Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information

Most text-classification approaches represent the input based on textual features, either feature-based or continuous. However, this ignores strong non-linguistic similarities like homophily: people within a demographic group use language more similar to each other than to non-group members. We use homophily cues to retrofit text-based author representations with non-linguistic information, and introduce a trade-off parameter. This approach increases in-class similarity between authors, and improves classification performance by making classes more linearly separable. We evaluate the effect of our method on two author-attribute prediction tasks with various training-set sizes and parameter settings. We find that our method can significantly improve classification performance, especially when the number of labels is large and limited labeled data is available. It is potentially applicable as preprocessing step to any text-classification task.
The Effect of Ice-Bag Applied to Femoral Region of Individuals with Percutaneous Coronary Intervention on Local Vascular Complications and Low Back-Pain

Objectives: The main aim is of this paper are to determine the effect of applying local ice-bag on local vascular complications and on low back-pain in individuals, who were applied coronary invasive intervention. Background: One of the methods used in both diagnosis and treatment of coronary artery disease is percutaneous coronary intervention (PCI). The complications which develop due to these interventions are important causes of morbidity. After coronary invasive interventions, sandbag is prescribed in order to minimize vascular complications, but this often leads to back pain. Methods: The study was conducted with 298 patients (104 in ice-bag group, 100 in sandbag group and 94 in no application group) who were eligible for the study criteria. The data were collected with Patient Identification Form, Vascular Complications Form, Vital Signs Form and Visual Analog Scale (VAS). Local vascular complications developing at the hospital were called as “first follow-up complications”, complications developing after one week from the discharge were called as “second follow-up complications” and if complications developed both at the hospital and after discharge, these complications were called as “total complications”. The low back-pain that patients fell was evaluated 5 times, first immediately after the procedure and then 2-4 and 6 hours after the procedure and finally immediately before discharge from the hospital. Signed consent forms for the study were obtained from patients after the ethics committee approval. Results: In the first follow-up, the group who had least complications was the ice-bag group (p>0.05) and at the second and total follow-up, the group who had least complications was ice-bag group (p<0.001). VAS score of all groups was gradually increased and lowest increase was in the ice-bag group. Follow up times and VAS score of groups were determined as statistically significant (p<0.001). Conclusion: Applying ice-bag to individuals who were applied coronary invasive intervention is an effective method for reducing complication development and low back-pain. Therefore, this approach is recommended to be included in nursing practices.
A Frequency-Domain Implementation of a Sliding-Window Traffic Sign Detector for Large Scale Panoramic Datasets

In large-scale automatic traffic sign surveying systems, the primary computational effort is concentrated at the traffic sign detection stage. This paper focuses on reducing the computational load of particularly the sliding window object detection algorithm which is employed for traffic sign detection. Sliding-window object detectors often use a linear SVM to classify the features in a window. In this case, the classification can be seen as a convolution of the feature maps with the SVM kernel. It is well known that convolution can be efficiently implemented in the frequency domain, for kernels larger than a certain size. We show that by careful reordering of sliding-window operations, most of the frequency-domain transformations can be eliminated, leading to a substantial increase in efficiency. Additionally, we suggest to use the overlap-add method to keep the memory use within reasonable bounds. This allows us to keep all the transformed kernels in memory, thereby eliminating even more domain transformations, and allows all scales in a multiscale pyramid to be processed using the same set of transformed kernels. For a typical sliding-window implementation, we have found that the detector execution performance improves with a factor of 5.3. As a bonus, many of the detector improvements from literature, e.g. chi-squared kernel approximations, sub-class splitting algorithms etc., can be more easily applied at a lower performance penalty because of an improved scalability.
Interliminal Design: Mitigating Cognitive Bias and Design Distortion

