Miracle-dz/UniFAR-FTUs huggingface dataset:
Unified Data Analytics: State-of-the-art and Open Problems

There is an urgent need for unifying data analytics as more and more application tasks become more complex: Nowadays, it is normal to see tasks performing data preparation, analytical processing, and machine learning operations in a single pipeline. Despite this need, achieving this is still a dreadful process where developers have to get familiar with many data processing platforms and write ad hoc scripts for integrating them. This tutorial is motivated by this need from both academia and industry. We will discuss the importance of unifying data processing as well as the current efforts to achieve it. In particular, we will introduce a classification of the different cases where an application needs or benefits from data analytics unification and discuss the challenges in each case. Along with this classification, we will also present current efforts known up to date that aim at unifying data processing, such as Apache Beam and Apache Wayang, and emphasize their differences. We will conclude with open problems and their challenges.
Efficient Load-Balanced Butterfly Counting on GPU

Butterfly counting is an important and costly operation for large bipartite graphs. GPUs are popular parallel heterogeneous devices and can bring significant performance improvement for data science applications. Unfortunately, no work enables efficient butterfly counting on GPU currently. To fill this gap, we propose a GPU-based butterfly counting, called G-BFC. G-BFC addresses three main technical challenges. First, butterfly counting involves massive serial operations, which leads to severe synchronization overheads and performance degradation. We unlock the serial region and utilize the shared memory on GPU to efficiently handle it. Second, butterfly counting on GPU faces the workload imbalance problem. We develop a novel adaptive strategy to balance the workload among threads for efficiency. Third, butterfly counting in parallel suffers from the traversal of the huge amount of two-hop paths, also called wedges, in bipartite graphs. We develop a novel preprocessing strategy, which can effectively reduce the number of wedges to be traversed. Experiments show that G-BFC brings significant performance benefits. On eleven real datasets, G-BFC achieves 19.8X performance speedup over the state-of-the-art solution.
Probing an ytterbium Bose-Einstein condensate using an ultranarrow optical line : towards artificial gauge fields in optical lattices

In this work I present the development of a new experiment to produce quantum degenerate gases of ytterbium. This project aims at realizing artificial gauge fields with ultracold atoms in optical lattices. Combining intense gauge fields with strong on-site interactions is expected to open a new area for ultracold quantum gases, where for instance the atomic analogs of fractional quantum Hall systems could be realized.First I describe the experimental methods for the production of a Bose-Einstein condensate (BEC) of 174Yb. This implies magneto-optical trapping on the 1S0-3P1 intercombination transition and a transport of the atomic cloud in an optical dipole trap over a distance of 22 cm. Evaporative cooling in a crossed dipole trap results in the production of pure BECs of about 6x10^4 atoms.The planned implementation of artificial gauge fields requires the coherent driving of the 1S0-3P0 clock transition of ytterbium. For this purpose an ultrastable laser system at 578 nm, frequency locked to an ultralow expansion (ULE) cavity, has been realized. A precise determination of the temperature zero-crossing point of the ULE cavity allowed us to limit laser frequency drifts below 100 mHz/s. Spectroscopic measurements of the clock transition on a trapped and free falling BEC are presented, where typical linewidths in the kHz range are observed, limited by interatomic interactions. Finally I present a detailed discussion of the methods to achieve artificial gauge fields in optical lattices and their possible experimental implementation. This includes a scheme to realize a bichromatic state-dependent optical superlattice in a doubly-resonant cavity.
Orchestrating Data Placement and Query Execution in Heterogeneous CPU-GPU DBMS

There has been a growing interest in using GPU to accelerate data analytics due to its massive parallelism and high memory bandwidth. The main constraint of using GPU for data analytics is the limited capacity of GPU memory. Heterogeneous CPU-GPU query execution is a compelling approach to mitigate the limited GPU memory capacity and PCIe bandwidth. However, the design space of heterogeneous CPU-GPU query execution has not been fully explored. We aim to improve state-of-the-art CPU-GPU data analytics engine by optimizing data placement and heterogeneous query execution. First, we introduce a semantic-aware fine-grained caching policy which takes into account various aspects of the workload such as query semantics, data correlation, and query frequency when determining data placement between CPU and GPU. Second, we introduce a heterogeneous query executor which can fully exploit data in both CPU and GPU and coordinate query execution at a fine granularity. We integrate both solutions in Mordred, our novel hybrid CPU-GPU data analytics engine. Evaluation on the Star Schema Benchmark shows that the semantic-aware caching policy can outperform the best traditional caching policy by up to 3x. Compared to existing GPU DBMSs, Mordred can outperform by an order of magnitude.
Package ‘ gmgm ’ May 27 , 2022 Type Package

