bluuebunny/crossref_metadata_2025 huggingface dataset:
Vil-Cre specific Schlafen 3 knockout mice exhibit sex-specific differences in intestinal differentiation markers and Schlafen family members expression levels
Authors: Vomhof-DeKrey Emilie E. , Stover Allie D. , Labuhn Mary , Osman Marcus R. , Basson Marc D.
Published: October 2021

The intestinal epithelium requires self-renewal and differentiation in order to function and adapt to pathological diseases such as inflammatory bowel disease, short gut syndrome, and ulcers. The rodent Slfn3 protein and the human Slfn12 analog are known to regulate intestinal epithelial differentiation. Previous work utilizing a pan-Slfn3 knockout (KO) mouse model revealed sex-dependent gene expression disturbances in intestinal differentiation markers, metabolic pathways, Slfn family member mRNA expression, adaptive immune cell proliferation/functioning genes, and phenotypically less weight gain and sex-dependent changes in villus length and crypt depth. We have now created a Vil-Cre specific Slfn3KO (VC-Slfn3KO) mouse to further evaluate its role in intestinal differentiation. There were increases in Slfn1, Slfn2, Slfn4, and Slfn8 and decreases in Slfn5 and Slfn9 mRNA expression that were intestinal region and sex-specific. Differentiation markers, sucrase isomaltase (SI), villin 1, and dipeptidyl peptidase 4 and glucose transporters, glucose transporter 1 (Glut1), Glut2, and sodium glucose transporter 1 (SGLT1), were increased in expression in VC-Slfn3KO mice based on intestinal region and were also highly female sex-biased, except for SI in the ileum was also increased for male VC-Slfn3KO mice and SGLT1 was decreased for both sexes. Overall, the variations that we observed in these VC-Slfn3KO mice indicate a complex regulation of intestinal gene expression that is sex-dependent.
Proteomic identification of novel plasma biomarkers associated with spontaneous preterm birth in women with preterm labor without infection/inflammation
Authors: Lee Ji Eun , Park Kyo Hoon , Kim Hyeon Ji , Kim Yu Mi , Choi Ji-Woong , Shin Sue , Lee Kyong-No
Published: October 2021

Objective

We sought to identify plasma biomarkers associated with spontaneous preterm birth (SPTB, delivery within 21 days of sampling) in women with preterm labor (PTL) without intra-amniotic infection/inflammation (IAI) using label-free quantitative proteomic analysis, as well as to elucidate specific protein pathways involved in these cases.

Methods

This was a retrospective cohort study comprising 104 singleton pregnant women with PTL (24–32 weeks) who underwent amniocentesis and demonstrated no evidence of IAI. Analysis of pooled plasma samples collected from SPTB cases and term birth (TB) controls (n = 10 for each group) was performed using label-free quantitative mass spectrometry for proteome profiling in a nested case-control study design. Eight candidate proteins of interest were validated by ELISA-based assay and a clot-based assay in the total cohort.

Results

Ninety-one proteins were differentially expressed (P < 0.05) in plasma samples obtained from SPTB cases, of which 53 (58.2%) were upregulated and 38 (41.8%) were downregulated when compared to TD controls. A validation study confirmed that plasma from women who delivered spontaneously within 21 days of sampling contained significantly higher levels of coagulation factor Ⅴ and lower levels of S100 calcium binding protein A9 (S100A9), especially the former which was independent of baseline variables. The top-ranked pathways related to the 91 differentially expressed proteins were liver-X-receptor/retinoid X receptor (RXR) activation, acute phase response signaling, farnesoid X receptor/RXR activation, coagulation system, and complement system.

Conclusions

Proteomic analyses in this study identified potential novel biomarkers (i.e., coagulation factor V and S100A9) and potential protein pathways in plasma associated with SPTB in the absence of IAI in women with PTL. The present findings provide novel insights into the molecular pathogenesis and therapeutic targets specific for idiopathic SPTB.
Perceived e-learning stress as an independent predictor of e-learning readiness: Results from a nationwide survey in Bangladesh
Authors: Kabir Humayun , Nasrullah Sarker Mohammad , Hasan Md. Kamrul , Ahmed Shakil , Hawlader Mohammad Delwer Hossain , Mitra Dipak Kumar
Published: October 2021

Background

E-learning is a relatively trending system of education that has been placed over conventional campus-based learning worldwide, especially since the emergence of the COVID-19 pandemic. This study aimed to assess e-learning readiness among university students of a developing country like Bangladesh and identify the independent predictors of e-learning readiness.

Methods

From 26 December 2020 to 11 January 2021, a total of 1162 university students who had enrolled for e-learning completed a semi-structured questionnaire. Data were collected online via “Google Form” following the principles of snowball sampling through available social media platforms in Bangladesh. A multivariable linear regression model was fitted to investigate the association of e-learning readiness with perceived e-learning stress and other independent predictor variables.

Results

A total of 1162 university students participated in this study. The results indicated that with the increase of students’ perceived e-learning stress score, the average e-learning readiness score was significantly decreased (β = -0.43, 95% CI: -0.66, -0.20). The students did not seem ready, and none of the e-learning readiness scale items reached the highest mean score (5.0). The age, gender, divisional residence, preference of students and their parents, devices used, and having any eye problems were significantly associated with the students’ e-learning readiness.

