Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecological drift during colonization drives within- and between-host heterogeneity in animal symbiont populations

Host-associated microbiomes vary greatly in composition both within and between host individuals, providing the raw material for natural selection to act on host-microbe associations. Nonetheless, the drivers of compositional heterogeneity in host-associated microbiomes have only rarely been examined. To understand how this heterogeneity arises, we utilize the squash bug, Anasa tristis, and its bacterial symbionts in the genus Caballeronia. We artificially modulate symbiont bottleneck size and strain diversity during colonization to demonstrate the significance of ecological drift, which causes stochastic fluctuations in community composition. Consistent with predictions from the neutral theory of biodiversity, ecological drift alone can account for heterogeneity in symbiont community composition between hosts, even when two strains are nearly genetically identical. When acting on competing, unrelated strains, ecological drift can maintain symbiont genetic diversity among different hosts by stochastically determining the dominant strain within each host. Finally, ecological drift mediates heterogeneity in isogenic symbiont populations even within a single host, along a consistent gradient running the anterior-posterior axis of the symbiotic organ. Our results demonstrate that symbiont population structure across scales does not necessarily require host-mediated selection, but emerges as a result of ecological drift acting on both isogenic and unrelated competitors. Our findings illuminate the processes that might affect symbiont transmission, coinfection, and population structure in nature, which can drive the evolution of host-microbe symbiosis and microbe-microbe interactions within host-associated microbiomes.
Front-end design prototyping strategies during remote stakeholder engagement

Abstract Engineers must engage project stakeholders effectively if stakeholder needs are to be met, and prototypes are key tools for communicating design form and function. Quality stakeholder engagement in the front end of design processes, in particular, is critical in the success or failure of design projects. As remote stakeholder engagement has become increasingly common as industry trends toward distributed design, there is a need to develop the theory and practices behind effective remote design processes, which have not yet been as well-studied as in-person design. This study explored the prototyping strategies for remote stakeholder engagement during front-end design used by 10 engineering practitioners and 10 senior engineering students through semi-structured interviews. Prototyping strategies were found to overlap with many of the strategies described by prior literature that are not specific to remote engagement modes, though several of these strategies were adapted to the remote context, and three emergent strategies for prototyping in remote engagements were identified. Designers’ perceptions of remote versus in-person prototyping strategies for stakeholder engagement in front-end design, including perceived advantages and limitations, were also explored, and recommendations for educators to better prepare engineering students for hybrid and remote work are provided.
Climate change and U.S. water resources

THE SETTING: The Issues Future Water Use in the Present Climate Prospects for Climate Change Decision- Making Criteria Under Uncertainty FROM CLIMATE TO WATER RESOURCES: Climate Forecasting What Statistics Can Tell Us From Climate and CO2 Enrichment to Evapotranspiration From Climate to Flow From Flow to Storage Vulnerability of Water Systems IMPACT AND RESPONSES: Floods and Droughts Irrigation Water Quality Recreation and Wildlife Urban Water Water, Electricity and Institutional Innovation Reallocation by Markets and Prices The Political Agenda CLIMATE CHANGE AND US WATER RESOURCES: Summary.
Getting to the one: Prioritizing an idea set using preference-based decision-specific heuristics

ABSTRACT We propose and test a process where potential entrepreneurs (PEs) prioritize a venture idea consideration set using preference-based decision-specific heuristics to assess idea feasibility and desirability. We test our hypotheses through two studies with PEs. The first experiment shows that prioritization occurs, with 113 of 122 PEs voluntarily changing a randomized list of their ideated ventures into a rank-ordered priority list of potential opportunities. Second, we employ a novel “equivocal forced-choice” conjoint design with 250 PEs. We find empirical support that PEs prioritize via relative preferences for experience-based knowledge, strong social ties, and low risk/low reward venture ideas. We contribute to the entrepreneurship literature by theorizing and providing evidence of a prioritization stage for multiple idea sets before evaluation. Further, we demonstrate the influence of individual and social network factors on prioritization and expand our understanding of how PEs conceptualize risk in venturing.
Preliminary Concept Paper and Project Proposal Volunteer Computing Please Do Not Distribute without Permission

