nils-herrmann/clean_scirepeval huggingface dataset:
A hierarchical image clustering cosegmentation framework

Given the knowledge that the same or similar objects appear in a set of images, our goal is to simultaneously segment that object from the set of images. To solve this problem, known as the cosegmentation problem, we present a method based upon hierarchical clustering. Our framework first eliminates intra-class heterogeneity in a dataset by clustering similar images together into smaller groups. Then, from each image, our method extracts multiple levels of segmentation and creates connections between regions (e.g. superpixel) across levels to establish intra-image multi-scale constraints. Next we take advantage of the information available from other images in our group. We design and present an efficient method to create inter-image relationships, e.g. connections between image regions from one image to all other images in an image cluster. Given the intra & inter-image connections, we perform a segmentation of the group of images into foreground and background regions. Finally, we compare our segmentation accuracy to several other state-of-the-art segmentation methods on standard datasets, and also demonstrate the robustness of our method on real world data.
Schema matching and mapping: from usage to evaluation

This tutorial provides an overview of current evaluation techniques for schema matching and mapping tasks and tools, alongside existing and broadly used evaluation scenarios. The objective is to introduce the audience into the area of matching and mapping system evaluation, and to highlight the need for leveraging robust benchmarks and yardsticks for the comparison of the different matching and mapping tasks. Open research problems will be identified and presented. The tutorial is for both experienced researchers and unfamiliar investigators looking for a quick and complete introduction to the topic.
Scaling Up: Distributed Machine Learning with Cooperation

Machine-learning methods are becoming increasingly popular for automated data analysis. However, standard methods do not scale up to massive scientific and business data sets without expensive hardware. This paper investigates a practical alternative for scaling up: the use of distributed processing to take advantage of the often dormant PCs and workstations available on local networks. Each workstation runs a common rule-learning program on a subset of the data. We first show that for commonly used rule evaluation criteria, a simple form of cooperation can guarantee that a rule will look good to the set of cooperating learners if and only if it would look good to a single learner operating with the entire data set. We then show how such a system can further capitalize on different perspectives by sharing learned knowledge for significant reduction in search effort. We demonstrate the power of the method by learning from a massive data set taken from the domain of cellular fraud detection. Finally, we provide an overview of other methods for scaling up machine learning.
Impending skin necrosis after dermal filler injection: A "golden time" for first-aid intervention.

Vascular compromise with impending skin necrosis is one of the most serious potential complications. Early recognition of vascular occlusion and swift and aggressive treatment are required to avoid any irreversible changes. However, initial symptoms of a vascular event are often dismissed as simple post-procedural discomfort. If more than 3 days pass after filler injection, crust formation is initiated over the erythematous base along with a rim of fibrous tissue. Due to the replacement of normal tissues by fibrous material, the healing process may result in scar formation in spite of debridement and aggressive dressing changes. Scars often cause contracture and subsequent cosmetic disfigurement, which results in a traumatic burden to the patient. By sharing our experience of the patients with filler induced skin necrosis, we suggest that treatment should be initiated no later than 3 days after the procedure.
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360 ^\circ ∘ Panoramic Imagery

Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360◦ panoramic cameras. We present an approach to adapt contemporary deep network architectures developed on conventional rectilinear imagery to work on equirectangular 360◦ panoramic imagery. To address the lack of annotated panoramic automotive datasets availability, we adapt a contemporary automotive dataset, via style and projection transformations, to facilitate the cross-domain retraining of contemporary algorithms for panoramic imagery. Following this approach we retrain and adapt existing architectures to recover scene depth and 3D pose of vehicles from monocular panoramic imagery without any panoramic training labels or calibration parameters. Our approach is evaluated qualitatively on crowd-sourced panoramic images and quantitatively using an automotive environment simulator to provide the first benchmark for such techniques within panoramic imagery.
Efficient and Available In-Memory KV-Store with Hybrid Erasure Coding and Replication