The presence of cognitive biases and heuristics induces design distortions, unintended mismatches between desired and actual design outcomes. Interliminal Design is an intentional, adaptive and imaginative design process that mitigates design distortions. The process involves intentional and agile maneuvering between various personal cognitive and structural systems, thereby overcoming negative impacts of cognitive biases and heuristics.Cognitive heuristics are mental shortcuts adapted to enable rapid interpretation of the complex environment in which we evolved and live. These heuristics are inherent in human cognition and resist modification. When applied outside the appropriate context, these heuristics often give rise to systematic errors in human reasoning. Cognitive biases are the result of the context-inappropriate application of cognitive heuristics. Unfortunately, cognitive bias in design thinking often goes unnoticed and unaddressed, resulting in all degrees of design distortion that often affect multiple dimensions of an issue.Design distortions induced by cognitive biases are most apparent in failures to address complex, wicked and super wicked problems. These problems are characterized by incomplete, changing, intricately interdependent, yet contradictory, requirements. They frequently have short timelines, no central authority, and are caused by the same entities charged with solving them using existing irrational policies. We propose a design methodology, emerging at the Collaborative Design program at Pacific Northwest College of Art, to specifically mitigate the contributing factor of irrationality to design. The authors developed and taught an MFA course called Design Thinking and Cognitive Biases to explore the influence of cognitive biases on design and formulate techniques to raise awareness and reduce design distortion induced by cognitive biases. Interliminal Design recognizes design thinking as an ecosystem comprised of evolving individuals in conscious and subconscious relationships with each other where learning, emergence and adaptation are frequent and nonlinear. Individuals in collaborative design groups working on various dimensions of the design process must effortfully mitigate biases on a personal level. In addition, group-level biases must also be addressed. Counter-intuitively, individuals in groups do not “average out” their biases; instead, biases common across individuals, as well as group dynamics, can result in the strengthening of design biases that further distort the design process and outcomes. Thus, the collaborative group as a whole must also work to mitigate bias.
Effects of alternative raw materials and varying resin content on mechanical and fracture mechanical properties of particle board

Particle boards were produced from different types of wood particles, i.e. spruce, recovered particles, willow, poplar, and locust. Effects of raw material, as well as varying resin content on mechanical and fracture mechanical properties were investigated. For the analysis of mechanical properties, specific fracture energy, stress intensity factors, and the industrial European standard methods internal bond strength according to EN 319 and bending strength according to EN 310 were used. The total fracture energy was measured, and the stress intensity factor was calculated by means of data achieved through finite element simulations. Results of the fracture energy analysis were compared to internal bond strength (IB) and bending strength. While IB and the modulus of elasticity (MOE) showed a high variability, data scattering for fracture energy tests and modulus of rupture (MOR) were smaller, which are due to significant differences between the resin contents of the various board types.
Detecting Irregular Patterns in IoT Streaming Data for Fall Detection

Detecting patterns in real time streaming data has been an interesting and challenging data analytics problem. With the proliferation of a variety of sensor devices, real-time analytics of data from the Internet of Things (IoT) to learn regular and irregular patterns has become an important machine learning problem to enable predictive analytics for automated notification and decision support. In this work, we address the problem of learning an irregular human activity pattern, fall, from streaming IoT data from wearable sensors. We present a deep neural network model for detecting fall based on accelerometer data giving 98.75 percent accuracy using an online physical activity monitoring dataset called “MobiAct”, which was published by Vavoulas et al. The initial model was developed using IBM Watson studio and then later transferred and deployed on IBM Cloud with the streaming analytics service supported by IBM Streams for monitoring real-time IoT data. We also present the systems architecture of the real-time fall detection framework that we intend to use with Mbientlab's wearable health monitoring sensors for real time patient monitoring at retirement homes or rehabilitation clinics.
An Efficient General Decentralized Clustering Exploiting Hierarchy

Clustering or unsupervised learning is important for analyzing large data sets. Large amounts of data are distributed among multiple sources. Examination of this data and identifying clusters is challenging due to processing storage and transmission costs. In this project we are implementing GD cluster, General Decentralized Clustering (GD) method which is capable of clustering dynamic and disturbed data sets. Nodes store and share a set of data items from other nodes. Data items represent internal data which may change over time and external data which may also store attribute vectors of data items from other nodes. Each data item is presented using an attribute vector. The union of internal and external data items is referred to as data item. Each node performs two tasks “DRIVE” and “COLLECT” which executes repeatedly and continuously in parallel. Nodes continuously cooperate through decentralized gossip-based communication to maintain summarized views of dataset. We modify GD cluster for execution of the Hierarchical Grid clustering methods on the summarized views and also offer enhancements to the basic algorithm. In this project experimental evaluation shows that GD cluster compared with the popular method LSP2P, clusters efficiently with scalable transmission cost. Indexterms: Distributedsystems, clustering, hierarchical clustering, dynamic system -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 04-07-2017 Date of acceptance: 26-07-2017 -------------------------------------------------------------------------------------------------------------------------------------
Clustering of Janus particles in an optical potential driven by hydrodynamic fluxes