The introduction of Java by Sun Microsystems, and the recent explosive growth of interest in platform-independent executable content on the WorldWide Web , has opened exciting new possibilites in parallel computing. This paper proposes the idea of volunteer computing , which aims to allow people from anywhere in the world to volunteer their computers' processing power towards solving a large parallel problem by simply browsing a web page. Because it requires no prior human contact and very little technical knowledge from the client user, volunteer computing makes it possible to very quickly build a massively parallel computer marshalling the strength of thousands, or even millions, of computers around the world. Volunteer computing is useful in both true volunteer situations, where the volunteers come from independent administrative domains (e.g., when anybody on the Internet can volunteer), and in quasi-volunteer situations, where the volunteers all belong to the same administrative domain (e.g., within an intranet). In both cases, a volunteer computing system would allow one to forgo the clumsy and time-consuming setup procedures required by currently available network-of-workstations systems software, making it possible to build heterogenous networks-of-workstations orders-of-magnitude larger than any that have been built so far. The goal of this paper is to spur enough interest in the MIT community to embark on a research project that will produce a usable volunteer computing system. It proposes possible implementations of volunteer computing using Java, and discusses issues and problems that arise, such as motivation, applications, eeciency, security, and reliability. The potential contributions of a successful volunteer computing project, including such interesting topics as the \client-as-server" model, massive networks-of-workstations, and networks-of-information-appliances, are also discussed. Finally, a preliminary survey of related work is provided, and their possible implications on the context of this research are brieey discussed.
ICLEA: Interactive Contrastive Learning for Self-supervised Entity Alignment

Self-supervised entity alignment (EA) aims to 001 link equivalent entities across different knowl- 002 edge graphs (KGs) without seed alignments. 003 The current SOTA self-supervised EA method 004 draws inspiration from contrastive learning, 005 originally designed in computer vision based 006 on instance discrimination and contrastive loss, 007 and suffers from two shortcomings. Firstly, it 008 puts unidirectional emphasis on pushing sam- 009 pled negative entities far away rather than 010 pulling positively aligned pairs close, as is 011 done in the well-established supervised EA. 012 Secondly, KGs contain rich side information 013 (e.g., entity description), and how to effec- 014 tively leverage those information has not been 015 adequately investigated in self-supervised EA. 016 In this paper, we propose an interactive con- 017 trastive learning model for self-supervised EA. 018 The model encodes not only structures and se- 019 mantics of entities (including entity name, en- 020 tity description, and entity neighborhood), but 021 also conducts cross-KG contrastive learning 022 by building pseudo-aligned entity pairs. Exper- 023 imental results show that our approach outper- 024 forms previous best self-supervised results by 025 a large margin (over 9% average improvement) 026 and performs on par with previous SOTA su- 027 pervised counterparts, demonstrating the effec- 028 tiveness of the interactive contrastive learning 029 for self-supervised EA. 030
Injectable butyrate-prodrug micelles induce long-acting immune modulation and suppress autoimmune arthritis in mice

Dysbiosis is linked to autoimmune diseases such as rheumatoid arthritis (RA), where microbial metabolites, such as short chain fatty acids (SCFAs), mediate the so-called gut-joint axis. The therapeutic potential of SCFAs is limited due to the frequent and high oral dosage requirements. RA is characterized by aberrant activation of peripheral T cells and myeloid cells. We aim to deliver butyrate, an SCFA, directly to the lymphatics using a polymeric micelle as a butyrate prodrug, creating a depot for inducing long-lasting immunomodulatory effects. Notably, negatively charged micelles (Neg-ButM) demonstrate superior efficacy in targeting the lymphatics post-subcutaneous administration, and were retained in the draining lymph nodes, spleen, and liver for over a month. In a mouse RA model, we found that Neg-ButM substantially mitigated arthritis symptoms and promoted tolerogenic phenotypes in T cells and myeloid cells, both locally and systemically. These findings suggest potential applications of this approach in treating inflammatory autoimmune diseases.
Revisiting Machine-Learning based Drug Repurposing: Drug Indications Are Not a Right Prediction Target

In this paper, we challenge the utility of approved drug indications as a prediction target for machine learning in drug repurposing (DR) studies. Our research highlights two major limitations of this approach: 1) the presence of strong confounding between drug indications and drug characteristics data, which results in shortcut learning, and 2) inappropriate normalization of indications in existing drug-disease association (DDA) datasets, which leads to an overestimation of model performance. We show that the collection patterns of drug characteristics data were similar within drugs of the same category and the Anatomical Therapeutic Chemical (ATC) classiﬁcation of drugs could be predicted by using the data collection patterns. Furthermore, we conﬁrm that the performance of existing DR models is signiﬁcantly degraded in the realistic evaluation setting we proposed in this study. We provide realistic data split information for two benchmark datasets, Fdataset and deepDR dataset.
Optimizing Cloud Data Lakes Queries

Cloud data lakes emerge as an inexpensive solution for storing very large amounts of data. The main idea is the separation of compute and storage layers. Thus, cheap cloud storage is used for storing the data, while compute engines are used for running analytics on this data in “on-demand” mode. However, to perform any computation on the data in this architecture, the data should be moved from the storage layer to the compute layer over the network for each calculation. Obviously, that hurts calculation performance and requires huge network bandwidth. Our research focuses on three related topics: (1) identify the key challenges to improving query performance in cloud data lakes, (2) provide a theoretical model that formally defines the problem of poor query performance in cloud data lakes, (3) design a practical solution to the problem and demonstrate its efficiency via large-scale experimental evaluation.
Sharjah, U.A.E.: the urban conservative dilemma

