactive phytochemicals, such as garcinol, benzophenones and xanthones are isolated from G. gummi-gutta and have shown antibacterial, antiviral and antioxidant activities. We sequenced the total genomic DNA using Illumina Hiseq 2000 platform and examined 241,141,804 bp high quality data, assembled into 773,889 contigs. In these contigs, 27,313 simple-sequence repeats (SSRs) were identiﬁed, among which mononucleotide repeats were predominant (44.98%) followed by dinucleotide and trinucleotide repeats. Primers were designed for 9964 microsatellites among which 32 randomly selected SSR primer pairs were standardized for ampliﬁcation. Polymerase chain reaction (PCR) ampliﬁcation of genomic DNA in 30 G. gummi-gutta genotypes revealed polymorphic information content (PIC) across all 32 loci ranging from 0.867 to 0.951, with a mean value of 0.917. The observed and expected heterozygosity ranged from 0.00 to 0.63 and 0.896 to 0.974, respectively. Alleles per locus ranged from 12 to 27. This is the ﬁrst report on the development of genomic SSR markers in G. gummi-gutta using next-generation sequencing technology. The genomic SSR markers developed in this study will be useful in identiﬁcation, mapping, diversity and breeding studies.
Spooky: Granulating LSM-Tree Compactions Correctly

Modern storage engines and key-value stores have come to rely on the log-structured merge-tree (LSM-tree) as their core data structure. LSM-tree operates by gradually merge-sorting data across levels of exponentially increasing capacities in storage. A crucial design dimension of LSM-tree is its compaction granularity. Some designs perform Full Merge , whereby entire levels get compacted at once. Others perform Partial Merge , whereby smaller groups of files with overlapping key ranges are compacted independently. This paper shows that both strategies exhibit serious flaws. With Full Merge, space-amplification is exorbitant. The reason is that while compacting the LSM-tree's largest level, there must be at least twice as much storage space as data to store both the original and new files until the compaction is finished. On the other hand, Partial Merge exhibits excessive write-amplification. The reason is twofold. (1) The files getting compacted typically do not have perfectly overlapping key ranges, and so some non-overlapping data is superfluously rewritten in each compaction. (2) Files with different lifetimes become interspersed within the SSD leading to high SSD garbage-collection overheads. As the data size grows, these problems grow in magnitude. We introduce Spooky, a novel compaction granulation method to address these problems. Spooky partitions data at the largest level into equally sized files, and it partitions data at smaller levels based on the file boundaries at the largest level. This allows merging one group of perfectly overlapping files at a time to limit space-amplification and compaction overheads. At the same time, Spooky writes larger though fewer files simultaneously so that files with different lifetimes do not become as interspersed within the SSD. This cheapens garbage-collection. We show empirically that Spooky achieves >2x lower space-amplification than Full Merge and >2x lower write-amplification than Partial Merge at the same time.
Service Quality and Brand Experience as Factors Affecting Repurchase Intention through Word of Mouth in a Mexican Hotel Chain during the Pandemic Covid-19

Prior empirical research suggests that guests visiting a hotel chain during the Covid-19 pandemic are susceptible to have future behavioral intentions such as repurchase intention provided that they perceived a high service quality from the hotel chain. While service quality is important, also the paper discuss the relevance of brand experience define by (Brakus et al., 2009; Meyer & Schwager, 2007) “as consumers’ internal subjective and behavioral responses induced at different levels of interaction, both direct and indirect, with brand-related stimuli” and repurchase intention through word of mouth. We develop a conceptual model base on prior research (Gómez-Suárez. and Veloso, 2020; Prabowo, Astuti, and Respati, 2020). A quantitative research method was used; 351 useful questionnaires were obtained from three different locations of the Mexican Hotel Chain located in the Pacific Coast during the pandemic. Our results provide confirmation that positive evaluations of the service quality and a favorable brand experience increases behavioral intentions such as repurchase intention through word of mouth.
Electronic structures and surface states of the topological insulator Bi 1 − x Sb x

