Remote Work Optimization with Robust Multi-channel Graph Neural Networks

The spread of COVID-19 leads to the global shutdown of many corporate offices, and encourages companies to open more opportunities that allow employees to work from a remote location. As the workplace type expands from onsite offices to remote areas, an emerging challenge for an online hiring marketplace is how these remote opportunities and user intentions to work remotely can be modeled and matched without prior information. Despite the unprecedented amount of remote jobs posted amid COVID-19, there is no existing approach that can be directly applied. Introducing a brand new workplace type naturally leads to the cold-start problem, which is particularly more common for less active job seekers. It is challenging, if not impossible, to onboard a new workplace type for any predictive model if existing information sources can provide little information related to a new category of jobs, including data from resumes and job descriptions. Hence, in this work, we aim to propose a principled approach that jointly models the remoteness of job seekers and job opportunities with limited information, which also suffices the needs of web-scale applications. Existing research on the emerging type of remote workplace mainly focuses on qualitative studies, and classic predictive modeling approaches are inapplicable considering the problem of cold-start and information scarcity. We precisely try to close this gap with a novel graph neural architecture. Extensive experiments on large-scale data from real-world applications have been conducted to validate the superiority of the proposed approach over competitive baselines. The improvement may translate to more rapid onboarding of the new workplace type that can benefit job seekers who are interested in working remotely

Clearwater: Extensible, Flexible, Modular Code Generation

International audienceDistributed applications typically interact with a number of heterogeneous and autonomous components that evolve independently. Methodical development of such applications can benefit from approaches based on domain-specific languages (DSLs). However, the evolution and customization of heterogeneous components introduces significant challenges to accommodating the syntax and semantics of a DSL in addition to the heterogeneous platforms on which they must run. In this paper, we address the challenge of implementing code generators for two such DSLs that are flexible (resilient to changes in generators or input formats), extensible (able to support multiple output targets and multiple input variants), and modular (generated code can be rewritten). Our approach, Clearwater, leverages XML and XSLT standards: XML supports extensibility and mutability for inprogress specification formats, and XSLT provides flexibility and extensibility for multiple target languages. Modularity arises from using XML meta-tags in the code generator itself, which supports controlled addition, subtraction, or replacement to the generated code via XML-weaving. We discuss the use of our approach and show its advantages in two non-trivial code generators: the Infopipe Stub Generator (ISG) to support distributed flow applications, and the Automated Composable Code Translator to support automated distributed application deployment. As an example, the ISG accepts as input an XML description and generates output for C, C++, or Java using a number of communications platforms such as sockets and publish-subscribe

Gigahertz-rate-switchable wavefront shaping through integration of metasurfaces with photonic integrated circuit

Achieving spatiotemporal control of light at high-speeds presents immense possibilities for various applications in communication, computation, metrology, and sensing. The integration of subwavelength metasurfaces and optical waveguides offers a promising approach to manipulate light across multiple degrees of freedom at high-speed in compact photonic integrated circuit (PICs) devices. Here, we demonstrate a gigahertz-rate-switchable wavefront shaping by integrating metasurface, lithium niobite on insulator (LNOI) photonic waveguide and electrodes within a PIC device. As proofs of concept, we showcase the generation of a focus beam with reconfigurable arbitrary polarizations, switchable focusing with lateral focal positions and focal length, orbital angular momentum light beams (OAMs) as well as Bessel beams. Our measurements indicate modulation speeds of up to gigahertz rate. This integrated platform offers a versatile and efficient means of controlling light field at high-speed within a compact system, paving the way for potential applications in optical communication, computation, sensing, and imaging

Evaluation of the Observational Associations and Shared Genetics Between Glaucoma With Depression and Anxiety

PURPOSE: Glaucoma, a leading cause of blindness worldwide, is suspected to exhibit a notable association with psychological disturbances. This study aimed to investigate epidemiological associations and explore shared genetic architecture between glaucoma and mental traits, including depression and anxiety.METHODS: Multivariable logistic regression and Cox proportional hazards regression models were employed to investigate longitudinal associations based on UK Biobank. A stepwise approach was used to explore the shared genetic architecture. First, linkage disequilibrium score regression inferred global genetic correlations. Second, MiXeR analysis quantified the number of shared causal variants. Third, specific shared loci were detected through conditional/conjunctional false discovery rate (condFDR/conjFDR) analysis and characterized for biological insights. Finally, two-sample Mendelian randomization (MR) was conducted to investigate bidirectional causal associations.RESULTS: Glaucoma was significantly associated with elevated risks of hospitalized depression (hazard ratio [HR] = 1.54; 95% confidence interval [CI], 1.01-2.34) and anxiety (HR = 2.61; 95% CI, 1.70-4.01) compared to healthy controls. Despite the absence of global genetic correlations, MiXeR analysis revealed 300 variants shared between glaucoma and depression, and 500 variants shared between glaucoma and anxiety. Subsequent condFDR/conjFDR analysis discovered 906 single-nucleotide polymorphisms (SNPs) jointly associated with glaucoma and depression and two associated with glaucoma and anxiety. The MR analysis did not support robust causal associations but indicated the existence of pleiotropic genetic variants influencing both glaucoma and depression.CONCLUSIONS: Our study enhances the existing epidemiological evidence and underscores the polygenic overlap between glaucoma and mental traits. This observation suggests a correlation shaped by pleiotropic genetic variants rather than being indicative of direct causal relationships.</p

