nils-herrmann/clean_scirepeval huggingface dataset:
Altered Functional Network Affects Amyloid and Structural Covariance in Alzheimer's Disease
Venue: BioMed research international
Year: 2018

We aimed to investigate how altered intrinsic connectivity networks (ICNs) affect pathologic changes of Alzheimer's disease (AD) at a network based level.  Thirty normal controls (NCs) 23 patients with AD mild cognitive impairment (MCI) and 20 patients with AD dementia were enrolled. We compared the organization of grey matter structural covariance and functional connectivity in ICNs between NCs and all AD patients who were amyloid b (Ab) positive. We further used seed based interregional covariance analysis to compare structural and Ab plaque covariance in default mode network (DMN) between AD MCI and AD dementia groups.  The patients with AD had increased functional interregional covariance among the regions of the ICN anchored to dorsal caudate (DC) seeds compared to the NCs. The increased connectivity was associated with extended patterns of reduced Ab plaque covariance in the AD dementia group compared to the AD MCI group within the striatal network anchored to DC seeds. Patterns of lower Ab plaque covariance in the AD dementia group compared to the AD MCI group were more extended within the network anchored to DC seeds than within the DMN, which was undergoing functional failure in the patients with AD. Significant decreased structural covariance in the AD dementia group compared to the AD MCI group was more extended in the DMN during functional failure.  Functional connectivity in ICNs affects the topographic spread of molecular pathologies. The temporal trajectory of pathologic alterations can be well demonstrated by pathologic covariance comparisons between different clinical stages. Pathologic covariance can provide critical support to pathologic interactions at network and molecular levels.
Context aware Attentional Pooling (CAP) for Fine grained Visual Classification
Venue: AAAI
Year: 2021

Deep convolutional neural networks (CNNs) have shown a strong ability in mining discriminative object pose and parts information for image recognition. For fine grained recognition, context aware rich feature representation of object/scene plays a key role since it exhibits a significant variance in the same subcategory and subtle variance among different subcategories. Finding the subtle variance that fully characterizes the object/scene is not straightforward. To address this, we propose a novel context aware attentional pooling (CAP) that effectively captures subtle changes via sub pixel gradients, and learns to attend informative integral regions and their importance in discriminating different subcategories without requiring the bounding box and/or distinguishable part annotations. We also introduce a novel feature encoding by considering the intrinsic consistency between the informativeness of the integral regions and their spatial structures to capture the semantic correlation among them. Our approach is simple yet extremely effective and can be easily applied on top of a standard classification backbone network. We evaluate our approach using six state of the art (SotA) backbone networks and eight benchmark datasets. Our method significantly outperforms the SotA approaches on six datasets and is very competitive with the remaining two.
TVPRNN for image caption generation
Year: 2017

Image caption generation has attracted considerable interest in computer vision and natural language processes. However, existing methods usually use convolution neural network (CNN) for extracting image feature and recurrent NN (RNN) to predict next produced word, which may make the obtained features unadaptable to the word generated at current time. A new time varying parallel RNN (TVPRNN) to deal with this task is proposed. TVPRNN uses two classical CNNs (i.e. VggNet and inception v3) for extracting global image feature, jointly with RNN to obtain time varying feature at each time step, which are used for representing current word. Visual and textual representation in a multimodal space is fused. Moreover, visual attention mechanism is introduced to guild the proposed network. The approach is evaluated using the benchmark Flickr8k, Flickr30k and MSCOCO datasets. The experimental results show that TVPRNN achieves better or on par with the state of the art methods.
Policy Evaluation Networks
Venue: ArXiv
Year: 2020

Many reinforcement learning algorithms use value functions to guide the search for better policies. These methods estimate the value of a single policy while generalizing across many states. The core idea of this paper is to flip this convention and estimate the value of many policies, for a single set of states. This approach opens up the possibility of performing direct gradient ascent in policy space without seeing any new data. The main challenge for this approach is finding a way to represent complex policies that facilitates learning and generalization. To address this problem, we introduce a scalable, differentiable fingerprinting mechanism that retains essential policy information in a concise embedding. Our empirical results demonstrate that combining these three elements (learned Policy Evaluation Network, policy fingerprints, gradient ascent) can produce policies that outperform those that generated the training data, in zero shot manner.
A Robust Algorithm for Long term Object Tracking Based on PTAV
Venue: 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
Year: 2019