We investigate the electronic structures of the alloyed Bi1-xSbx compounds based on first-principles calculations including spin-orbit coupling (SOC), and calculate the surface states of semi-infinite systems using maximally localized Wannier function. From the calculated results, we analyze the topological nature of Bi1-xSbx, and found the followings: (1) pure Bi crystal is topologically trivial. (2) Topologically nontrivial phase can be realized by reducing the strength of SOC via Sb doping. (3) The indirect bulk band gap, which is crucial to realize the true bulk insulating phase, can be enhanced by uniaxial pressure along c axis. (4) The calculated surface states can be compared with experimental results, which confirms the topological nature. (5) We predict the spin-resolved Fermi surfaces and showed the vortex structures, which should be examined by future experiments.
PerMA-Bench: Benchmarking Persistent Memory Access

Persistent memory's (PMem) byte-addressability and persistence at DRAM-like speed with SSD-like capacity have the potential to cause a major performance shift in database storage systems. With the availability of Intel Optane DC Persistent Memory, initial benchmarks evaluate the performance of real PMem hardware. However, these results apply to only a single server and it is not yet clear how workloads compare across different PMem servers. In this paper, we propose PerMA-Bench, a configurable benchmark framework that allows users to evaluate the bandwidth, latency, and operations per second for customizable database-related PMem access. Based on PerMA-Bench, we perform an extensive evaluation of PMem performance across four different server configurations, containing both first- and second-generation Optane, with additional parameters such as DIMM power budget and number of DIMMs per server. We validate our results with existing systems and show the impact of low-level design choices. We conduct a price-performance comparison that shows while there are large differences across Optane DIMMs, PMem is generally competitive with DRAM. We discuss our findings and identify eight general and implementation-specific aspects that influence PMem performance and should be considered in future work to improve PMem-aware designs.
Construction of a medical corpus based on information extraction results

: The paper presents a method of automatic construction of a semantically annotated corpus using the results of a rule-based information extraction (IE) application. Construction of the corpus is based on using existing programs for text tokenization and morphological analysis and combining their results with domain re-lated correction rules. We reuse the specialized IE system to obtain a corpus annotated on the semantic level. The texts included within the corpus are Polish free text clinical data. We present the documents — diabetic patients’ discharge records, the structure of the corpus annotation and the methods for obtaining the annotations. Initial evaluations based on the results of manual veriﬁcation of selected data subset are also presented. The corpus, once manually corrected, is designed to be used for developing supervised machine learning models for IE applications.
Big Graphs: Challenges and Opportunities

Big data is typically characterized with 4V's: Volume, Velocity, Variety and Veracity. When it comes to big graphs, these challenges become even more staggering. Each and every of the 4V's raises new questions, from theory to systems and practice. Is it possible to parallelize sequential graph algorithms and guarantee the correctness of the parallelized computations? Given a computational problem, does there exist a parallel algorithm for it that guarantees to reduce parallel runtime when more machines are used? Is there a systematic method for developing incremental algorithms with effectiveness guarantees in response to frequent updates? Is it possible to write queries across relational databases and semistructured graphs in SQL? Can we unify logic rules and machine learning, to improve the quality of graph-structured data, and deduce associations between entities? This paper aims to incite interest and curiosity in these topics. It raises as many questions as it answers.
Clock-Offset Invariant Beamforming in Wireless Acoustic Sensor Networks

Clock synchronization among the nodes of a wireless acoustic sensor network (WASN) is a significant issue that affects the performance of multi-channel noise reduction schemes. Since independent sensors are utilized, each accompanied by its internal clock, clock offsets are inevitable, even if the mismatch in the sampling frequencies is negligible. In this thesis, clock offsets are mathematically modeled and the problem of multi-channel linear filtering for speech enhancement is addressed through signal subspace methods. For this purpose, the generalized eigenvalue decomposition (GEVD) of the cross-power spectral density (CPSD) matrices of the noise and target speech processes is capitalized. Beamformers based on this technique are proved to be invariant to sensor clock offsets when used in a blind manner, exploiting only network measurements. This result is confirmed through experiments in a simulated environment. Clock-Offset Invariant Beamforming in Wireless Acoustic Sensor Networks A Generalized Eigenvalue Decomposition Approach
Maker Education Infusion in Educator Preparation Programs: A 2025 Vision for Technology and Teacher Education