Conclusion

During the prolonged period of the COVID-19 pandemic, e-learning implication strategies are needed to be assessed systematically with the level of readiness and its’ impacts among students for the continuation of sound e-learning systems. The study findings recommend evaluating the e-learning readiness of university students and the mental health outcomes during the COVID-19 catastrophe in Bangladesh.
Personalized digital extension services and agricultural performance: Evidence from smallholder farmers in India
Authors: Rajkhowa Pallavi , Qaim Matin
Published: October 2021

Productivity growth in smallholder agriculture is an important driver of rural economic development and poverty reduction. However, smallholder farmers often have limited access to information, which can be a serious constraint for increasing productivity. One potential mechanism to reduce information constraints is the public agricultural extension service, but its effectiveness has often been low in the past. Digital technologies could enhance the effectiveness of extension by reducing outreach costs and helping to better tailor the information to farmers’ individual needs and conditions. Using primary data from India, this study analyses the association between digital extension services and smallholder agricultural performance. The digital extension services that some of the farmers use provide personalized information on the types of crops to grow, the types and quantities of inputs to use, and other methods of cultivation. Problems of selection bias in the impact evaluation are reduced through propensity score matching (PSM) combined with estimates of farmers’ willingness to pay for digital extension. Results show that use of personalized digital extension services is positively and significantly associated with input intensity, production diversity, crop productivity, and crop income.
Fahren und Kontrollieren
Authors: Distelmeyer Jan
Published: October 2021

Jan Distelmeyer widmet sich in diesem Beitrag der Frage, was für eine Art von Autonomie beim autonomen Fahren angestrebt und realisiert wird. Am Beispiel der Marke Tesla zeigt er, dass Vorstellungen einer Selbständigkeit und Unabhängigkeit von menschlicher Aktivität in die Irre führen. Stattdessen ist die Beziehung zwischen Mensch und Technik hier durch eine besondere, zunächst verdoppelte und letztlich zirkuläre Form von Kontrolle und Evaluation geprägt: durch eine (Wunsch-)Konstellation spezieller Feedback-Schleifen, die Kontrolle durch menschliche Akteure und Kontrolle durch computerisierte Technik anhaltend verknüpft. Dieses Verhältnis lässt sich als programmatischer Kreislauf von Kontrolle beschreiben. Er wird durch das Ineinandergreifen diverser Ebenen von Interfaces möglich, von denen das zentrale Dashboard-Display des Wagens nur die offensichtlichste und - als Touchscreen - betont brauchbare Interface-Schicht darstellt.
Vom Fahrzeug zum Fahrding
Authors: Alpsancar Suzana
Published: October 2021

Suzana Alpsancar analysiert in diesem Beitrag die Vision des vollautomatisiertem Fahrens aus der phänomenologischen Perspektive Heideggers im Kontrast zur postphänomenologischen Perspektive Ihdes. Sie kann zeigen, dass beide grundverschiedenen Positionen entwickelt haben und Heideggers zentrale Unterscheidung des Umgangs mit Zeugs und des Handhaben von Dingen mehr zu bieten hat, als Ihde ihm zutraut: mit ihr lässt sich die Idee einer vollautomatisierten Fahrmaschine als Verwandlung des Fahrzeugs in ein bloßes Fahrding verstehen.
Zur Wissenspolitik von Smart-Grid-Experimenten
Authors: Rohracher Harald , Envall Fredrik
Published: October 2021

Harald Rohracher und Fredrik Envall widmen sich in diesem Beitrag der Frage, auf welche Weise unterschiedlichen Konstellationen von Smart City Experimenten auch für unterschiedliche Wissenspolitiken stehen, d.h. für unterschiedlichen Formen von Wissen, die produziert werden, für unterschiedliche Wissenszirkulation im Gefolge der Pilotprojekte und schließlich für unterschiedliche Akteure, die sich dieses Wissen in öffentlich geförderten Experimenten anzueignen vermögen. Dies ist nicht zuletzt deshalb von Bedeutung, weil Experimente, Pilot- oder Demonstrationsprojekte in den letzten Jahren zunehmend zu einem zentralen Instrument der Innovationspolitik geworden sind, vor allem auch hinsichtlich umweltpolitischer Zielsetzungen einer Transformation und Dekarbonisieriung von Infrastruktursystemen wie dem Energiesystem.
Digitalisierung und Verkehrswende
Authors: Mack Birgit , Tampe-Mai Karolin , Wilkes Gabriel , Kagerbauer Martin , Diesch Eugen
Published: October 2021