May 27, 2022 Type Package Title Gaussian Mixture Graphical Model Learning and Inference Version 1.1. 1 Description Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>. Depends R (>= 3.5.0) Imports dplyr (>= 1.0.5), ggplot2 (>= 3.2.1), purrr (>= 0.3.3), rlang (>= 0.4.10), stats (>= 3.5.0), stringr (>= 1.4.0), tidyr (>= 1.0.0), visNetwork (>= 2.0.8) Suggests testthat (>= 2.3.2) License GPL-3 Encoding UTF-8 LazyData true RoxygenNote 7.2.0 NeedsCompilation no Author Jérémy Roos [aut, cre, cph], RATP Group [fnd, cph] Maintainer Jérémy Roos <jeremy.roos@gmail.com> Repository CRAN Date/Publication 2022-05-27 18:40:02 UTC
Model Calculation of n + 6Li Reactions Below 20 MeV

Based on the unified Hauser–Feshbach and exciton model for light nuclei, the calculations of reaction cross sections and the double-differential cross sections for n + 6Li are performed. Since all of the first-particle emissions are from the compound nucleus to the discrete levels, the angular momentum coupling effect in pre-equilibrium mechanism must be taken into account. The fitting of the measured data indicates that the three-body break-up process needs to be involved, and the pre-equilibrium reaction mechanism dominates the reaction processes. In light nucleus reactions the recoil effect must be taken into account.
Effective Community Search over Large Star-Schema Heterogeneous Information Networks

Community search (CS) enables personalized community discovery and has found a wide spectrum of emerging applications such as setting up social events and friend recommendation. While CS has been extensively studied for conventional homogeneous networks, the problem for heterogeneous information networks (HINs) has received attention only recently. However, existing studies suffer from several limitations, e.g., they either require users to specify a meta-path or relational constraints, which pose great challenges to users who are not familiar with HINs. To address these limitations, in this paper, we systematically study the problem of CS over large star-schema HINs without asking users to specify these constraints; that is, given a set Q of query vertices with the same type, find the most-likely community from a star-schema HIN containing Q , in which all the vertices are with the same type and close relationships. To capture the close relationships among vertices of the community, we employ the meta-path-based core model, and maximize the number of shared meta-paths such that each of them results in a cohesive core containing Q. To enable efficient CS, we first develop online algorithms via exploiting the anti-monotonicity property of shared meta-paths. We further boost the efficiency by proposing a novel index and an efficient index-based algorithm with elegant pruning techniques. Extensive experiments on four real large star-schema HINs show that our solutions are effective and efficient for searching communities, and the index-based algorithm is much faster than the online algorithms.
Reply on RC2

The Response to the Reviewers file provides complete documentation of the changes made in response to each comment. The document is designed so that the changes that have been made in response to each comment can be immediately read and understood, independent of the other comments and responses. While this comprehensive commentby-comment explanation requires some duplication of material throughout the document, our intention is that it helps to evaluate precisely how each comment has been addressed.
Turbo-Charging SPJ Query Plans with Learned Physical Join Operator Selections

The optimization of select-project-join (SPJ) queries entails two major challenges: (i) finding a good join order and (ii) selecting the best-fitting physical join operator for each single join within the chosen join order. Previous work mainly focuses on the computation of a good join order, but leaves open to which extent the physical join operator selection accounts for plan quality. Our analysis using different query optimizers indicates that physical join operator selection is crucial and that none of the investigated query optimizers reaches the full potential of optimal operator selections. To unlock this potential, we propose TONIC , a novel cardinality estimation-free extension for generic SPJ query optimizers in this paper. TONIC follows a learning-based approach and revises operator decisions for arbitrary join paths based on learned query feedback. To continuously capture and reuse optimal operator selections, we introduce a lightweight yet powerful Query Execution Plan Synopsis ( QEP-S ). In comparison to related work, TONIC enables transparent planning decisions with consistent performance improvements. Using two real-life benchmarks, we demonstrate that extending existing optimizers with TONIC substantially reduces query response times with a cumulative speedup of up to 2.8x.
Depolarization of astrocytes in the basolateral amygdala restores WFS1 neuronal activity and rescues impaired risk-avoidance behavior in DISC1TM mice

