nils-herrmann/clean_scirepeval huggingface dataset:
The Lifecycle and Cascade of WeChat Social Messaging Groups

Social instant messaging services are emerging as a transformative form with which people connect, communicate with friends in their daily life — they catalyze the formation of social groups, and they bring people stronger sense of community and connection. However, research community still knows little about the formation and evolution of groups in the context of social messaging — their lifecycles, the change in their underlying structures over time, and the diffusion processes by which they develop new members. In this paper, we analyze the daily usage logs from WeChat group messaging platform — the largest standalone messaging communication service in China — with the goal of understanding the processes by which social messaging groups come together, grow new members, and evolve over time. Specifically, we discover a strong dichotomy among groups in terms of their lifecycle, and develop a separability model by taking into account a broad range of group-level features, showing that long-term and shortterm groups are inherently distinct. We also found that the lifecycle of messaging groups is largely dependent on their social roles and functions in users’ daily social experiences and specific purposes. Given the strong separability between the long-term and short-term groups, we further address the problem concerning the early prediction of successful communities. In addition to modeling the growth and evolution from grouplevel perspective, we investigate the individual-level attributes of group members and study the diffusion process by which groups gain new members. By considering members’ historical engagement behavior as well as the local social network structure that they embedded in, we develop a membership cascade model and demonstrate the effectiveness by achieving AUC of 95.31% in predicting inviter, and an AUC of 98.66% in predicting invitee.
RE-WOCHAT : Workshop on Collecting and Generating Resources for Chatbots and Conversational Agents-Development and Evaluation Workshop Programme ( May 28

This paper presents a dataset collected from natural dialogs which enables to test the ability of dialog systems to learn new facts from user utterances throughout the dialog. This interactive learning will help with one of the most prevailing problems of open domain dialog system, which is the sparsity of facts a dialog system can reason about. The proposed dataset, consisting of 1900 collected dialogs, allows simulation of an interactive gaining of denotations and questions explanations from users which can be used for the interactive learning.
Social-Aware Stateless Routingin Pocket Switched Networks

Existing social-aware routing protocols for packet switched networks make use of the information about the social structure of the network deduced by state information of nodes (e.g., history of past encounters) to optimize routing. Although these approaches are shown to have superior performance to social-oblivious, stateless routing protocols (BinarySW, Epidemic), the improvement comes at the cost of considerable storage overhead required on the nodes. In this paper we present SANE, the first routing mechanism that combines the advantages of both social-aware and stateless approaches. SANE is based on the observation - that we validate on a real-world trace - that individuals with similar interests tend to meet more often. In SANE, individuals (network members) are characterized by their interest profile, a compact representation of their interests. By implementing a simple routing rule based on interest profile similarity, SANE is free of network state information, thus overcoming the storage capacity problem with existing social-aware approaches. Through thorough experiments, we show the superiority of SANE over existing approaches, both stateful, social-aware and stateless, social-oblivious. We discuss the statelessness of our approach in the supplementary file, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPDS.2014.2307857, of this manuscript. Our interest-based approach easily enables innovative networking services, such as interest-casting. An interest-casting protocol is also introduced in this paper, and evaluated through experiments based on both real-world and synthetic mobility traces.
Tetrode recordings in the cerebellar cortex

Multi-unit recordings with tetrodes have been used in brain studies for many years, but surprisingly, scarcely in the cerebellum. The cerebellum is subdivided in multiple small functional zones. Understanding the proper features of the cerebellar computations requires a characterization of neuronal activity within each area. By allowing simultaneous recordings of neighboring cells, tetrodes provide a helpful technique to study the dynamics of the cerebellar local networks. Here, we discuss experimental configurations to optimize such recordings and demonstrate their use in the different layers of the cerebellar cortex. We show that tetrodes can also be used to perform simultaneous recordings from neighboring units in freely moving rats using a custom-made drive, thus permitting studies of cerebellar network dynamics in a large variety of behavioral conditions.
A Deflationary Account of Metaphors

Are metaphors departures from a norm of literalness? According to classical rhetoric and most later theories, including Gricean pragmatics, they are. No, metaphors are wholly normal, say the Romantic critics of classical rhetoric and a variety of modern scholars ranging from hard-nosed cognitive scientists to postmodern critical theorists. On the metaphor-as-normal side, there is a broad contrast between those, like the cognitive linguists Lakoff, Talmy or Fauconnier, who see metaphor as pervasive in language because it is constitutive of human thought, and those, like the psycholinguists Glucksberg or Kintsch, or relevance theorists, who describe metaphor as emerging in the process of verbal communication. 1 While metaphor cannot be both wholly normal and a departure from normal language use, there might be distinct, though related, metaphorical phenomena at the level of thought, on the one hand, and verbal communication, on the other. This possibility is being explored (for instance) in the work of Raymond Gibbs. 2 In this chapter, we focus on the relevance-theoretic approach to linguistic metaphors.
Performance comparison for data storage - Db4o and MySQL databases