On-Demand Ridesharing-Shuttles kombinieren individuelle Fahrten zu global optimierten Routen mit flexiblen Zu- und Ausstiegsorten. Sie könnten dazu beitragen, Verkehrsstaus und CO2-Emissionen zu reduzieren. Der Beitrag geht der Frage nach, wie ein Shuttlesystem und sein Umfeld gestaltet sein sollten, so dass Pendler ermutigt werden, Shuttles anstelle des eigenen Autos zu nutzen, ohne gleichzeitig die Abkehr von öffentlichen Verkehrsmitteln zu fördern. In einer Online-Befragung untersuchten wir die Determinanten der Verkehrsmittelwahl von Pendlern in der Region Stuttgart (n=1000). Im Rahmen der Befragung wurde ein Choice-Based Conjoint Experiment durchgeführt. Die Studienteilnehmer konnten zwischen der Nutzung des eigenen Autos, des öffentlichen Nahverkehrs und eines Shuttleservices auf Elektrobusbasis wählen. Als Prädiktoren der Wahl wurden die Attribute Reisekosten, Reisezeit, Fußweg, Auslastung der Shuttles und öffentlichen Verkehrsmittel, soziale Normen und Parkgebühren variiert. Diese Attribute hatten signifikante Auswirkungen auf die Wahl des Verkehrsmittels. Für Werte, die einer realistischen Ausgangssituation für ein Shuttlesystem entsprechen - höhere Kosten und längere Fußwege bei der Nutzung von Shuttles gegenüber dem eigenen Pkw, gleiche Reisezeit für alle Verkehrsmittel, Einführung von innerstädtischen Parkgebühren für Pkw - entschieden sich 11 % für den Shuttle. Bei optimierten Bedingungen scheint die Einführung von Shuttle-Diensten also ein gangbarer Weg zu einem nachhaltigeren Verkehrssystem zu sein.
Öffentliche Kommunikation in der digitalisierten Gesellschaft
Authors: Schrape Jan-Felix
Published: October 2021

Die Plattformisierung der gesellschaftlichen Kommunikationsstrukturen geht mit einer Diversifizierung der Mediennutzung und einer Erosion medientechnischer Grenzziehungen zwischen den vielfältigen Sphären des öffentlichen Austauschs einher. Gleichwohl ist es inzwischen evident, dass die digitale Transformation nicht zu einem allgemeinen Relevanzverlust journalistischer Leistungen oder massenhaft rezipierter Inhalte an sich führt und Selektionsschwellen in der öffentlichen Kommunikation bestehen bleiben. Vor diesem Hintergrund argumentiert dieser Beitrag, dass es nach wie vor analytisch instruktiv erscheint, die Aushandlung öffentlicher Sichtbarkeit als einen Mehrebenenprozess zu beschreiben, der nun allerdings erheblich durch die Eigenheiten digitaler Plattformen mitgeprägt wird. Zunächst nimmt das Papier die zunehmende Plattformorientierung in der Mediendiffusion in den Blick. Anschließend werden die damit verknüpfte Diversifizierung individueller Medienrepertoires und die Pluralisierung des öffentlichen Austauschs diskutiert. Danach werden drei grundlegende Ebenen öffentlicher Kommunikation herausgearbeitet, die durch ein heterogenes Zusammenspiel sozialer und technischer Strukturierungsleistungen gekennzeichnet sind.
Der offene Anus im toten Winkel des ärztlichen Blicks?
Authors: Reichenpfader Julia
Published: October 2021

Julia Reichenpfader geht in ihrem Beitrag am Beispiel des Romans Feuchtgebiete von Charlotte Roche der Frage nach, inwiefern soziokulturell normalisierte Blicke auf den weiblichenø Körper in einer hegemonial strukturierten Institution subvertiert werden können, wobei die politische Signifikanz tabuisierter Körperöffnungen veranschaulicht wird. Der Artikel zeigt, dass sich die Protagonistin Helen konträr zur idealen Patientin verhält, indem sie den ärztlichen Blick zur Reziprozität auffordert, ihren Körper öffnet und die hierarchische Ordnung gefährdet. Der Fokus auf den wechselweise be- und entgrenzten tabuisierten weiblichenø Anus verdeutlicht das Ringen zwischen der konstruierten weiblichenø Pflicht zur Körperschließung und dem Wunsch nach Selbstermächtigung. Dieses Spannungsfeld gipfelt im operativen Verschluss, der die Ordnung und Objektifizierung der Patientin festigt, sodass Helen ihren Kampf um Kontrolle und den Status als mündiges Subjekt verliert.
Körperlyrik
Authors: Rouget Timo
Published: October 2021

Timo Rouget widmet sich an einem exemplarischen Gedicht der Gegenwartslyrik, Christiane Heidrichs »Mein Körper hält still«, typischen Themen und Formen der Gegenwartslyrik. Hierzu zählen Entgrenzung, Poetologie und die Leib-Körper-Differenz.
Ökonomien der Blindheit
Authors: Kunz Tanja Angela
Published: October 2021