Self-organisation is driven by the interactions between the individual components of a system mediated by the environment, and is one of the most important strategies used by many biological systems to develop complex and functional structures. Furthermore, biologically-inspired self-organisation offers opportunities to develop the next generation of materials and devices for electronics, photonics and nanotechnology. In this work, we demonstrate experimentally that a system of Janus particles (silica microspheres half-coated with gold) aggregates into clusters in the presence of a Gaussian optical potential and disaggregates when the optical potential is switched off. We show that the underlying mechanism is the existence of a hydrodynamic flow induced by a temperature gradient generated by the light absorption at the metallic patches on the Janus particles. We also perform simulations, which agree well with the experiments and whose results permit us to clarify the underlying mechanism. The possibility of hydrodynamic-flux-induced reversible clustering may have applications in the fields of drug delivery, cargo transport, bioremediation and biopatterning.
Radiofrequency Dosimetry in Subjects Implanted with Metallic Structures Undergoing MRI: a Numerical Study

A numerical study to investigate the effects of the exposure to radiofrequency (RF) field on biological tissues implanted with thin metallic structures has been carried out, using the finite difference time domain (FDTD) solution technique. The particular case of the RF used in the clinical 1.5 T magnetic resonance imaging (MRI) scanners has been studied. The results of the model show that the presence of a metallic wire yields to a significant increase in the local specific absorption rate (SAR) and in temperature around the implant. Present standards or guidelines do not specifically address this problem and use threshold levels and methods to define safe exposure conditions that cannot apply to reveal high SAR gradients, such as the ones generated by thin metallic implanted objects. In particular, an averaging mass of 10 g, typically used for SAR calculation to evaluate the effect of RF exposure of biological tissues, is too big if compared to the SAR gradient generated by thin metallic structures. The SAR estimation according to this method cannot be considered a reliable indicator to evaluate the potential damage due to the RF induced heating at the interface between the implant and biological tissues. Five wires of different lengths and thicknesses were simulated and the SAR at the wire tip was evaluated over 10g, 1g, 0.1g averaging mass: the SAR underestimation related to the 10g and 1g masses, compared to 0.1g mass, was greater than 90 % and 60 %, respectively. In conclusion, present standards cannot be applied to thin metallic implants. The amount of averaging mass should be chosen as a function of the dimension of the implanted object and of the SAR gradient that is generated around it. At the same time, the averaging volume has to be not too small to be just a single “hot spot”, not relevant for the evaluation of potential tissue damage due to RF exposure.
An Evolutionary Algorithm Based On The Aphid Life Cycle

This paper proposes an evolutionary algorithm based on the reproduction cycle of aphids. The proposed algorithm will alternate between multiple reproduction operators based on the fitness of the population. Through the alternation of reproduction strategies the balance between exploration and exploitation can be manipulated to achieve faster convergence. Two variations on the proposed algorithm are implemented and compared to the standard evolutionary algorithm and clonal expansion. The comparison of converging times of the algorithms show that both variations of the proposed algorithm can be ef-
Using Agent Belief to Model Stock Returns

It is clear that movements in stock prices are correlated with events in the larger world such as natural disasters, news, consumer attitudes, unemployment rates, etc. However, modeling even a few of these factors and their complex (largely hidden) interactions is infeasible. As a result, many choose to model stock returns as a purely stochastic process looking only to previous data to drive the model. The drawback to this approach is that seemingly spontaneous, drastic changes in stock volatility occur that are difficult to predict given only the past. In this paper, we propose a hybrid approach to modeling stock returns based on the assumption that the stock price is independent of external factors given every agent’s subjective belief of the worth of the stock. An agent can be an institution, mutual fund, insider or any other entity that can buy or sell stock. Every agent weighs the external factors and previous stock performance and decides how much a single share of stock should be worth. The stock price is then a function of these hidden agent beliefs independent of any external influences. To model an agent’s latent belief of the stock price, we must also model how agents influence one another and how beliefs evolve over time. In particular, we want to capture the tendency of investors to flock together. There are many models from various disciplines that simulate this type of behavior: particle-motion in physics, flocking in computer graphics, rumor cascades in social networks, and herding models in finance. We consider a herding model and combine it with a stochastic time series model for a new approach to modeling stock returns.
Biological structures as a source of habitat heterogeneity and biodiversity on the deep ocean margins

Biological structures exert a major influence on species diversity at both local and regional scales on deep continental margins. Some organisms use other species as substrates for attachment, shelter, feeding or parasitism, but there may also be mutual benefits from the association. Here, we highlight the structural attributes and biotic effects of the habitats that corals, sea pens, sponges and xenophyophores offer other organisms. The environmental setting of the biological structures influences their species composition. The importance of benthic species as substrates seems to increase with depth as the complexity of the surrounding geological substrate and food supply decline. There are marked differences in the degree of mutualistic relationships between habitat-forming taxa. This is especially evident for scleractinian corals, which have high numbers of facultative associates (commensals) and few obligate associates (mutualists), and gorgonians, with their few commensals and many obligate associates. Size, flexibility and architectural complexity of the habitat-forming organism are positively related to species diversity for both sessile and mobile species. This is mainly evident for commensal species sharing a facultative relationship with their host. Habitat complexity is enhanced by the architecture of biological structures, as well as by biological interactions. Colony morphology has a great influence on feeding efficiency for suspension feeders. Suspension feeding, habitat-forming organisms modify the environment to optimize their food uptake. This environmental advantage is also passed on to associated filter-feeding species. These effects are poorly understood but represent key points for understanding ecosystems and biodiversity on continental margins. In this paper we explore the contributions of organisms and the biotic structures they create (rather than physical modifications) to habitat heterogeneity and diversity on the deep continental margins.
An Empirical Analysis of Pakistan’s Bilateral Trade: A Gravity Model Approach