Choice of right and appropriate database is always crucial for any information system. Since database is an integral and important part, we choose to write the performance analysis of different type of databases in context to health care data. Health care database consists of Electronic Health Records. Also, various Electronic Health Record standards like HL7, openEHR and CEN EN 13606 have been defined for relational, object and object-relational databases. So far, none of the standard has been defined using object-database. In order to do so, we must compare and analyze the performance of object-database over others. In this paper, firstly we have studied the current trends in Indian Health Care with respect to medical data storage and retrieval. Next, we have compared the performance of MySQL (relational) and Db4o (object) database in terms of persistence time and storage space for a sample hospital data of 100 users.
Nonlinear identification of triple inverted pendulum based on GA-RBF-ARX

The triple inverted pendulum is a nonlinear, unsteady and dynamic system. The traditional model is built based on mathematical modeling method which has ignored many important factors in reality. In this paper, the GA-RBF-ARX is proposed to identify a nonlinear triple inverted pendulum model based on its input/output data. RBF-ARX model is a combination of Gaussian radial basis function (RBF) neural network and Autoregressive model with exogenous input (ARX) model. It not only has the advantages of RBF neural network, such as the approximation ability, simple structure and quickly learning rate, but also has the ability of describing globally of ARX model. The structured nonlinear parameter optimization method (SNPOM) is generally used to optimize parameters of the RBF-ARX model. However, SNPOM needs to classify the parameters before optimization. It is too complex to popularize and apply in practical engineering. Due to this problem, genetic algorithm (GA) is proposed to replace SNPOM. The identification process based on GA is simpler than SNPOM. Besides, GA has good parallel design structure and characteristics of global optimization. Finally, the MATLAB simulation results show that GA-RBF-ARX identification is effective.
NetSearch: Googling large-scale network management data

In order to ensure the service quality, modern Internet Service Providers (ISPs) invest tremendously on their network monitoring and measurement infrastructure. Vast amount of network data, including device logs, alarms, and active/passive performance measurement across different network protocols and layers, are collected and stored for analysis. As network measurement grows in scale and sophistication, it becomes increasingly challenging to effectively “search” for the relevant information that best support the needs of network operations. In this paper, we look into techniques that have been widely applied in the information retrieval and search engine domain and explore their applicability in network management domain. We observe that unlike the textural information on the Internet, network data are typically annotated with time and location information, which can be further augmented using information based on network topology, protocol and service dependency. We design NetSearch, a system that pre-processes various network data sources on data ingestion, constructs index that matches both the network spatial hierarchy model and the inherent timing/textual information contained in the data, and efficiently retrieves the relevant information that network operators search for. Through case study, we demonstrate that NetSearch is an important capability for many critical network management functions such as complex impact analysis.
Automatic translation UML activity diagrams to Petri net

Provides a method for automatic translation UML diagrams to Petri nets, which is to convert formats like structure .xmi and .cpn. We consider the transformation of the most frequently used items on activity diagram - state action, condition, fork and join based on rules transformation. These elements are shown in activity diagram and its corresponding Petri net. It is noted that in active diagram presence four types elements - state action, a pseudostate, final state and transition, in Petri nets involved three types elements - place, transition and arc. Discussed in detail the comparison of initial state, state action and final state of activity diagram and places Petri nets - element name and its properties.
Cultural factors and the International Space Station.

The American and Russian/Soviet space programs independently uncovered psychosocial risks inherent in long-duration space missions. Now that these two countries are working together on the International Space Station (ISS), American-Russian cultural differences pose an additional set of risk factors. These may echo cultural differences that have been observed in the general population of the two countries and in space analogue settings, but little is known about how relevant these are to the select population of space program personnel. The evidence for the existence of mission-relevant cultural differences is reviewed and includes cultural values, emotional expressivity, personal space norms, and personality characteristics. The review is focused primarily on Russia and the United States, but also includes other ISS partner countries. Cultural differences among space program personnel may have a wide range of effects. Moreover, culture-related strains may increase the probability of distress and impairment. Such factors could affect the individual and interpersonal functioning of both crewmembers and mission control personnel, whose performance is also critical for mission safety and success. Examples from the anecdotal and empirical literature are given to illustrate these points. The use of existing assessment strategies runs the risk of overlooking important early warning signs of behavioral health difficulties. By paying more attention to cultural differences and how they might be manifested, we are more likely to detect problems early while they are still mild and resolvable.
CAViAR: Context Aware Visual Indoor Augmented Reality for a University Campus