Der vorliegende Beitrag untersucht im Kontext der anatomischen Literatur der 1980er und 90er Jahre vergleichend die Funktion von Körpertranszendierungen in Georges Batailles Die Geschichte des Auges und Marcel Beyer Das Menschenfleisch anhand der Frage nach der veränderten Rolle von Exzessen und Obszönitäten angesichts einer ,entheiligten', d.h. grenzenlos (Michel Foucault) gewordenen säkularisierten Gesellschaft jenseits der Orgie (Jean Baudrillard). Die Analyse konzentriert sich auf die Darstellungen der Sinne, insbesondere von Sehsinn und Blindheit, beim Versuch der Entzifferung von Welt. Beyers Erstlingsroman erscheint dabei als Imagination einer Welt ohne uns, womit er im Kontext postmodernen Denkens steht. In Einverleibungsszenarien äußert sich das bis in den eigenen Tod verfolgte obsessive Begehren nach einer Lesbarkeit der dem Menschen körperlich und geistig ex- und internen Zeichensysteme. Der dabei explizierte Sprachskeptizismus fußt auf einem Scheitern am Vorstellungs-, Interpretations- und Kohärenzbildungszwang, der Anleihen bei Friedrich Nietzsches Metaphernkritik macht.
Repeated Pleasure: Reading the Threesome Ménage Romance as Digital Literature
Authors: Valovirta Elina
Published: October 2021

In this chapter, Elina Valovirta discusses a type of romantic love explored by recent New Adult literature; the polyamorous romance. Titles, such as Two Close for Comfort (2015) and Two Billionaires for Christmas (2017) are examples of the 'reverse harem' or 'MFM menage' e-romance bracket, which capitalizes on the erotic and exotic obstacle of two male friends falling in love with - and, ultimately, sharing - the same woman. The polyamorous romance's particular way of eliciting pleasure in readers is tied with specific stylistic strategies, such as alternating first-person narrators and using the plural form in dialogue. The chapter interrogates, how these elements are repeated throughout the genre to titillate and arouse readers.
Refiguring Reader-Response: Experience and Interpretation in J.G. Ballard's Crash
Authors: Kavanagh Ciarán
Published: October 2021

In »Refiguring Reader-Response: Experience and Interpretation in J.G. Ballard's Crash«, Ciarán Kavanagh seeks to establish a methodological basis for reader-response analysis and to give substance to theory through its deployment in his study of Ballard's novel, a text which combines and subverts multiple frameworks. His chapter thus focusses on the microcosmic, line-by-line reading experience provided by two excerpts that exemplify Ballard's clinical over-description of damaged and refigured bodies, as well as on macrocosmic interpretive frameworks relating to genre, embodiment, and aesthetics
Performing Rhythm Through Enunciation: Prose Versus Poetry
Authors: Boisseau Maryvonne
Published: October 2021

In this article, Maryvonne Boisseau, argues that rhythms set the conditions of the performance of a text. Two texts (prose and poetry) describing a lighthouse are compared. The notions of rhythm, enunciation and prosodic condensation are introduced. The first hypothesis postulates that the rhythms contributing to the significance of either text are inscribed in a similar geopoetic background; the second stipulates that the lighthouse's significance is construed by the texts' enunciation; the last assumes that, when the texts are performed, the traditional distinction between prose and poetry is blurred by the voice that carries the words to an audience.
Health tracker: data acquisition and analysis for monitoring health trends and assessing disease risk
Authors: Alzaleq Du'a , Alkhushayni Suboh , FitzGerald Austin
Published: October 2021

This paper describes HealthTracker, a mobile health application to record, store, display, and analyze personal health data. This application allows an individual to log several types of data encompassing their personal health. HealthTracker serves as a model for both a recording and a recommending system. Its goal is to serve as a bridge for future personal health systems to build from. A personâ€™s health information is displayed in an easy-to-understand manner but is also practical for medical professionals. Users should find the system useful and effective no matter if they use it simply or extensively. Currently, the system serves as a prototype for determining the practical applications for smart health systems running on mobile platforms.Â Â
Importance of spindle speed in ring frame
Authors: Redwanul Islam Md. , Chakrabortty Amit , Ghosh Joyjit , Iqbal Tareq , Tanvir Hossen Md.
Published: October 2021

In textile industries, spinning sector plays a vital role because this sector produces the main raw material for the textile industries which is known as yarn. So for getting good qualities garments, it is needed to ensure the good qualities yarn. There are many things which are re-sponsible for producing good qualities yarn. Among these spindle speed is an important thing. One must have to change the spindle speed, when he changes the count. As every count has a particular spindle speed in which the production shows maximum production efficiency. Here we used five most commonly used yarn counts in the maximum factories. The counts are 15 Ne, 20 Ne, 25 Ne, 30 Ne and 40 Ne. We analyzed these counts with different speed and found that every count showed maximum efficiency at a particular spindle speed. The out-come of this paper is applicable in all types of ring frame machine , especially for producing single carded and combed yarn.Â Â
Optimal power flow-path determination for voltage control in electricity distribution using the modified dijkstraâ€™s algorithm
Authors: Ajah Ofem Ofem , Adah Agana Moses , Owai E. Ejogobe
Published: October 2021