Many mental illnesses are accompanied by abnormal risk-avoidance behavior, yet we have only a limited understanding of the neuronal regulatory mechanisms involved. We previously established an inducible DISC1-N terminal fragment transgenic mouse (DISC1-NTM) model which has exhibited risk-avoidance deficiency. Using this model, we analyzed differentially expressed genes (DEGs) using snRNA-seq and the results indicate impaired neuron-astrocyte interactions. We used optogenetic tools to modulate astrocytes in the basolateral amygdala (BLA) and found that ChR2-expressing astrocytes were able to rescue risk-avoidance impairment in DISC1-NTM mice. Using patch clamp recordings combined with signal-cell qPCR, we found impaired excitability of BLAWFS1 neurons in DISC1-NTM mice and that ChR2-expressing astrocytes can induce action potentials (APs) in WFS1 neurons, which restores WFS1 neuronal activity. WFS1 neurons are necessary for BLA astrocytes to modulate impaired risk-avoidance behavior. These finding provide new insights into mechanisms of astrocyte-neuron interactions and suggest that BLA astrocytes may be a promising target for impaired risk avoidance in mental illness. Highlights ChR2-expressing astrocytes in the BLA rescue impaired risk-avoidance behavior in DISC1-NTM mice. Astrocytes in the BLA modulate different nearby neurons to different degrees. Depolarization of BLA astrocytes restore neuronal activity in WFS1 neurons. Astrocytes in the BLA modulate WFS1 neurons via NMDARs.
Optimization of a process for discoloration of low-grade syrup

A bstract. Color improvement of low-grade industrial syrup, on activated carbon, was optimized using a central composite design and response surface methodology. This study checks the effect of three experimental parameters: mass of adsorbent (activated carbon), contact time adsorbate-adsorbent (syrop - activated carbon), masses ratio of the flours constituting the filter cake (m w /m b ), and their interactions on the response: ICUMSA Color. The exploitation of the established model in the space of the variables allowed defining optimal economic conditions of obtaining clear class syrup according to the International Commission for Uniform Methods of Sugar Analysis (ICUMSA).
Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow

Pooling and sharing data increases and distributes its value. But since data cannot be revoked once shared, scenarios that require controlled release of data for regulatory, privacy, and legal reasons default to not sharing. Because selectively controlling what data to release is difficult, the few data-sharing consortia that exist are often built around data-sharing agreements resulting from long and tedious one-off negotiations. We introduce Data Station, a data escrow designed to enable the formation of data-sharing consortia. Data owners share data with the escrow knowing it will not be released without their consent. Data users delegate their computation to the escrow. The data escrow relies on delegated computation to execute queries without releasing the data first. Data Station leverages hardware enclaves to generate trust among participants, and exploits the centralization of data and computation to generate an audit log. We evaluate Data Station on machine learning and data-sharing applications while running on an untrusted intermediary. In addition to important qualitative advantages, we show that Data Station: i) outperforms federated learning baselines in accuracy and runtime for the machine learning application; ii) is orders of magnitude faster than alternative secure data-sharing frameworks; and iii) introduces small overhead on the critical path.
Essence—An LR Parser Generator for Scheme

Essence is a generator for LR(k) and SLR(k) parsers in Scheme. The generated parsers perform error recovery, and are highly eﬃcient. Testing and debugging a parser does not require an edit—generate—compile—test cycle. Rather, the parser generator results from a general parser which takes the input grammar as a parameter; no generation and recompilation is necessary to try out changes to a grammar. The generated parsers result from the general parser by an automatic program transformation called partial evaluation [9, 10]. This guarantees consistency and ensures correctness. However, no speciﬁc knowledge of partial evaluation is required to use Essence.
SQLite: Past, Present, and Future

In the two decades following its initial release, SQLite has become the most widely deployed database engine in existence. Today, SQLite is found in nearly every smartphone, computer, web browser, television, and automobile. Several factors are likely responsible for its ubiquity, including its in-process design, standalone codebase, extensive test suite, and cross-platform file format. While it supports complex analytical queries, SQLite is primarily designed for fast online transaction processing (OLTP), employing row-oriented execution and a B-tree storage format. However, fueled by the rise of edge computing and data science, there is a growing need for efficient in-process online analytical processing (OLAP). DuckDB, a database engine nicknamed “the SQLite for analytics”, has recently emerged to meet this demand. While DuckDB has shown strong performance on OLAP benchmarks, it is unclear how SQLite compares. Furthermore, we are aware of no work that attempts to identify root causes for SQLite’s performance behavior on OLAP workloads. In this paper, we discuss SQLite in the context of this changing workload landscape. We describe how SQLite evolved from its humble beginnings to the full-featured database engine it is today. We evaluate the performance of modern SQLite on three benchmarks, each representing a different flavor of in-process data management, including transactional, analytical, and blob processing. We delve into analytical data processing on SQLite, identifying key bottlenecks and weighing potential solutions. As a result of our optimizations, SQLite is now up to 4.2X faster on SSB. Finally, we discuss the future of SQLite, envisioning
Kelpie: an Explainability Framework for Embedding-based Link Prediction Models