CAViAR is a mobile software system for indoor environments that provides to the mobile user equipped with a smartphone indoor localization, augmented reality (AR), visual interaction, and indoor navigation. These capabilities are possible with the availability of state of the art AR technologies. The mobile application includes additional features, such as indoor maps, shortest path, inertial navigation, places of interest, location sharing and voice-commanded search. CAViAR was tested in a University Campus as one of the technologies to be used later in an intelligent Campus environment.
Surgical treatment of metopic synostosis.

Metopic synostosis is characterized by keel-shaped forehead (trigonocephaly), prominent midline ridge of the forehead, bitemporal narrowing, bilateral retrusion of supraorbits, egg-shaped orbits, low nasal dorsum, and reduced volume of the anterior cranial fossa. The mainstay treatment is early surgical intervention before the age of 12 months, which usually consists of bifrontal craniotomy with bilateral recontouring, lateral advancement, and lateral displacement of the superior orbital rims. Here, we have developed a new simplified technique for surgical treatment of trigonocephaly. A total of 60 cases of trigonocephaly were operated on between January 1995 and January 2010 by the first author. Surgical outcomes were evaluated 6 months after surgery using postoperative photographs and clinical examination notes, and scaling was made using the Whitaker classification. The evaluation showed that 85% of them were in class I, 11.6% were in class II, and 3.3% were in class III. No case was in class IV. Only the last 10 cases received the new surgical technique, and all were in class I. Complication rate was 38.3% for all cases and was only 20% for the last 10 cases, that is, the new technique group. Revision rate for trigonocephaly surgery was 13.3%, and the most common reason was hardware removal. None of the patients from the new technique group underwent revision surgery. We believe that our new technique is fast and easy, can provide sufficient bone graft, and is more useful for older patients (>1 y). Early postoperative results have been promising.
From swiping to casual sex and/or committed relationships: Exploring the experiences of Tinder users

To better understand if and how the mechanics of the process Tinder imposes on its users (i.e., swiping, matching, and starting conversations) influences the resulting sexual or romantic interactions, we collected data from 1038 Belgian Tinder users. Our findings show that a user’s swiping quantity does not guarantee a higher number of Tinder matches, women have generally more matches than men and men usually have to start a conversation on Tinder. Moreover, while having conversations was positively associated with reporting having had offline Tinder encounters, less than half of our sample reported having had an offline meeting with another Tinder user. Whereas more than one third of those offline encounters lead to casual sex, more than a quarter of those offline encounters result in the formation of a committed relationship. Such findings indicate that Tinder is not “just a hookup app”, as often assumed in public discourse. We argue it is plausible that sexual encounters will eventually lead to committed relationships in a society where initiation of relationship formation with dating has been replaced by hooking up.
Automated soft tissue lesion detection and segmentation in digital mammography using a u-net deep learning network

Computer-aided detection or decision support systems aim to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. Commonly such methods proceed in two steps: selection of candidate regions for malignancy, and later classification as either malignant or not. In this study, we present a candidate detection method based on deep learning to automatically detect and additionally segment soft tissue lesions in DM. A database of DM exams (mostly bilateral and two views) was collected from our institutional archive. In total, 7196 DM exams (28294 DM images) acquired with systems from three different vendors (General Electric, Siemens, Hologic) were collected, of which 2883 contained malignant lesions verified with histopathology. Data was randomly split on an exam level into training (50%), validation (10%) and testing (40%) of deep neural network with u-net architecture. The u-net classifies the image but also provides lesion segmentation. Free receiver operating characteristic (FROC) analysis was used to evaluate the model, on an image and on an exam level. On an image level, a maximum sensitivity of 0.94 at 7.93 false positives (FP) per image was achieved. Similarly, per exam a maximum sensitivity of 0.98 at 7.81 FP per image was achieved. In conclusion, the method could be used as a candidate selection model with high accuracy and with the additional information of lesion segmentation.
Joint Doppler and DOA estimation using 2D MUSIC in presence of phase residual

This paper investigates the joint Doppler and DOA (Direction-of-Arrival) estimation with a wideband phased array in presence of phase residual due to range-Doppler coupling appearing in pulse-Doppler radars. 2D MUSIC algorithm is applied and a compensation approach is developed to eliminate the influence of phase residual. Simulation data validate the improvement of joint Doppler and DOA estimation performance using the proposed method.
Presence of kynurenic acid in food and honeybee products