This paper examines the electric power distribution network system of the Port Harcourt Electricity Distribution Company (PHEDC); its shortcomings, costs and voltage loss in distribution with a view to finding optimal solution through determination of optimal power flow path. The Modified Dijsktraâ€™s Algorithm was applied to generate optimal flow path model of the distribution network with seven (7) nodes from Afam Thermal Power Station (source) to the Calabar Distribution Centre (destination) via the interconnected substations. The structural design of the PHEDC distribution network and a review of relevant literatures on shortest path problems were adopted. The modified Dijkstraâ€™s algorithm was simulated using JavaScript and is able to run on any web browser (Google Chrome, Mozilla Firefox, etc). It was applied to a practical 330kV network using the relevant data obtained from the company and the result shows the negative effect of distance on voltage quality. It was observed that the Modified Dijkstraâ€™s Algorithm is suitable for determining optimal power flow path with up to 98 percent level of accuracy because of its suitability for determining the shortest route in both transportation and power energy distribution as well as its overall performance with minimal memory space and fast response time.Â Â
Renewed talent management: more productive development teams with digitalization supported HR tools
Authors: Vatousios Antonis , Happonen Ari
Published: October 2021

In most companies and organizations, performance is related to talent management and skills to analyze what and why people are working on. However, many companies do fail to implement a long-term strategy for the performance enhancement activities, considering the talents they have recruited. In this article, we propose a tool for HR work, in context of talent management and how to utilize people skills and productivity analytics to improve team performance and related KPIs. A project data-based case study is illustrated, in which a set of devel-oper and content marketers were analyzed as core team members. In practice, the presented framework makes an important contribution to decision-making activities, where people analytics and proper software tools are used to build new novel knowledge into talent pool of the team. With the framework-based analysis, it is possible to analytically compare team membersâ€™ performance and enhance the teamâ€™s skill and structural development which means that we can employ analytics to find best performers and set their roles for more optimally working teams. Our research supports the concept of using the right framework can make a big positive difference in team analytics.Â Â
Higher education during the pandemic. An overview of the higher education system in Uzbekistan
Authors: Cytlak Izabela , Mamadaminova Nigora
Published: October 2021

The article focuses on the development of the education system in Uzbekistan after the country gained independence. It highlights the obstacles to the development of the education system and changes during the pandemic, particularly in the sphere of higher education. The purpose of this research is to provide an overview of the steps in the development of education within the state. It will also evaluate the present condition of higher education during the pandemic. The conclusion states the findings and recommendations for further development.
DENOUNCER
Authors: Li Jinyang , Moskovitch Yuval , Jagadish H. V.
Published: October 2021

The use of automated data-driven tools for decision-making has gained popularity in recent years. At the same time, the reported cases of algorithmic bias and discrimination increase as well, which in turn lead to an extensive study of algorithmic fairness. Numerous notions of fairness have been proposed, designed to capture different scenarios. These measures typically refer to a "protected group" in the data, defined using values of some sensitive attributes. Confirming whether a fairness definition holds for a given group is a simple task, but detecting groups that are treated unfairly by the algorithm may be computationally prohibitive as the number of possible groups is combinatorial. We present a method for detecting such groups efficiently for various fairness definitions. Our solution is implemented in a system called DENOUNCER, an interactive system that allows users to explore different fairness measures of a (trained) classifier for a given test data. We propose to demonstrate the usefulness of DENOUNCER using real-life data and illustrate the effectiveness of our method.
TraNCE
Authors: Smith Jaclyn , Benedikt Michael , Moore Brandon , Nikolic Milos
Published: October 2021

Nested relational query languages have long been seen as an attractive tool for scenarios involving large hierarchical datasets. There has been a resurgence of interest in nested relational languages. One driver has been the affinity of these languages for large-scale processing platforms such as Spark and Flink.

This demonstration gives a tour of TraNCE, a new system for processing nested data on top of distributed processing systems. The core innovation of the system is a compiler that processes nested relational queries in a series of transformations; these include variants of two prior techniques, shredding and unnesting, as well as a
materialization
transformation that customizes the way levels of the nested output are generated. The TraNCE platform builds on these techniques by adding components for users to create and visualize queries, as well as data exploration and notebook execution targets to facilitate the construction of large-scale data science applications. The demonstration will both showcase the system from the viewpoint of usability by data scientists and illustrate the data management techniques employed.
Automatic data acquisition for deep learning
Authors: Liu Jiabin , Zhu Fu , Chai Chengliang , Luo Yuyu , Tang Nan
Published: October 2021

Deep learning (DL) has widespread applications and has revolutionized many industries. Although automated machine learning (AutoML) can help us away from coding for DL models, the acquisition of
lots of
high-quality data for model training remains a main bottleneck for many DL projects, simply because it requires high human cost. Despite many works on weak supervision (
i.e.
, adding weak labels to seen data) and data augmentation (
i.e.
, generating more data based on seen data), automatically acquiring training data, via smartly searching a pool of training data collected from open ML benchmarks and data markets, is not explored.