In-memory key/value store (KV-store) is a key building block for many systems like databases and large websites. Two key requirements for such systems are efficiency and availability, which demand a KV-store to continuously handle millions of requests per second. A common approach to availability is using replication, such as primary-backup (PBR), which, however, requires M+1 times memory to tolerate M failures. This renders scarce memory unable to handle useful user jobs.
 This article makes the first case of building highly available in-memory KV-store by integrating erasure coding to achieve memory efficiency, while not notably degrading performance. A main challenge is that an in-memory KV-store has much scattered metadata. A single KV put may cause excessive coding operations and parity updates due to excessive small updates to metadata. Our approach, namely Cocytus, addresses this challenge by using a hybrid scheme that leverages PBR for small-sized and scattered data (e.g., metadata and key), while only applying erasure coding to relatively large data (e.g., value). To mitigate well-known issues like lengthy recovery of erasure coding, Cocytus uses an online recovery scheme by leveraging the replicated metadata information to continuously serve KV requests. To further demonstrate the usefulness of Cocytus, we have built a transaction layer by using Cocytus as a fast and reliable storage layer to store database records and transaction logs. We have integrated the design of Cocytus to Memcached and extend it to support in-memory transactions. Evaluation using YCSB with different KV configurations shows that Cocytus incurs low overhead for latency and throughput, can tolerate node failures with fast online recovery, while saving 33% to 46% memory compared to PBR when tolerating two failures. A further evaluation using the SmallBank OLTP benchmark shows that in-memory transactions can run atop Cocytus with high throughput, low latency, and low abort rate and recover fast from consecutive failures.
DataMPI: Extending MPI to Hadoop-Like Big Data Computing

MPI has been widely used in High Performance Computing. In contrast, such efficient communication support is lacking in the field of Big Data Computing, where communication is realized by time consuming techniques such as HTTP/RPC. This paper takes a step in bridging these two fields by extending MPI to support Hadoop-like Big Data Computing jobs, where processing and communication of a large number of key-value pair instances are needed through distributed computation models such as MapReduce, Iteration, and Streaming. We abstract the characteristics of key-value communication patterns into a bipartite communication model, which reveals four distinctions from MPI: Dichotomic, Dynamic, Data-centric, and Diversified features. Utilizing this model, we propose the specification of a minimalistic extension to MPI. An open source communication library, DataMPI, is developed to implement this specification. Performance experiments show that DataMPI has significant advantages in performance and flexibility, while maintaining high productivity, scalability, and fault tolerance of Hadoop.
SPOT: a smart personalized office thermal control system

Heating, Ventilation, and Air Conditioning (HVAC) accounts for about half of the energy consumption in buildings. HVAC energy consumption can be reduced by changing the indoor air temperature setpoint, but changing the setpoint too aggressively can overly reduce user comfort. We have therefore designed and implemented SPOT: a Smart Personalized Office Thermal control system that balances energy conservation with personal thermal comfort in an office environment. SPOT relies on a new model for personal thermal comfort that we call the Predicted Personal Vote model. This model quantitatively predicts human comfort based on a set of underlying measurable environmental and personal parameters. SPOT uses a set of sensors, including a Microsoft Kinect, to measure the parameters underlying the PPV model, then controls heating and cooling elements to dynamically adjust indoor temperature to maintain comfort. Based on a deployment of SPOT in a real office environment, we find that SPOT can accurately maintain personal comfort despite environmental fluctuations and allows a worker to balance personal comfort with energy use.
Psychological, cognitive, and personal variables that predict college academic achievement among health sciences students.

There are many factors that affect college academic achievement among health sciences students.



The aim of this study was to examine selected psychological, cognitive, and personal variables that affect students' academic achievement among health sciences college students in Saudi Arabia.



A correlational descriptive cross-sectional design was employed to collect data on the studied variables from 510 health sciences students (Medicine, Nursing, Respiratory Therapy, and Pharmacy Doctor) employing self-administered questionnaire.



Results showed that students experienced low level of self-esteem and low level of student-faculty interaction; and high level of achievement motivation and satisfaction with life. Also, they reported mild levels of depression and stress and a moderate level of anxiety. Female students reported higher level of achievement motivation, depression, anxiety, and stress; while male students reported a higher level of self-esteem. Results also showed that achievement motivation, mothers' educational level, working besides studying, gender, aptitude test score, and depression level were the best predictors of academic achievement and accounting for 43% of the total variance.