Long term object tracking is one of the most challenging problems in computer vision due to various factors such as deformation, abrupt motion, heavy occlusion and out of view. In this paper, we propose a tracking method based on Parallel Tracking and Verifying (PTAV) Firstly, we replace the fDSST tracker with a better performance tracker ECO HC. Then we add self test mechanism for the tracker, which include backtracking check using forward backward overlap rate and multi peak detection mechanism. At last, we modify the parallel framework between the tracker and the verifier in the PTAV, so that the algorithm can get timey feedback about the abnormal information. We perform experiments on the benchmark OTB 2015. Results show that our method has better accuracy and robustness in case of occlusion, out of view and other interference factors.
Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
Year: 2020

Error Correction Schemes (ECSs) significantly contribute to enhancing reliability and energy efficiency of Wireless Sensor Networks (WSNs) This review paper offers an overview of the different types of ECS used in communication systems and a synopsis of the standards for WSN. We also discuss channels and network models for WSN as they are crucial for efficient ECS design and implementation. The literature review conducted on the proposed energy consumption and efficiency models for WSN indicates that existing research work has not considered Single Hop Asymmetric Structure (SHAS) with high performing Error Correcting Codes (ECCs) We present a review on proposed ECS for WSN based on three criteria: Forward Error Correction (FEC) adaptive error correction techniques, and other techniques. Based on our review work, we found that there are limited works on ECS design on a realistic network model i.e. a modified multi hop WSN model. Finally, we offer future research challenges and opportunities on ECS design and implementation for WSN.
The Relative Importance of Values, Social Norms, and Enjoyment Based Motivation in Explaining Pro Environmental Product Purchasing Behavior in Apparel Domain
Year: 2020

Changing consumption behavior can offer co benefits in reduction of environmental issues and encouraging improvements to environmentally friendly or sustainable production. We propose a novel value social norm enjoyment based motivation (VSE) model and test the factors that influence individual pro environmental apparel purchasing behavior. Data were obtained from 353 college students in Korea and analyzed by using SEM. Our results show that individuals who endorse bio altruistic values who engage in eco friendly environmental behavior in apparel domain are influenced by descriptive norms and injunctive norms. Further, enjoyment based motivation was found to be a key mediator among bio altruistic value, descriptive norms, and injunctive norms on pro environmental purchasing behavior. However, injunctive norms do not directly influence purchasing behavior, but rather, are integrated to enjoyment based intrinsic motivation, then indirectly affect purchasing behavior.
Ethnic minority businesses and immigrant entrepreneurship in Greece
Year: 2010

The present paper aims to report and analyse empirical data from 391 ethnic minority entrepreneurs and 132 of their family members, from eight different ethnic communities of Albanians, Armenians, Bulgarians, Chinese, Georgians, Indians, Nigerians and Russians in Greece. The paper presents empirical data from face to face interview type research conducted between January 2006 and August 2008 in the regions of Attica and Central Macedonia, Greece. The selection criteria used for the inclusion of enterprises in the ethnic minority business (EMB) database were: ethnicity of the business owner; sectors, including traditional sectors and emerging sectors of EMB; and finally location, i.e. in the regions of Attica and Central Macedonia. The findings of this research point to the fact that the three theories of "block mobility" "opportunity structures" and "ethnic resources" complement each other in explaining the process of starting up an ethnic minority busines.
Distant from input: Evidence of regions within the default mode network supporting perceptually decoupled and conceptually guided cognition
Venue: NeuroImage
Year: 2018

&NA; The default mode network supports a variety of mental operations such as semantic processing, episodic memory retrieval, mental time travel and mind wandering, yet the commonalities between these functions remains unclear. One possibility is that this system supports cognition that is independent of the immediate environment; alternatively or additionally, it might support higher order conceptual representations that draw together multiple features. We tested these accounts using a novel paradigm that separately manipulated the availability of perceptual information to guide decision making and the representational complexity of this information. Using task based imaging we established regions that respond when cognition combines both stimulus independence with multi modal information. These included left and right angular gyri and the left middle temporal gyrus. Although these sites were within the default mode network, they showed a stronger response to demanding memory judgements than to an easier perceptual task, contrary to the view that they support automatic aspects of cognition. In a subsequent analysis, we showed that these regions were located at the extreme end of a macroscale gradient, which describes gradual transitions from sensorimotor to transmodal cortex. This shift in the focus of neural activity towards transmodal, default mode, regions might reflect a process of where the functional distance from specific sensory enables conceptually rich and detailed cognitive states to be generated in the absence of input. HighlightsBrain regions supporting meaning overlap with stimulus independence.Bilateral angular gyri and left MTG respond strongly to both features of cognition.These patterns reflect a shift in activity towards regions of transmodal cortex.Complex memory representations may emerge in cortical areas distant from input.
Improvements on Fast Motion Estimation Strategy for H.264/AVC
Venue: IEEE Transactions on Circuits and Systems for Video Technology
Year: 2008