Evaluation of the Observational Associations and Shared Genetics Between Glaucoma With Depression and Anxiety

PURPOSE: Glaucoma, a leading cause of blindness worldwide, is suspected to exhibit a notable association with psychological disturbances. This study aimed to investigate epidemiological associations and explore shared genetic architecture between glaucoma and mental traits, including depression and anxiety.METHODS: Multivariable logistic regression and Cox proportional hazards regression models were employed to investigate longitudinal associations based on UK Biobank. A stepwise approach was used to explore the shared genetic architecture. First, linkage disequilibrium score regression inferred global genetic correlations. Second, MiXeR analysis quantified the number of shared causal variants. Third, specific shared loci were detected through conditional/conjunctional false discovery rate (condFDR/conjFDR) analysis and characterized for biological insights. Finally, two-sample Mendelian randomization (MR) was conducted to investigate bidirectional causal associations.RESULTS: Glaucoma was significantly associated with elevated risks of hospitalized depression (hazard ratio [HR] = 1.54; 95% confidence interval [CI], 1.01-2.34) and anxiety (HR = 2.61; 95% CI, 1.70-4.01) compared to healthy controls. Despite the absence of global genetic correlations, MiXeR analysis revealed 300 variants shared between glaucoma and depression, and 500 variants shared between glaucoma and anxiety. Subsequent condFDR/conjFDR analysis discovered 906 single-nucleotide polymorphisms (SNPs) jointly associated with glaucoma and depression and two associated with glaucoma and anxiety. The MR analysis did not support robust causal associations but indicated the existence of pleiotropic genetic variants influencing both glaucoma and depression.CONCLUSIONS: Our study enhances the existing epidemiological evidence and underscores the polygenic overlap between glaucoma and mental traits. This observation suggests a correlation shaped by pleiotropic genetic variants rather than being indicative of direct causal relationships.</p

Partial persistent sequences and their applications to collaborative text document editing and processing

In a variety of text document editing and processing applications, it is necessary to keep track of the revision history of text documents by recording changes and the metadata of those changes (e.g., user names and modification timestamps). The recent Web 2.0 document editing and processing applications, such as real-time collaborative note taking and wikis, require fine-grained shared access to collaborative text documents as well as efficient retrieval of metadata associated with different parts of collaborative text documents. Current revision control techniques only support coarse-grained shared access and are inefficient to retrieve metadata of changes at the sub-document granularity. In this dissertation, we design and implement partial persistent sequences (PPSs) to support real-time collaborations and manage metadata of changes at fine granularities for collaborative text document editing and processing applications. As a persistent data structure, PPSs have two important features. First, items in the data structure are never removed. We maintain necessary timestamp information to keep track of both inserted and deleted items and use the timestamp information to reconstruct the state of a document at any point in time. Second, PPSs create unique, persistent, and ordered identifiers for items of a document at fine granularities (e.g., a word or a sentence). As a result, we are able to support consistent and fine-grained shared access to collaborative text documents by detecting and resolving editing conflicts based on the revision history as well as to efficiently index and retrieve metadata associated with different parts of collaborative text documents. We demonstrate the capabilities of PPSs through two important problems in collaborative text document editing and processing applications: data consistency control and fine-grained document provenance management. The first problem studies how to detect and resolve editing conflicts in collaborative text document editing systems. We approach this problem in two steps. In the first step, we use PPSs to capture data dependencies between different editing operations and define a consistency model more suitable for real-time collaborative editing systems. In the second step, we extend our work to the entire spectrum of collaborations and adapt transactional techniques to build a flexible framework for the development of various collaborative editing systems. The generality of this framework is demonstrated by its capabilities to specify three different types of collaborations as exemplified in the systems of RCS, MediaWiki, and Google Docs respectively. We precisely specify the programming interfaces of this framework and describe a prototype implementation over Oracle Berkeley DB High Availability, a replicated database management engine. The second problem of fine-grained document provenance management studies how to efficiently index and retrieve fine-grained metadata for different parts of collaborative text documents. We use PPSs to design both disk-economic and computation-efficient techniques to index provenance data for millions of Wikipedia articles. Our approach is disk economic because we only save a few full versions of a document and only keep delta changes between those full versions. Our approach is also computation-efficient because we avoid the necessity of parsing the revision history of collaborative documents to retrieve fine-grained metadata. Compared to MediaWiki, the revision control system for Wikipedia, our system uses less than 10% of disk space and achieves at least an order of magnitude speed-up to retrieve fine-grained metadata for documents with thousands of revisions.PhDCommittee Chair: Calton Pu; Committee Member: Lakshmish Ramaswamy; Committee Member: Leo Mark; Committee Member: Ling Liu; Committee Member: Sham Navath