This study aimed to investigate Pakistan’s bilateral trade flows with major trading partners. Panel data for the time period 1990-2010 with a frequency of two years was analyzed. Gravity model was employed for the analysis of the data and proved to be successful in explaining Pakistan’s bilateral trade flows by high values of R-square and adjusted R-square. Results revealed that GDP and GDP Per capita positively affect trade volume while distance and dummy variable for cultural similarities showed a negative relationship towards trade volume. Ratio of actual trade to predicted trade determined for each of the partner country for the year 2010 revealed that Japan, Turkey, Malaysia, India and Iran have greater unrealized trade potential with Pakistan which leads to policy implications.
1 Endophytic fungi associated with leaves of Betulaceae in Japan 1 2

Diversity and species composition of endophytic fungi on leaves of 11 tree 10 species in Betulaceae were studied, with reference to climatic, tree species, and seasonal 11 variations. A total of 186 fungal isolates were obtained from 190 leaves collected in a 12 subalpine, a cool temperate, and a subtropical forest in Japan and were divided into 46 13 operational taxonomic units (OTUs) according to the base sequences of D1/D2 region of 14 LSU rDNA. The two most frequent OTUs were Muscodor sp. and Nemania sp. in 15 Xylariaceae, followed by Gnomonia sp. , Glomerella acutata, Apiosporopsis sp. , Asteroma 16 sp. , and Cladosporium cladosporioides. The similarities of OTU composition in endophytic 17 2 fungal assemblages on leaves of Betulaceae were generally low among the forests of 1 different climatic regions. Fungal OTU compositions were relatively similar between two 2 Betula species in subalpine forest, whereas seven tree species in cool temperate forest were 3 divided into three groups according to the similarity of endophytic fungal assemblages on 4 the leaves, with four Carpinus species assigned into two of the three groups. The similarity 5 of endophytic fungal assemblages between August and October was relatively high in 6 subalpine forest, whereas the seasonal changes were generally greater (i.e. the similarities 7 among sampling dates were lower) in cool temperate forest. 8
Knowledge of Men and Women about Infertility Risk Factors

With technological developments, creation of new industries and changing lifestyles, infertility risk factors (IRF) increased. Therefore creation of suitable solutions in society is crucial for the prevention of infertility. Since prevention of infertility in a society is directly related to awareness about infertility, in the present study the awareness of people about infertility risk factors was assessed. In this study, a survey questionnaire containing 24 factors related to IRF was developed and 170 persons (62 men and 108 women) from Fooladshahr, Najafabad and Mobarakeh participated and data were extracted from these questionnaires and were statistically analyzed. Our study showed that average infertility awareness for men and women is 38% and 49% respectively. Also overall awareness was 44%. Also results showed that women’s awareness about mobile radiation and fast foods is significantly higher than men. On the other hand men have significantly higher awareness about adverse effects of chlorine in swimming pool on fertility. Higher women awareness may be related to their higher sensitivity for fertility. Also, generally a higher level of awareness was observed relating to mobile, air pollution, stress, fast foods, smoking and alcohol. Women have a higher level of awareness about infertility risk factors. Higher awareness about some IRFs may be results from widespread media coverage.
EFFECT OF FLUORIDE ON METABOLIC PATTERNS AND NITRATE REDUCTASE ACTIVITY IN Ziziphus

The objective of present study was to investigate the effect of sodium fluoride (NaF) on pigments, metabolic patterns and Nitrate reductase activity in in-vitro grown seedlings of Ziziphus mauritiana cv.Tikadi. Sodium fluoride treatments (1 and 10 mM ) decreased pigment composition (chl a, b and carotenoids) and chlorophyll (a and b) is more reduced than carotenoids. F toxicity also decreased the soluble protein content whereas, NR activity and total soluble sugars were increased significantly with increased level of Fluoride. Lower concentration of fluoride increased proline content while it was decreased with higher concentration of NaF.