Several psychological, cognitive, and personal variables were found to affect college academic achievement among health sciences students. Recommendations and implications to enhance students' academic achievement are discussed.
Discovering Interacting Artifacts from ERP Systems

Enterprise Resource Planning (ERP) systems are widely used to manage business documents along a business processes and allow very detailed recording of event data of past process executions and involved documents. This recorded event data is the basis for auditing and detecting unusual flows. Process mining techniques can analyze event data of processes stored in linear event logs to discover a process model that reveals unusual executions. Existing approaches to obtain linear event logs from ERP data require a single case identifier to which all behavior can be related. However, in ERP systems processes such as Order to Cash operate on multiple interrelated business objects, each having their own case identifier, their own behavior, and interact with each other. Forcing these into a single case creates ambiguous dependencies caused by data convergence and divergence which obscures unusual flows in the resulting process model. In this paper, we present a new semi-automatic, end-to-end approach for analyzing event data in a plain database of an ERP system for unusual executions. More precisely, we identify an artifact-centric process model describing the business objects, their life-cycles, and how the various objects interact along their life-cycles. This way, we prevent data divergence and convergence. We report on two case studies where our approach allowed to successfully analyze processes of ERP systems and reliably revealed unusual flows later confirmed by domain experts.
Human immune responses to Plasmodium falciparum infection: molecular evidence for a suboptimal THαβ and TH17 bias over ideal and effective traditional TH1 immune response

Using microarray analysis, this study showed up-regulation of toll-like receptors 1, 2, 4, 7, 8, NF-κB, TNF, p38-MAPK, and MHC molecules in human peripheral blood mononuclear cells following infection with Plasmodium falciparum. This analysis reports herein further studies based on time-course microarray analysis with focus on malaria-induced host immune response. The results show that in early malaria, selected immune response-related genes were up-regulated including α β and γ interferon-related genes, as well as genes of IL-15, CD36, chemokines (CXCL10, CCL2, S100A8/9, CXCL9, and CXCL11), TRAIL and IgG Fc receptors. During acute febrile malaria, up-regulated genes included α β and γ interferon-related genes, IL-8, IL-1b IL-10 downstream genes, TGFB1, oncostatin-M, chemokines, IgG Fc receptors, ADCC signalling, complement-related genes, granzymes, NK cell killer/inhibitory receptors and Fas antigen. During recovery, genes for NK receptorsand granzymes/perforin were up-regulated. When viewed in terms of immune response type, malaria infection appeared to induce a mixed TH1 response, in which α and β interferon-driven responses appear to predominate over the more classic IL-12 driven pathway. In addition, TH17 pathway also appears to play a significant role in the immune response to P. falciparum. Gene markers of TH17 (neutrophil-related genes, TGFB1 and IL-6 family (oncostatin-M)) and THαβ (IFN-γ and NK cytotoxicity and ADCC gene) immune response were up-regulated. Initiation of THαβ immune response was associated with an IFN-αβ response, which ultimately resulted in moderate-mild IFN-γ achieved via a pathway different from the more classic IL-12 TH1 pattern. Based on these observations, this study speculates that in P. falciparum infection, THαβ/TH17 immune response may predominate over ideal TH1 response.
From social integration to health : Durkheim in the new millennium p

It is widely recognized that social relationships and aliation have powerful eects on physical and mental health. When investigators write about the impact of social relationships on health, many terms are used loosely and interchangeably including social networks, social ties and social integration. The aim of this paper is to clarify these terms using a single framework. We discuss: (1) theoretical orientations from diverse disciplines which we believe are fundamental to advancing research in this area; (2) a set of de®nitions accompanied by major assessment tools; and (3) an overarching model which integrates multilevel phenomena. Theoretical orientations that we draw upon were developed by Durkheim whose work on social integration and suicide are seminal and John Bowlby, a psychiatrist who developed attachment theory in relation to child development and contemporary social network theorists. We present a conceptual model of how social networks impact health. We envision a cascading causal process beginning with the macro-social to psychobiological processes that are dynamically linked together to form the processes by which social integration eects health. We start by embedding social networks in a larger social and cultural context in which upstream forces are seen to condition network structure. Serious consideration of the larger macro-social context in which networks form and are sustained has been lacking in all but a small number of studies and is almost completely absent in studies of social network in ̄uences on health. We then move downstream to understand the in ̄uences network structure and function have on social and interpersonal behavior. We argue that networks operate at the behavioral level through four primary pathways: (1) provision of social support; (2) social in ̄uence; (3) on social engagement and attachment; and (4) access to resources and material goods. 7 2000 Elsevier Science Ltd. All rights reserved.
Game-Theoretic Learning in Distributed Control