The latest generations of Link Prediction (LP) models rely on embeddings to tackle incompleteness in Knowledge Graphs, achieving great performance at the cost of interpretability. Their opaqueness limits the trust that users can place in them, hindering their adoption in real-world applications. We have recently introduced Kelpie, an explainability framework tailored specifically for embedding-based LP models. Kelpie can be applied to any embedding-based LP model, and supports two explanation scenarios that we have called necessary and sufficient. In this demonstration we showcase Kelpie's capability to explain the predictions of models based on vastly different architectures on the 5 major datasets in literature.
Polyglot Data Management: State of the Art & Open Challenges

Due to the increasing variety of the current database landscape, polyglot data management has become a hot research topic in recent years. The underlying idea is to combine the benefits of different data stores behind a predefined set of common interfaces and thus address use cases that individual stores cannot meet. This can be accomplished using different approaches which vary greatly in terms of capabilities, functionality, and architectural concepts. This tutorial provides a detailed overview of the current state of research in polyglot data management. We motivate its use by showing the high diversity of existing data stores and discussing three use cases in which individual stores are insufficient. Thereafter, we present different taxonomies for classifying polyglot data systems and give a detailed review of a number of selected systems. Finally, we compare these systems based on their features and discuss open challenges that still need to be addressed in future research.
WebMILE: Democratizing Network Representation Learning at Scale

In recent years, we have seen the success of network representation learning (NRL) methods in diverse domains ranging from computational chemistry to drug discovery and from social network analysis to bioinformatics algorithms. However, each such NRL method is typically prototyped in a programming environment familiar to the developer. Moreover, such methods rarely scale out to large-scale networks or graphs. Such restrictions are problematic to domain scientists or end-users who want to scale a particular NRL method-of-interest on large graphs from their specific domain. In this work, we present a novel system, WebMILE to democratize this process. WebMILE can scale an unsupervised network embedding method written in the user's preferred programming language on large graphs. It provides an easy-to-use Graphical User Interface (GUI) for the end-user. The user provides the necessary input (embedding method file, graph, required packages information) through a simple GUI, and WebMILE executes the input network embedding method on the given input graph. WebMILE leverages a pioneering multi-level method, MILE (alternatively DistMILE if the user has access to a cluster), that can scale a network embedding method on large graphs. The language agnosticity is achieved through a simple Docker interface. In this demonstration, we will showcase how a domain scientist or end-user can utilize WebMILE to rapidly prototype and learn node embeddings of a large graph in a flexible and efficient manner - ensuring the twin goals of high productivity and high performance.
CloudJump: Optimizing Cloud Databases for Cloud Storages

There has been an increasing interest in building cloud-native databases that decouple computation and storage for elasticity. A cloud-native database often adopts a cloud storage underneath its storage engine, leveraging another layer of virtualization and providing a high-performance and elastic storage service without exposing complex storage details. It helps reduce the maintenance cost and expedite development cycles for the database kernels. We have observed that there are significant differences between the local and the cloud storage that invalid many designs inside existing databases when they are ported to the cloud storage. In this paper, we analyze the challenges and opportunities of both B-tree and LSM-tree-based storage engines when they are deployed on a cloud storage. We propose an optimization framework that guides database developers to transform on-premise databases into their cloud-native counterparts. We use a B+-tree-based InnoDB as a demonstration vehicle where we have implemented a suite of optimizations using the proposed framework and extend such efforts to the LSM-tree-based RocksDB. On both engines, our evaluations show significant performance improvements on the cloud storage.
SQUALEN BULLETIN Anticancer Potential of Three Sea Cucumber Species Extracts on Human Breast Cancer Cell Line

Sea cucumber has long been utilized as a treatment for a variety of ailments, including antibacterial, antifungal, immunomodulatory, and wound healing. As for the first time, the extracts of three sea cucumber species’ i.e, Actinopyga lecanora, Holothuria atra, and Stichopus vastus, were compared and tested on the cytotoxicity of cancer cells using MTT and Annexin V/Propidium Iodide assays. This study investigates the protection of sea cucumber extracts against the breast cancer cell line (T-47D). All three extracts were found to be actively declining the cancer cell progression, with the IC 50 values of 6.25±0.50 µg/ml ( A. lecanora ), 7.5 ± 1.39 µg/ml ( H. atra ) and 3.25 ± 0.53 µg/ml ( S. vastus ). LC-MS/ MS analysis was used to identify chemical compunds in the extracts. The 1,1-diphenyl-2-picrylhydrazyl (DPPH), total phenolic and flavonoid contents, and anti-collagenase activity were also assessed in all the three extracts. The results demonstrated the absence of antioxidant and flavonoid chemicals in S. vastus, A. leconara and H. atra extracts. However, H. atra contained phenolic compounds (0.4794 mgGAE/g DW). Furthermore, all tested extracts showed significant anti-collagenase activity, which supported the reduction of cancer cell proliferation. However, more research into the mechanism of action of the extract is needed before sea cucumbers therapeutic characteristics may be used to combat breast cancer.