Before the COVID-19 pandemic, some teacher education programs started maker education initiatives to integrate the tools, practices, and mindset of the maker movement into their curricula. These efforts came to a grinding halt at the beginning of the pandemic due to the sudden transition to emergency remote teaching (ERT). Post-ERT, educators have been exploring strategies for students to recover from the interruptions of their learning gains and maker education seems to be on the backburner compared to other priorities. However, maker education is still meaningful post-pandemic because it promotes creativity, collaboration, problem-solving, and resilience using hands-on projects. Envisioning teacher education programs in 2025, all teacher educators will have developed the competencies necessary to apply and implement the practices of maker education within their subject areas. Furthermore, all preservice and in-service teachers should have the literacy, fluency, and toolsets to engage with maker education, as well as develop a maker mindset. In other words, maker education should be infused into every aspect of teacher education. Detailed vision and suggestions for implementation are discussed.
Improved turbulence models based on large eddy simulation of homogeneous, incompressible, turbulent flows

The physical bases of large eddy simulation and subgrid modeling are studied. A subgrid scale similarity model is developed that can account for system rotation. Large eddy simulations of homogeneous shear flows with system rotation were carried out. Apparently contradictory experimental results were explained. The main effect of rotation is to increase the transverse length scales in the rotation direction, and thereby decrease the rates of dissipation. Experimental results are shown to be affected by conditions at the turbulence producing grid, which make the initial states a function of the rotation rate. A two equation model is proposed that accounts for effects of rotation and shows good agreement with experimental results. In addition, a Reynolds stress model is developed that represents the turbulence structure of homogeneous shear flows very well and can account also for the effects of system rotation.
Anti-diabetic therapy inhibits mammary tumor progression Insulin sensitizing therapy attenuates type 2 diabetes-mediated mammary tumor progression Running Title: Anti-diabetic therapy inhibits mammary tumor progression

Running Title: Anti-diabetic therapy inhibits mammary tumor progression Yvonne Fierz, Ruslan Novosyadlyy, Archana Vijayakumar, Shoshana Yakar and Derek LeRoith Division of Endocrinology, Diabetes and Bone Diseases, The Samuel Bronfman Department of Medicine, Mount Sinai School of Medicine, New York, NY, USA Corresponding author: Derek LeRoith, MD, PhD E-mail: derek.leroith@mssm.edu Submitted 31 August 2009 and accepted 13 November 2009. Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals. org
Rewriting the Infinite Chase

Guarded tuple-generating dependencies (GTGDs) are a natural extension of description logics and referential constraints. It has long been known that queries over GTGDs can be answered by a variant of the chase ---a quintessential technique for reasoning with dependencies. However, there has been little work on concrete algorithms and even less on implementation. To address this gap, we revisit Datalog rewriting approaches to query answering, where GTGDs are transformed to a Datalog program that entails the same base facts on each base instance. We show that the rewriting can be seen as containing "shortcut" rules that circumvent certain chase steps, we present several algorithms that compute the rewriting by simulating specific types of chase steps, and we discuss important implementation issues. Finally, we show empirically that our techniques can process complex GTGDs derived from synthetic and real benchmarks and are thus suitable for practical use.
DORIAN in action: Assisted Design of Data Science Pipelines

Existing automated machine learning solutions and intelligent discovery assistants are popular tools that facilitate the end-user with the design of data science (DS) pipelines. However, they yield limited applicability for a wide range of real-world use cases and application domains due to (a) the limited support of DS tasks; (b) a small, static set of available operators; and (c) restriction to evaluation processes with quantifiable loss functions. We demonstrate DORIAN, a human-in-the-loop approach for the assisted design of data science pipelines that supports a large and growing set of DS tasks, operators, and arbitrary user-defined evaluation processes. Based on the user query, i.e., a dataset and a DS task, DORIAN computes a ranked list of candidate pipelines that the end-user can choose from, alter, execute and evaluate. It stores executed pipelines in an experiment database and utilizes similarity-based search to identify relevant previously-run pipelines from the experiment database. DORIAN also takes user interaction into account to improve suggestions over time. We show how users can interact with DORIAN to create and compare DS pipelines on various real-world DS tasks without the need for writing any code.
Operon: An Encrypted Database for Ownership-Preserving Data Management