This thesis has two broad aims, to outline and analyse the urban development of the city of Sharjah, United Arab Emirates, illuminating significant events leading to the establishment and subsequent spatial expansion of the settlement. This analysis provides the basis from which the second part of the thesis is derived, thus fulfilling the second aim; to create a series of conservation zones in the city based upon the historical, architectural and cultural significance of groups of buildings, plus their feasibility for preservation according to their physical condition. It is to attempt to redress the balance of the wholesale demolition of historic areas of Middle-Eastern cities that this thesis is offered, in the hope that the suggestions contained within may provide, at the very least, a stimulus or springboard that could result in the extension and application of ideas to other Gulf cities, so resulting in the regional development of conservation zones. Chapters 1.1, 1.2 and 2 trace the establishment and development of the settlement called Sharjah, outlining significant historic events and their geographical impact upon the town, including the Master Plan of 1959.Chapter 3 widens the approach, comparing the town to others in the Middle East with respect to components considered to be essential elements of urban cores ('medinas'). Chapters 4, 5 and 6 analyse these individual components; religious institutional buildings, suqs and residential structures, collating evidence to create proposed conservation zones based on both broad-based patterns and individual case studies to exemplify such findings in detail. Chapter 7 outlines potential problems of large-scale conservation schemes, offering some possible solutions but significantly, presenting the dilemma common to many such cities: the conflict of land use at the heart of the city: its historic urban core. Chapter 8 concludes the thesis by summarizing all previous evidence and submitting up-to-date findings from a field visit in 1991, thus illustrating any significant developments with respect to conservation in the city.
Who Controlled the Evidence? Question Answering for Disclosure Information Extraction

Conflict of interest (COI) disclosure statements provide rich information to support trans-parency and reduce bias in research. We introduce a novel task to identify relationships between sponsoring entities and the research studies they sponsor from the disclosure statement. This task is challenging due to the complexity of recognizing all potential relationship patterns and the hierarchical nature of identifying entities first and then extracting their relationships to the study. To overcome these challenges, in this paper, we also constructed a new annotated dataset and proposed a Question Answering-based method to recognize entities and extract relationships. Our method has demonstrated robustness in handling diverse relationship patterns, and it remains effective even when trained on a low-resource dataset.
Active Inference of Extended Finite State Models of Software Systems

Extended finite state machines (EFSMs) model stateful systems with internal data variables, and have many software engineering applications. It is possible to infer such models by observing system behaviour. Still, existing approaches are either limited to classical FSM models with no internal data state, or implicitly require the ability to reset the sys-tem under inference, which may not always be possible. We present an extension to the hW-inference algorithm that can infer EFSM models, with input and output parameters as well as guards and internal registers and their data update functions, from systems without a reliable reset. For the problem to be tractable, we require some assumptions on the observability and determinism of the system. The main restriction is that the control flow of the system must be finite, although data types could be infinite.
Towards Workload Trend Time Series Probabilistic Prediction via Probabilistic Deep Learning

The workloads of autonomous driving traffic accident cloud data centers exhibit high variance and uncertainty. Accurate modeling and prediction of the variance and uncertainty of cloud workloads are crucial for the realization of reliable resource management in cloud data centers. Existing solutions are point prediction methods that can not capture the variance and uncertainty of the cloud workloads. In this paper, we propose a workload probabilistic prediction method with deep learning to model and predict the variance and uncertainty of cloud workload. Our method is a hybrid deep learning model which combines exponential smoothing, bidirectional long short-term memory (BLSTM) and quantile regression. First, a cloud workload pre-processing method based on exponential smoothing is proposed to smooth the high variance feature of cloud workloads. Then, a BLSTM based cloud workload algorithm is introduced. Finally, a differentiable quantile loss function is introduced into the prediction model to generate predictions of multiple quantiles. The experimental results on the Google cluster trace show that our method outperforms other four baseline models.
Traffic Spatial-Temporal Prediction Based on Neural Architecture Search