This paper analyzes the statistical characters of rate distortion performances of the fast motion estimation (ME) algorithms in H.264/AVC and introduces an additional early termination scheme with adaptive thresholds for UMHS and CBFPS. This keeps the R D performance of the original UMHS and CBFPS, while reducing the computational complexity of encoding process considerably. Simulation results in different conditions (image sizes from QCIF to HD and various quantization parameters) are shown. The proposed method is suitable on other video coding platforms and could be integrated with ease. Partial methods in this paper have been adopted into the JM software and the JVT test model.
An investigation of mental health status of children and adolescents in china during the outbreak of COVID 19
Venue: Journal of Affective Disorders
Year: 2020

The sudden outbreak of Coronavirus Disease 2019 (COVID 19) has had a dramatic effect on the mental health of the public. In the present study, we demonstrated the psychological effects on children and adolescents associated with the epidemic Methods By using convenience sampling method, questionnaires, such as Spence Child Anxiety Scale, Child Depression Inventory and Coping style Scale, were distributed to participating 359 children and 3254 adolescents online.  The anxiety levels of children and adolescents were (23.87+ 15.79) and (29.27+ 19.79) respectively. 22.28% respondents were suffering from depressive symptoms. Seven significant factors associated with increased levels of anxiety, including female, resident in urban regions, emotion focused coping style. Nine factors associated with increased levels of depression, such as smartphone addiction (OR 1.411, 95% CI 1.099 1.180) Internet addiction (OR 1.844, 95% CI 1.209 2.811) and resident in Hubei province (OR 3.107, 95% CI 1.252 7.708) Two additional factors associated with decreased levels of depressive symptoms: hours spend on Internet per day before the epidemic (OR 0.652, 95% CI 0.609 0.697) and tendency to apply problem focused coping style (OR 0.937, 95% CI 0.923 0.951)  Our findings indicate that the COVID 19 outbreak has had a significant psychosocial impact on children and adolescents. Findings of current levels of anxiety and depression not only highlight the need to address emotional distress for children and adolescents during the epidemic but also provide researchers with scientific fundamentals to formulate targeted interventions based on the significant influencing factors.
Willingness to pay for ecological function regions protection based on a choice experiment method: a case study of the Shiwandashan nature reserve
Year: 2019

The ecological function regions have enormous environmental and social value, and its protection and sustainable development need to sacrifice local economic development and local people's land development rights and interests. Therefore, it is important to study the willingness to pay for ecological protection in the ecological function region to calculate the amount of ecological compensation. Here, we conducted a choice experiment method and MNL model to explore this issue. The obtained results show that: 1. Respondents' willingness to pay for the attribute of environmental quality is strongest, followed by the area of the nature reserve, while the emphasis on development intensity is relatively weak in Model 1 and Model 2. 2. Education level (5.097) number of family members 0.429) industry (0.135) annual household income (0.089) and age (0.032) all significantly influence the willingness to pay. 3. According to the results of the CE method, respondents are willing to pay about CNY 441.66 for ecological protection per year.
Breathing at a rate of 5.5 breaths per minute with equal inhalation to exhalation ratio increases heart rate variability.
Venue: International journal of psychophysiology official journal of the International Organization of Psychophysiology
Year: 2014

Prior studies have found that a breathing pattern of 6 or 5.5 breaths per minute (bpm) was associated with greater heart rate variability (HRV) than that of spontaneous breathing rate. However, the effects of combining the breathing rate with the inhalation to exhalation ratio (I:E ratio) on HRV indices are inconsistent. This study aimed to examine the differences in HRV indices and subjective feelings of anxiety and relaxation among four different breathing patterns.  Forty seven healthy college students were recruited for the study, and a Latin square experimental design with a counterbalance in random sequences was applied. Participants were instructed to breathe at two different breathing rates (6 and 5.5 breaths) and two different I:E ratios (5:5 and 4:6) The HRV indices as well as anxiety and relaxation levels were measured at baseline (spontaneous breathing) and for the four different breathing patterns.  The results revealed that a pattern of 5.5 bpm with an I:E ratio of 5:5 produced a higher NN interval standard deviation and higher low frequency power than the other breathing patterns. Moreover, the four different breathing patterns were associated with significantly increased feeling of relaxation compared with baseline.  The study confirmed that a breathing pattern of 5.5 bpm with an I:E ratio of 5:5 achieved greater HRV than the other breathing patterns. This finding can be applied to HRV biofeedback or breathing training in the future.
Authentic leadership, knowledge sharing, and employees' creativity
Year: 2019