Kynurenic acid (KYNA) is an endogenous antagonist of ionotropic glutamate receptors and the α7 nicotinic acetylcholine receptor, showing anticonvulsant and neuroprotective activity. In this study, the presence of KYNA in food and honeybee products was investigated. KYNA was found in all 37 tested samples of food and honeybee products. The highest concentration of KYNA was obtained from honeybee products’ samples, propolis (9.6 nmol/g), honey (1.0–4.8 nmol/g) and bee pollen (3.4 nmol/g). A high concentration was detected in fresh broccoli (2.2 nmol/g) and potato (0.7 nmol/g). Only traces of KYNA were found in some commercial baby products. KYNA administered intragastrically in rats was absorbed from the intestine into the blood stream and transported to the liver and to the kidney. In conclusion, we provide evidence that KYNA is a constituent of food and that it can be easily absorbed from the digestive system.
A Scalable Distributed Stream Mining System for Highway Traffic Data

To achieve the concept of smart roads, intelligent sensors are being placed on the roadways to collect real-time traffic streams. Traditional method is not a real-time response, and incurs high communication and storage costs. Existing distributed stream mining algorithms do not consider the resource limitation on the lightweight devices such as sensors. In this paper, we propose a distributed traffic stream mining system. The central server performs various data mining tasks only in the training and updating stage and sends the interesting patterns to the sensors. The sensors monitor and predict the coming traffic or raise alarms independently by comparing with the patterns observed in the historical streams. The sensors provide real-time response with less wireless communication and small resource requirement, and the computation burden on the central server is reduced. We evaluate our system on the real highway traffic streams in the GCM Transportation Corridor in Chicagoland.
Anomaly detection in trajectories

In this work, we study the problem of anomaly detection of the trajectories of objects in a visual scene. For this purpose, we propose a novel representation for trajectories utilizing covariance features. Representing trajectories via co-variance features enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances, anomaly detection is achieved by sparse representations on nearest neighbours. Conducted experiments on both synthetic and real datasets show that the proposed method yields results which are outperforming or comparable with state of the art.
The role of synovitis in osteoarthritis pathogenesis.

Research into the pathophysiology of osteoarthritis (OA) has focused on cartilage and peri-articular bone, but there is increasing recognition that OA affects all of the joint tissues, including the synovium (SM). Under normal physiological conditions the synovial lining consists of a thin layer of cells with phenotypic features of macrophages and fibroblasts. These cells and the underlying vascularized connective tissue stroma form a complex structure that is an important source of synovial fluid (SF) components that are essential for normal cartilage and joint function. The histological changes observed in the SM in OA generally include features indicative of an inflammatory "synovitis"; specifically they encompass a range of abnormalities, such as synovial lining hyperplasia, infiltration of macrophages and lymphocytes, neoangiogenesis and fibrosis. The pattern of synovial reaction varies with disease duration and associated metabolic and structural changes in other joint tissues. Imaging modalities including magnetic resonance (MRI) and ultrasound (US) have proved useful in detecting and quantifying synovial abnormalities, but individual studies have varied in their methods of evaluation. Despite these differences, most studies have concluded that the presence of synovitis in OA is associated with more severe pain and joint dysfunction. In addition, synovitis may be predictive of faster rates of cartilage loss in certain patient populations. Recent studies have provided insights into the pathogenic mechanisms underlying the development of synovitis in OA. Available evidence suggests that the inflammatory process involves engagement of Toll-like receptors and activation of the complement cascade by degradation products of extracellular matrices of cartilage and other joint tissues. The ensuing synovial reaction can lead to synthesis and release of a wide variety of cytokines and chemokines. Some of these inflammatory mediators are detected in joint tissues and SF in OA and have catabolic effects on chondrocytes. These inflammatory mediators represent potential targets for therapeutic interventions designed to reduce both symptoms and structural joint damage in OA. This article is part of a Special Issue entitled "Osteoarthritis".
A 2.4GHz mixed-signal polar power amplifier with low-power integrated filtering in 65nm CMOS

A 65nm digitally-modulated polar transmitter incorporates a fully-integrated 2.4GHz efficient switching Inverse Class D power amplifier. Low power digital filtering on the amplitude path helps remove spectral images for coexistence. The transmitter integrates the complete LO distribution network and digital drivers. Operating from a 1-V supply, the PA has 21.8dBm peak output power with 44% efficiency. Simple static predistortion helps the transmitter meet EVM and mask requirements of 802.11g 54Mbps WLAN standard with 18% average efficiency.