Traffic spatial-temporal prediction is essential for intelligent transportation systems. However, the current approach relies heavily on expert knowledge and time-consuming manual modeling. Neural architecture search can build models adaptively, but it is rarely used for traffic spatial-temporal prediction, nor is it designed specifically for traffic spatial-temporal feature. In response to the above problems, we propose neural architecture search spatial-temporal prediction (NASST), which is a method to automatically generate a traffic spatial-temporal prediction network by performing a differentiable neural network architecture search in an optimized search space. First, we adopt a differentiable neural architecture search method to continuously relax the discrete traffic spatial-temporal prediction model architecture search, and adopt a fusion strategy of comprehensive concatenate and addition (CA) to achieve efficient neural architecture search. Second, we optimize the search space and introduce a series of classic traffic spatial-temporal feature extraction modules, which are more in line with the architectural requirements of traffic spatial-temporal prediction network. Finally, our model is validated on two public traffic datasets and achieves the best predictions. Compared with traditional manual modeling methods, our method can realize the automatic search of high-precision predictive model architectures, which improves the modeling efficiency.
Strain localization and exhumation of the lower crust: A study of the three-dimensional structure and flow kinematics of central Fiordland, New Zealand

In this thesis, I present structural and kinematic data on rock fabrics, shear zones and fault zones from the Cretaceous Malaspina orthogneiss and some of its satellite plutons in central Fiordland, New Zealand. Central Fiordland exposes a large tract of granuliteto eclogite-facies lower crust that was exhumed between late Mesozoic to Cenozoic times. The deformational structures of interest were formed and preserved during the lifecycle of a Cretaceous continental arc that involved thickening to over 60 km followed by collapse and rifting. As such, they provide an excellent opportunity to study strain localization in the deep crust and the process of exhumation. Detailed structural mapping, analysis, and the construction of a 45-kilometer cross section through the Malaspina orthogneiss and adjacent plutons reveal the spatial distribution, sequence, and kinematics of crosscutting deformational structures. The earliest structures record Cretaceous magmatism, high-grade metamorphism at the granulite and eclogite facies, and ductile flow that resulted in widespread (over 1200 km), disorganized magmatic foliations. These events were followed by regional extension that resulted in the formation of multiple, ≤0.5 km-thick ductile, upper amphibolite facies shear zones that record cooling, hydration, and horizontal flow during the Late Cretaceous. Extension continued but changed obliquity in the early to middle Tertiary and resulted in sets of strike-slip and normal brittle to semi-brittle faults forming a sinistral transtensional system. These faults are distributed across central Fiordland and crosscut and transpose the ductile shear zones and magmatic foliations. Lastly, a change in relative plate motions resulted in the inception of the Alpine fault and the development of a late Tertiary transpressional fault system that crosscuts all previous structures. The dominant factors controlling strain localization in central Fiordland changed from magma, heat, and melting, to fluid activity, plate boundary reorganization, and reactivation of inherited structures. The succession of contrasting strain localization styles in response to changing tectonic and local conditions led to the development of multiple phases of deformation. These multiple phases of deformation allowed the deep crust to be exhumed in a heterogeneous and fragmented, or ‘piecemeal’, way. In particular, the inability of late Cretaceous ductile shear zones to fully exhume the lower crust was compensated by the ability of early Tertiary transtensional faults to simultaneously thin and further exhume the lower crust. Investigations of strain localization patterns in central Fiordland shed light on the causes and mechanisms of crustal exhumation, a phenomenon that is integral to the lifecycle of virtually all orogenic belts.
Interaction Design

From the Publisher: Accomplished authors, Preece, Rogers and Sharp, have written a key new textbook on this core subject area. Interaction Design deals with a broad scope of issues, topics and paradigms that has traditionally been the scope of Human-Computer Interaction (HCI) and Interaction Design (ID). The book covers psychological and social aspects of users, interaction styles, user requirements, design approaches, usability and evaluation, traditional and future interface paradigms and the role of theory in informing design. The topics will be grounded in the design process and the aim is to present relevant issues in an integrated and coherent way, rather than assembling a collection of chapters on individual HCI topics. KEY FEATURES: · This truly integrated approach to HCI provides students with background information from psychology, sociology, anthropology, information systems and computer science · Provides principles and skills for designing any technology through the use of many interesting and state of the art examples · The author supported, highly interactive Web Site provides resources that allow students to collaborate on experiments, participate in design competitions, collaborate on design, find resources and communicate with others · The accompanying Web Site also features examples, step-by-step exercises and templates for questionnaires CONTENTS: Preface 1. What is interaction design? Interview with Gitta Saloman 2. Understanding and conceptualizing interaction Interview with Terry Winograd 3. Understanding users 4. Understanding and designing for collaboration and communication Interview with AbigailSellen 5. Understanding how interfaces affect users 6 The process of interaction design Interview with Gillian Crampton Smith 7 Identifying needs and establishing requirements Interview with Suzanne Robertson 8 Design, prototyping and construction 9 User-centered approaches to interaction design Interview with Karen Holtzblatt 10 Introducing evaluation 11 A framework for evaluation 12 Observing users Interview with Sara Bly 13 Asking users and experts Interview with Jakob Nielsen 14 Testing and modeling users Interview with Ben Shneiderman 15 Doing design and evaluation in the real world: communicators and advisory systems Epilogue Glossary