Consistency in Real-time Collaborative Editing Systems Based on Partial Persistent Sequences

In real-time collaborative editing systems, users create a shared document by issuing insert, delete, and undo operations on their local replica anytime and anywhere. Data consistency issues arise due to concurrent editing conflicts. Traditional consistency models put restrictions on editing operations updating different portions of a shared document, which is unnecessary for many editing scenarios, and cause their view synchronization strategies to become less efficient. To address these problems, we propose a new data consistency model that preserves convergence and synchronizes editing operations only when they access overlapped or contiguous characters. Our view synchronization strategy is implemented by a novel data structure–partial persistent sequence. A partial persistent sequence is an ordered set of items indexed by persistent and unique position identifiers. It captures data dependencies of editing operations and encodes them in a way that they can be correctly executed on any document replica. As a result, a simple and efficient view synchronization strategy can be implemented

DAG Synchronization Constraint Language for Business Processes

Correct synchronization among activities is critical in a business process. Current workflow languages such as BPEL specify the control flow of processes explicitly. However, their procedural style may cause inflexibility and tangled code. We propose DSCL (Dag-acyclic-graph Synchronization Constraint Language) to achieve three desirable properties for a synchronization modeling language: declarative syntax, fine granularity and validation support. Instead of composing service out of structured constructs, DSCL declaratively describe the synchronization constraints in three basic relations on activity states. The state relationships collectively determine the execution order of activities in a composite process. The relationships are automatically translated into Petri Nets and simulated in the CPN/Tools, from which several correctness criteria can be validated for the composite process. We illustrate the advantages of DSCL with a Purchasing workflow example from BPEL 1.0 specification, and verify its correctness using CPN/Tools. 1

Cosmos: A Wiki Data Management System

Wiki applications are becoming increasingly important for knowledge sharing between large numbers of users. To prevent against vandalism and recover from destructive edits, wiki applications need to maintain the revision histories of all documents. Due to the large amounts of data and traffic, a Wiki application needs to store the data economically and retrieve documents efficiently. Current Wiki Data Management Systems (WDMS) make a trade-off between storage requirement and access time for document update and retrieval. We introduce a new data management system, Cosmos, to balance this trade-off. To compare Cosmos with the other WDMSs, we use a 68GB data sample from English Wikipedia. Our experiments show that Cosmos uses one-fifth of the disk space when compared to MediaWiki (Wikipedia’s backend) and performs faster than other WDMSs at document retrieval

Clinical implication and immunological characterisation of the ARF-GEF family member CYTH4 in ovarian cancer

Background The GTP exchange factors on ADP-ribosylation factor (ARF) mediate the GDP/GTP exchange on ARF, serve as regulators in protein trafficking and membrane dynamics, and play critical roles in various cell processes. However, the relationship between the expression of ARF-GEF family genes and clinical implications in ovarian cancer remains unclear. Methods We performed a systematic investigation on the role of ARF-GEF family genes in ovarian cancer by using Gene Expression Omnibus (GEO), Kaplan-Meier plotter, Gene set enrichment analysis (GSEA), TIMER and TISIDB database. Results We found that the ARF-GEF family gene CYTH4 exhibited significant expressional upregulation in ovarian cancer compared to normal ovary tissues. The expression of CYTH4 was also higher in metastases from the omentum than in matched primary ovarian tumours. Kaplan-Meier plotter showed that high expression of CYTH4 predicted worse overall survival, progression free survival and post-progression survival of ovarian cancer patients. Notably, from our correlation analysis, CYTH4 expression showed closely association with tumour-infiltrating immune cells. Intriguingly, the expression of CYTH4 was also significantly correlated with a variety of immunomodulators, chemokines and major histocompatibility complex molecules. Conclusion Overall, our findings provide a valuable source of data about the clinical significance of CYTH4 in ovarian cancer