In this demonstration, we demonstrate a new system, automatic data acquisition (AutoData), which automatically searches training data from a heterogeneous data repository and interacts with AutoML. It faces two main challenges. (1) How to search high-quality data from a large repository for a given DL task? (2) How does AutoData interact with AutoML to guide the search? To address these challenges, we propose a reinforcement learning (RL)-based framework in AutoData to guide the iterative search process. AutoData encodes current training data and feedbacks of AutoML, learns a policy to search fresh data, and trains in iterations. We demonstrate with two real-life scenarios, image classification and relational data prediction, showing that AutoData can select high-quality data to improve the model.
DBMind
Authors: Zhou Xuanhe , Jin Lianyuan , Sun Ji , Zhao Xinyang , Yu Xiang , Feng Jianhua , Li Shifu , Wang Tianqing , Li Kun , Liu Luyang
Published: October 2021

We demonstrate a self-driving system DBMind, which provides three autonomous capabilities in database, including self-monitoring, self-diagnosis and self-optimization. First, self-monitoring judiciously collects database metrics and detects anomalies (e.g., slow queries and IO contention), which can profile database status while only slightly affecting system performance (<5%). Then, self-diagnosis utilizes an LSTM model to analyze the root causes of the anomalies and automatically detect root causes from a pre-defined failure hierarchy. Next, self-optimization automatically optimizes the database performance using learning-based techniques, including deep reinforcement learning based knob tuning, reinforcement learning based index selection, and encoder-decoder based view selection. We have implemented DBMind in an open source database openGauss and demonstrated real scenarios.
Full encryption
Authors: Zhu Jinwei , Cheng Kun , Liu Jiayang , Guo Liang
Published: October 2021

In this paper, we present a novel mechanism called Full Encryption (FE) in GaussDB. FE-in-GaussDB provides column-level encryption for sensitive data, and secures the asset from any malicious cloud administrator or information leakage attack. It ensures not only the security of operations on ciphertext data, but also the efficiency of query execution, by combining the advantages of cryptography algorithms (i.e. software mode) and Trusted Execution Environment (i.e. hardware mode). With this, FE-in-GaussDB supports full-scene query processing including the matching, the comparison and other rich computing functionalities. We demonstrate the prototype of FE-in-GaussDB and an experimental performance evaluation to prove its availability and effectiveness.
Compliant geo-distributed data processing in action
Authors: Beedkar Kaustubh , Brekardin David , Quiané-Ruiz Jorge-Anulfo , Markl Volker
Published: October 2021

In this paper we present our work on compliant geo-distributed data processing. Our work focuses on the new dimension of dataflow constraints that regulate the movement of data across geographical or institutional borders. For example, European directives may regulate transferring only certain information fields (such as non personal information) or aggregated data. Thus, it is crucial for distributed data processing frameworks to consider compliance with respect to dataflow constraints derived from these regulations. We have developed a compliance-based data processing framework, which (i) allows for the declarative specification of dataflow constraints, (ii) determines if a query can be translated into a compliant distributed query execution plan, and (iii) executes the compliant plan over distributed SQL databases. We demonstrate our framework using a geo-distributed adaptation of the TPC-H benchmark data. Our framework provides an interactive dashboard, which allows users to specify dataflow constraints, and analyze and execute compliant distributed query execution plans.
GEDet
Authors: Guan Sheng , Ma Hanchao , Choudhury Sutanay , Wu Yinghui
Published: October 2021

Detecting nodes with erroneous values in real-world graphs remains challenging due to the lack of examples and various error scenarios. We demonstrate GEDet, an error detection engine that can detect erroneous nodes in graphs with a few examples. The GEDet framework tackles error detection as a few-shot node classification problem. We invite the attendees to experience the following unique features. (1)
Few-shot detection
. Users only need to provide a few examples of erroneous nodes to perform error detection with GEDet. GEDet achieves desirable accuracy with (a) a graph augmentation module, which automatically generates synthetic examples to learn the classifier, and (b) an adversarial detection module, which improves classifiers to better distinguish erroneous nodes from both cleaned nodes and synthetic examples. We show that GEDet significantly improves the state-of-the-art error detection methods. (2)
Diverse error scenarios
. GEDet profiles data errors with a built-in library of transformation functions from correct values to errors. Users can also easily "plug in" new error types or examples. (3)
User-centric detection
. GEDet supports (a) an active learning mode to engage users to verify detected results, and adapts the error detection process accordingly; and (b) visual interfaces to interpret and track detected errors.
Davos
Authors: Shang Zeyuan , Zgraggen Emanuel , Buratti Benedetto , Eichmann Philipp , Karimeddiny Navid , Meyer Charlie , Runnels Wesley , Kraska Tim
Published: October 2021

Recently, a new horizon in data analytics, prescriptive analytics, is becoming more and more important to make data-driven decisions. As opposed to the progress of democratizing data acquisition and access, making data-driven decisions remains a significant challenge for people without technical expertise. In this regard, existing tools for data analytics which were designed decades ago still present a high bar for domain experts, and removing this bar requires a fundamental rethinking of both interface and backend.

At Einblick, an MIT/Brown spin-off based on the Northstar project, we have been building the next generation analytics tool in the last few years. To overcome the shortcomings of existing processing engines, we propose
Davos
, Einblick's novel backend.
Davos
combines aspects of progressive computation, approximate query processing and sampling, with a specific focus on supporting user-defined operations. Moreover,
Davos
optimizes multi-tenant scenarios to promote collaboration. Both empirical evaluation and user study verify that
Davos
can greatly empower data analytics for new needs.
Mixer
Authors: Qin An , Xiao Mengbai , Wu Yongwei , Huang Xinjie , Zhang Xiaodong
Published: October 2021

Visual contents, including images and videos, are dominant on the Internet today. The conventional search engine is mainly designed for textual documents, which must be extended to process and manage increasingly high volumes of visual data objects.