The past decade has witnessed the rapid development of cloud computing and data-centric applications. While these innovations offer numerous attractive features for data processing, they also bring in new issues about the loss of data ownership. Though some encrypted databases have emerged recently, they can not fully address these concerns for the data owner. In this paper, we propose an ownership-preserving database (OPDB), a new paradigm that characterizes different roles' responsibilities from nowadays applications and preserves data ownership throughout the entire application. We build Operon to follow the OPDB paradigm, which utilizes the trusted execution environment (TEE) and introduces a behavior control list (BCL). Different from access controls that merely handle accessibility permissions, BCL further makes data operation behaviors under control. Besides, we make Operon practical for real-world applications, by extending database capabilities towards flexibility, functionality and ease of use. Operon is the first database framework with which the data owner exclusively controls its data across different roles' subsystems. We have successfully integrated Operon with different TEEs, i.e. , Intel SGX and an FPGA-based implementation, and various database services on Alibaba Cloud, i.e. , PolarDB and RDS PostgreSQL. The evaluation shows that Operon achieves 71% - 97% of the performance of plaintext databases under the TPC-C benchmark while preserving the data ownership.
Patterns of Feature Cooccurrence: The Case of Nasality

It is widely acknowledged that certain feature combinations are more likely to occur than others. For example, the feature of nasality is much more likely to appear on segments that are voiced than on segments that are voiceless (see discussion below). Several properties of such combinatorial restrictions are important, including the following: (i) the motivation or source of such restrictions, (ii) their cross -linguistic variability, (iii) their language -internal strength, (iv) the manners in which they manifest themselves. This paper examines certain aspects of the phonology of nasal segments that bear on these issues.
JENNER: Just-in-time Enrichment in Query Processing

Emerging domains, such as sensor-driven smart spaces and social media analytics, require incoming data to be enriched prior to its use. Enrichment often consists of machine learning (ML) functions that are too expensive/infeasible to execute at ingestion. We develop a strategy entitled Just-in-time ENrichmeNt in quERy Processing (JENNER) to support interactive analytics over data as soon as it arrives for such application context. JENNER exploits the inherent tradeoffs of cost and quality often displayed by the ML functions to progressively improve query answers during query execution. We describe how JENNER works for a large class of SPJ and aggregation queries that form the bulk of data analytics workload. Our experimental results on real datasets (IoT and Tweet) show that JENNER achieves progressive answers performing significantly better than the naive strategies of achieving progressive computation.
Hu-Fu: A Data Federation System for Secure Spatial Queries

The increasing concerns on data security limit the sharing of data distributedly stored at multiple data owners and impede the scale of spatial queries over big urban data. In response, data federation systems have emerged to perform secure queries across multiple data owners leveraging secure multi-party computation. However, existing systems are designed for relational data. They are highly inefficient on spatial queries and limited in usability. In this demonstration, we introduce Hu-Fu, the first data federation system for secure spatial queries with high efficiency and usability. Hu-Fu is designed from the perspectives of the query user and the data owner for high usability and decomposes a spatial query into as many plaintext operators and as few secure operators as possible for high efficiency. We demonstrate the deployment and usage of Hu-Fu via cross-company taxi-calling, a popular smart city application.
Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem

We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch, TQP is able to: (1) integrate with ML tools (e.g., Pandas for data ingestion, Tensorboard for visualization); (2) target different hardware (e.g., CPU, GPU) and software (e.g., browser) backends; and (3) end-to-end accelerate queries containing both relational and ML operators. TQP is generic enough to supports the TPC-H benchmark, and it provides performance that are comparable to, and often better than, that of specialized CPU and GPU query processors.