In distributed architecture control problems, there is a collection of interconnected decision making components that seek to realize desirable collective behaviors through local interactions and by processing local information. Applications range from autonomous vehicles to energy to transportation. One approach to control of such distributed architectures is to view the components as players in a game. In this approach, two design considerations are the components’ incentives and the rules that dictate how components react to the decisions of other components. In game theoretic language, the incentives are defined through utility functions and the reaction rules are online learning dynamics. This chapter presents an overview of this approach, covering basic concepts in game theory, special game classes, measures of distributed efficiency, utility design, and online learning rules, all with the interpretation of using game theory as a prescriptive paradigm for distributed control design.
MT-ABAC: A Multi-Tenant Attribute-Based Access Control Model with Tenant Trust

A major barrier to the adoption of cloud Infrastructure-as-aService (IaaS) is collaboration, where multiple tenants engage in collaborative tasks requiring resources to be shared across tenant boundaries. Currently, cloud IaaS providers focus on multi-tenant isolation, and offer limited or no cross-tenant access capabilities in their IaaS APIs. In this paper, we present a novel attribute-based access control (ABAC) model to enable collaboration between tenants in a cloud IaaS, as well as more generally. Our approach allows cross-tenant attribute assignment to provide access to shared resources across tenants. Particularly, our tenanttrust authorizes a trustee tenant to assign its attributes to users from a trustor tenant, enabling access to the trustee tenant’s resources. We designate our multi-tenant attribute-based access control model as MTABAC. Previously, a multi-tenant role-based access control (MT-RBAC) model has been defined in the literature wherein a trustee tenant can assign its roles to users from a trustor tenant. We demonstrate that MTABAC can be configured to enforce MT-RBAC thus subsuming it as a
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings

Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is underexplored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.
NixOS: a purely functional Linux distribution

Existing package and system configuration management tools suffer from an imperative model, where system administration actions such as upgrading packages or changes to system configuration files are stateful: they destructively update the state of the system. This leads to many problems, such as the inability to roll back changes easily, to run multiple versions of a package side-by-side, to reproduce a configuration deterministically on another machine, or to reliably upgrade a system. In this paper we show that we can overcome these problems by moving to a purely functional system configuration model. This means that all static parts of a system (such as software packages, configuration files and system startup scripts) are built by pure functions and are immutable, stored in a way analogously to a heap in a purely function language. We have implemented this model in NixOS, a non-trivial Linux distribution that uses the Nix package manager to build the entire system configuration from a purely functional specification.
A Mobile Manipulator

This paper describes a mobile manipulator that uses its wheels for manipulation as well as locomotion. This robot, named themobipulator , looks like a small car with four independently powered wheels, none of them steered. It is designed to manipulate paper and other objects on the surface of a desk. The wheels are used for locomotion or for manipulation, switching functions dynamically as the task demands. So far we have preliminary demonstrations of a variety of motions, and performance data for the task of moving a sheet of paper in a square while maintaining constant orientation.
The ZX calculus is a language for surface code lattice surgery

Quantum computing is moving rapidly to the point of deployment of technology. Functional quantum devices will require the ability to correct error in order to be scalable and effective. A leading choice of error correction, in particular for modular or distributed architectures, is the surface code with logical two-qubit operations realised via “lattice surgery”. These operations consist of “merges” and “splits” acting non-unitarily on the logical states and are not easily captured by standard circuit notation. This raises the question of how best to reason about lattice surgery in order efficiently to use quantum states and operations in architectures with complex resource management issues. In this paper we demonstrate that the operations of the ZX calculus, a form of quantum diagrammatic reasoning designed using category theory, match exactly the operations of lattice surgery. Red and green “spider” nodes match rough and smooth merges and splits, and follow the axioms of a dagger special associative Frobenius algebra. Some lattice surgery operations can require non-trivial correction operations, which are captured natively in the use of the ZX calculus in the form of ensembles of diagrams. We give a first taste of the power of the calculus as a language for surgery by considering two operations (magic state use and producing a CNOT ) and show how ZX diagram re-write rules give lattice surgery procedures for these operations that are novel, efficient, and highly configurable.