In this paper, we present Mixer, an effective system to identify and analyze visual contents and to extract their features for data retrievals, aiming at addressing two critical issues: (1) efficiently and timely understanding visual contents, (2) retrieving them at high precision and recall rates without impairing the performance. In Mixer, the visual objects are categorized into different classes, each of which has representative visual features. Subsystems for model production and model execution are developed. Two retrieval layers are designed and implemented for images and videos, respectively. In this way, we are able to perform aggregation retrievals of the two types in efficient ways. The experiments with Baidu's production workloads and systems show that Mixer halves the model production time and raises the feature production throughput by 9.14x. Mixer also achieves the precision and recall of video retrievals at 95% and 97%, respectively. Mixer has been in its daily operations, which makes the search engine highly scalable for visual contents at a low cost. Having observed productivity improvement of upper-level applications in the search engine, we believe our system framework would generally benefit other data processing applications.
RAMP-TAO
Authors: Cheng Audrey , Shi Xiao , Pan Lu , Simpson Anthony , Wheaton Neil , Lawande Shilpa , Bronson Nathan , Bailis Peter , Crooks Natacha , Stoica Ion
Published: October 2021

Facebook's graph store TAO, like many other distributed data stores, traditionally prioritizes availability, efficiency, and scalability over strong consistency or isolation guarantees to serve its large, read-dominant workloads. As product developers build diverse applications on top of this system, they increasingly seek transactional semantics. However, providing advanced features for select applications while preserving the system's overall reliability and performance is a continual challenge. In this paper, we first characterize developer desires for transactions that have emerged over the years and describe the current failure-atomic (i.e., write) transactions offered by TAO. We then explore how to introduce an intuitive read transaction API. We highlight the need for atomic visibility guarantees in this API with a measurement study on potential anomalies that occur without stronger isolation for reads. Our analysis shows that 1 in 1,500 batched reads reflects partial transactional updates, which complicate the developer experience and lead to unexpected results. In response to our findings, we present the RAMP-TAO protocol, a variation based on the Read Atomic Multi-Partition (RAMP) protocols that can be feasibly deployed in production with minimal overhead while ensuring atomic visibility for a read-optimized workload at scale.
Knowledge graphs 2021
Authors: Weikum Gerhard
Published: October 2021

Providing machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing vision and challenge for AI. Over the last 15 years, huge knowledge bases, also known as knowledge graphs, have been automatically constructed from web data, and have become a key asset for search engines and other use cases. Machine knowledge can be harnessed to semantically interpret texts in news, social media and web tables, contributing to question answering, natural language processing and data analytics. This position paper reviews these advances and discusses lessons learned. It highlights the role of "DB thinking" in building and maintaining high-quality knowledge bases from web contents. Moreover, the paper identifies open challenges and new research opportunities. In particular, extracting quantitative measures of entities (e.g., height of buildings or energy efficiency of cars), from text and web tables, presents an opportunity to further enhance the scope and value of knowledge bases.
Data augmentation for ML-driven data preparation and integration
Authors: Li Yuliang , Wang Xiaolan , Miao Zhengjie , Tan Wang-Chiew
Published: October 2021

In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques developed by the data management community for data preparation and data integration. In addition to surveying task-specific DA operators that leverage rules, transformations, and external knowledge for creating additional training data, we also explore the advanced DA techniques such as interpolation, conditional generation, and DA policy learning. Finally, we describe the connection between DA and other machine learning paradigms such as active learning, pre-training, and weakly-supervised learning. We hope that this discussion can shed light on future research directions for a holistic data augmentation framework for high-quality dataset creation.
Array DBMS
Authors: Zalipynis Ramon Antonio Rodriges
Published: October 2021

Array DBMSs strive to be the best systems for managing, processing, and even visualizing bigN-d arrays. The last decade blossomed with R&D in array DBMS, making it a young and fast-evolving area. We present the first comprehensive tutorial on array DBMS R&D. We start from past impactful results that are still relevant today, then we cover contemporary array DBMSs, array-oriented systems, and state-of-the-art research in array management, flavored with numerous promising R&D opportunities for future work. A great deal of our tutorial was not covered in any previous tutorial or survey article. Advanced array management research is just emerging and many R&D opportunities still "lie on the surface". Hence, nowadays we have the most favorable conditions to start contributing to this research area. This tutorial will jump-start such efforts.
TSCache
Authors: Liu Jian , Wang Kefei , Chen Feng
Published: October 2021

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, calledTSCache. By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.
View selection over knowledge graphs in triple stores
Authors: Mailis Theofilos , Kotidis Yannis , Christoforidis Stamatis , Kharlamov Evgeny , Ioannidis Yannis
Published: October 2021

Knowledge Graphs (KGs) are collections of interconnected and annotated entities that have become powerful assets for data integration, search enhancement, and other industrial applications. Knowledge Graphs such as DBPEDIA may contain billion of triple relations and are intensively queried with millions of queries per day. A prominent approach to enhance query answering on Knowledge Graph databases is View Materialization, ie., the materialization of an appropriate set of computations that will improve query performance.