This paper aims to analyze the influence of authentic leadership on knowledge sharing and employee's creativity through the mediating role of team environment of psychological safety and trust.,The participants in this study were 60 team leaders and 300 team members' working in different Overseas Employment Promoters Agencies (OEPA) at Islamabad region, Pakistan. In addition to the authentic leadership of their supervisors, employees reported their perception of being psychologically safe and in the environment of team trust at work, whereas supervisors rated employee's creativity at work areas. Confirmatory factor analysis (CFA) was carried out using structural equation modelling (SEM) for validation of results among the variables.,The major findings of the study were (a) authentic leadership positively predicted knowledge sharing and employees' creativity through the mediator's role of team environment (psychological safety and team environment of trust) and (b) team environment of psychological safety and team environment of trust affected both the knowledge sharing and employee's creativity.,Authentic leadership has to be synergized with environment of psychological safety and team environment of trust, to enhance employees' creativity. The results of the study delineated practical applications for both the researchers and policymakers. The results of this study would also augment the body of knowledge on human resource practices in both developed and developing countries.,This is one of the first studies to empirically examine the influence of authentic leadership on knowledge sharing and employee's creativity through the mediator role of team environment of psychological safety and trust in OEPA in Pakistan.
Gallbladder Carcinoma: Epidemiology, Risk Factors and Modes of Presentation in Eastern Part of India
Year: 2020

Gallbladder cancer is the most common malignant tumour of the biliary tract worldwide and also the most aggressive cancer of the biliary tract with the shortest median survival from the time of diagnosis.  A descriptive observational study was carried out at R.G. Kar Medical College, Kolkata over a period of one and half years (January 2012 to June 2013) Fifty one diagnosed cases of carcinoma gall bladder (proven either on preoperative/peroperative biopsy or computed tomography or ultrasound guided fine needle aspiration cytology) were included as study subjects. Interview of the study subjects for eliciting socio demographic, reproductive and other risk factors was done. Information on clinical and histological staging was ascertained by review of relevant records/reports as well as clinical examination as and when applicable.  Our study revealed that gallbladder cancer has a prominent peak in the 5th decade of life with the mean age of presentation being 58.2 years. Male:female ratio of gallbladder cancer is 1:2.93. Only four patients had a suggestive family history. Most of the patients are from rural Bengal and of poor socioeconomic status. The most prevalent addiction in the affected patients was addiction to tobacco and pan (beetle leaf) Most of the patients were having body mass index below 24, which is in sharp contrast to the available literatures. Significant association between gallstone disease and chronic typhoid infection was found. In female significant association is found with reproductive characteristics. Increased exposure to reproductive hormones by means of increased level of estrogen and progesterone in body as in OCP intake, early age at menarche, late age of first pregnancy, late menopause etc, postmenopausal status are found in majority of female patients. Pain is the most common symptom followed by constitutional symptoms, and most common clinical finding was jaundice followed by lump abdomen. Majority of patients presented at later stage of the disease i.e. in stage IV and stage III with initial raised CA19 9 level.  Gall bladder cancer is a very deadly disease with very poor prognosis with most of the cases presenting in advanced stages.
Mining Subjectively Interesting Attributed Subgraphs
Venue: ArXiv
Year: 2019

Community detection in graphs, data clustering, and local pattern mining are three mature fields of data mining and machine learning. In recent years, attributed subgraph mining is emerging as a new powerful data mining task in the intersection of these areas. Given a graph and a set of attributes for each vertex, attributed subgraph mining aims to find cohesive subgraphs for which (a subset of) the attribute values has exceptional values in some sense. While research on this task can borrow from the three abovementioned fields, the principled integration of graph and attribute data poses two challenges: the definition of a pattern language that is intuitive and lends itself to efficient search strategies, and the formalization of the interestingness of such patterns. We propose an integrated solution to both of these challenges. The proposed pattern language improves upon prior work in being both highly flexible and intuitive. We show how an effective and principled algorithm can enumerate patterns of this language. The proposed approach for quantifying interestingness of patterns of this language is rooted in information theory, and is able to account for prior knowledge on the data. Prior work typically quantifies interestingness based on the cohesion of the subgraph and for the exceptionality of its attributes separately, combining these in a parameterized trade off. Instead, in our proposal this trade off is implicitly handled in a principled, parameter free manner. Extensive empirical results confirm the proposed pattern syntax is intuitive, and the interestingness measure aligns well with actual subjective interestingness.