We study the problem of view materialization and propose a view selection methodology for processing query workloads with more than a million queries. Our approach heavily relies on subgraph pattern mining techniques that allow to create efficient summarizations of massive query workloads while also identifying the candidate views for materialization. In the core of our work is the correspondence between the
view selection
problem to that of
Maximizing a Nondecreasing Submodular Set Function Subject to a Knapsack Constraint
. The latter leads to a tractable view-selection process for native triple stores that allows a (1 -
e
---1
)-approximation of the optimal selection of views. Our experimental evaluation shows that all the steps of the view-selection process are completed in a few minutes, while the corresponding rewritings accelerate 67.68% of the queries in the DBPEDIA query workload. Those queries are executed in 2.19% of their initial time on average.
Impact of fear in a prey-predator system with herd behaviour
Authors: Saha Sangeeta , Samanta Guruprasad
Published: October 2021

Fear of predation plays an important role in the growth of a prey species in a prey-predator system. In this work, a two-species model is formulated where the prey species move in a herd to protect themselves and so it acts as a defense strategy. The birth rate of the prey here is affected due to fear of being attacked by predators and so, is considered as a decreasing function. Moreover, there is another fear term in the death rate of the prey population to emphasize the fact that the prey may die out of fear of predator too. But, in this model, the function characterizing the fear effect in the death of prey is assumed in such a way that it is increased only up to a certain level. The results show that the system performs oscillating behavior when the fear coefficient implemented in the birth of prey is considered in a small amount but it changes its dynamics through Hopf bifurcation and becomes stable for a higher value of the coefficient. Regulating the fear terms ultimately makes an impact on the growth of the predator population as the predator is taken as a specialist predator here. The increasing value of the fear terms (either implemented in birth or death of prey) decrease the count of the predator population with time. Also, the fear implemented in the birth rate of prey makes a higher impact on the growth of the predator population than in the case of the fear-induced death rate.
Processing into Dominance: Nero, the Crowning of Tiridates I, and a New Narrative of Rome’s Supremacy in the East
Authors: Clark Timothy
Published: October 2021

In 66 CE, the emperor Nero crowned the Parthian prince Tiridates I king of Armenia before the Roman people in the Forum Romanum. Much scholarship on Roman interactions with Parthia or Armenia focuses on histories of military conflict or diplomatic negotiation. Ritual and ceremonial evidence, however, is often taken for granted. This article uses the coronation to highlight a different way in which Rome articulated its relations with Parthia and Armenia to domestic and foreign audiences. It will show how Nero and his regime used the art of public spectacle to project an image of Roman superiority over Parthia and Armenia in spite of Roman military losses in the recent Armenian war. Tiridates, a Parthian prince and a brother of the Parthian king of kings, traveled to Rome to be crowned the first king of Armenia from the Parthian royal family. To receive this title, Tiridates passed by several monuments to Augustan triumphs over Parthia and Armenia in the Forum. He was also surrounded by a group of Roman citizens, who watched him as they would have watched a defeated foreign leader in a triumph. At the culmination of the ceremony, Tiridates performedproskynesisbefore Nero at therostra Augustiand was granted his crown. Through Augustus’ monuments, the collective viewing of Tiridates, and his acts of public submission and deference to Nero, the crowning intimated a new narrative about the state of Roman-Parthian/Armenian relations. While Augustus had represented Parthian and Armenian defeat in art, Nero had compelled a representative of both Parthia and Armenia to come to Rome and kneel before the emperor. Both states were now subservient to Rome, which remained the dominant power in the East.
Immediate effect of a meditation technique on blood glucose, state anxiety and relaxation in patients with type 2 diabetes: a pilot randomized crossover study
Authors: Wen Xu , Saoji Apar Avinash , Metri Kashinath , Mohanty Sriloy , Vijayakumar Venugopal
Published: October 2021

Objectives

Type 2 diabetes mellitus (T2DM) is a major burden on global health and economy. Various Yogic techniques are found to be beneficial in the management of T2DM. Mind Sound Resonance Technique (MSRT) is one of the yoga-based meditation techniques observed to be effective in clinical settings.

Methods

Thirty-two patients with T2DM were randomized to either MSRT or supine rest (SR) sessions on two separate days separated by a washout period of one day. Fasting blood glucose levels were measured before and immediately after the sessions. State anxiety and subjective feeling of relaxation were assessed using Spielberg’s state anxiety inventory (STAI) and Visual Analogue Scale (VAS) respectively.

Results

There were significant differences between MSRT and SR groups in fasting blood glucose (p=0.019), STAI scores (p<0.001) and subjective relaxation (p<0.001). Within group analyses revealed significant reductions (p<0.001) in fasting blood glucose and STAI scores, along with an increase in subjective relaxation following the practice of MSRT, whereas, non-significant changes were found following the SR session.

Conclusions

A single session of MSRT was found to reduce blood glucose levels and state anxiety along with enhanced relaxation when compared to SR.