Miracle-dz/UniFAR-FTUs huggingface dataset:
Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Personality Impressions

Flickr allows its users to generate galleries of "faves", i.e., pictures that they have tagged as favourite. According to recent studies, the faves are predictive of the personality traits that people attribute to Flickr users. This article investigates the phenomenon and shows that faves allow one to predict whether a Flickr user is perceived to be above median or not with respect to each of the Big-Five Traits (accuracy up to 79\% depending on the trait). The classifier - based on Gaussian Processes with a new kernel designed for this work - allows one to identify the visual characteristics of faves that better account for the prediction outcome.
Feature Selection for Case-Based Learning : A Cognitive Bias Approach

Experimental research in psychology, psycholinguistics, and cognitive science has discovered and examined numerous psychological constraints on human information processing. The tendency to concentrate on (a) present, rather than missing cues, (b) the current focus of attention, and (c) recent information are three examples. Short term memory limitations provide a fourth constraint. This paper shows that psychological constraints such as these can be used effectively as domain-independent sources of bias to improve learning. We first show that cognitive biases can be automatically and explicitly encoded into a baseline training instance representation. We then investigate the related problems of cognitive bias interaction and cognitive bias selection, and compare two selection methods that make varying assumptions about the independence of individual component biases. Finally, the paper shows that performance of a nearest-neighbor case-based learning algorithm on a natural language task improves as more cognitive biases are explicitly encoded into the baseline instance representation.
Using single source data to better understand User-generated Content (UGC) behavior

Single source refers to the unified measurement of different aspects of the same individual based on data from multiple sources. In the context of UGC, single source data can be used to study at least two important but as yet insufficiently investigated theoretical issues. First, single source data are ideal sources for studying inter-platform dynamics such as user migration across UGC platforms. Second, single source data can help to link individual self-reported cognitive factors with web crawled individual behavior logs, to achieve better understanding of individual behavior. In this paper, we select a random sample of Sina Blog users and collect their behavior information on both Sina Blog and Sina Weibo platforms; we also conduct an online survey to collect information about their cognitive factors. Merging all data together, we observe and quantify different behavior patterns of the same people across Blog and Weibo; we also identify alternative attractiveness and perceived popularity as significant drivers of one of the most important inter-platform dynamics - switching behavior.
Parallel multi-dimensional ROLAP indexing

This paper addresses the query performance issue for Relational OLAP (ROLAP) datacubes. We present a distributed multi-dimensional ROLAP indexing scheme which is practical to implement, requires only a small communication volume, and is fully adapted to distributed disks. Our solution is efficient for spatial searches in high dimensions and scalable in terms of data sizes, dimensions, and number of processors. Our method is also incrementally maintainable. Using "surrogate" group-bys, it allows for the efficient processing of arbitrary OLAP queries on partial cubes, where not all of the group-bys have been materialized. Our experiments show that the ROLAP advantage of better scalability, in comparison to MOLAP can be maintained while providing, at the same time, a fast and flexible index for OLAP queries.
Short-term link quality estimation for Opportunistic and Mobility Aware Routing in wearable body sensors networks

This paper investigates the issues related to the link quality estimation and routing performance in highly dynamic WBSNs, where the inherent body mobility introduces frequent time and space variations in the observed link reliability and network connectivity. A new Opportunistic and Mobility Aware Routing (OMAR) protocol is proposed, which optimizes the routing process by taking benefit from captured long-term and short-term link behavior. Extensive simulations are performed using realistic WBSNs mobility and channel modeling, and representative MAC constraints. The obtained results show that the proposed OMAR approach outperforms conventional routing protocols in terms of energy consumption and routing path length, while ensuring a stable packet delivery ratio.
Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3. nus.edu. sg/cgi-bin/svmprot.cgi.
Structural Integrity of the Contralesional Hemisphere Predicts Cognitive Impairment in Ischemic Stroke at Three Months

After stroke, white matter integrity can be affected both locally and distally to the primary lesion location. It has been shown that tract disruption in mirror’s regions of the contralateral hemisphere is associated with degree of functional impairment. Fourteen patients suffering right hemispheric focal stroke (S) and eighteen healthy controls (HC) underwent Diffusion Weighted Imaging (DWI) and neuropsychological assessment. The stroke patient group was divided into poor (SP; n = 8) and good (SG; n = 6) cognitive recovery groups according to their cognitive improvement from the acute phase (72 hours after stroke) to the subacute phase (3 months post-stroke). Whole-brain DWI data analysis was performed by computing Diffusion Tensor Imaging (DTI) followed by Tract Based Spatial Statistics (TBSS). Assessment of effects was obtained computing the correlation of the projections on TBSS skeleton of Fractional Anisotropy (FA) and Radial Diffusivity (RD) with cognitive test results. Significant decrease of FA was found only in right brain anatomical areas for the S group when compared to the HC group. Analyzed separately, stroke patients with poor cognitive recovery showed additional significant FA decrease in several left hemisphere regions; whereas SG patients showed significant decrease only in the left genu of corpus callosum when compared to the HC. For the SG group, whole brain analysis revealed significant correlation between the performance in the Semantic Fluency test and the FA in the right hemisphere as well as between the performance in the Grooved Pegboard Test (GPT) and theTrail Making Test-part A and the FA in the left hemisphere. For the SP group, correlation analysis revealed significant correlation between the performance in the GPT and the FA in the right hemisphere.
Pattern Discovery for Locating Motifs in Multivariate , Real-valued Time-series Data

The problem of locating motifs in multivariate, real-valued time series data concerns the discovery of sets of recurring patterns embedded in the time series. Each set is composed of several nonoverlapping subsequences and constitutes a motif because all of the subsequences are similar. This task is a natural extension of univariate motif discovery in both the symbolic and real-valued domains as previously addressed in the bioinformatics and data-mining research.
anoreinforced biocompatible hydrogels from wood hemicelluloses and ellulose whiskers uzaffer

Nanoreinforced hydrogels with a unique network structure were prepared from wood cellulose whiskers coated with chemically modified wood hemicelluloses. The hemicelluloses were modified with 2hydroxyethylmethacrylate prior to adsorption onto the cellulose whiskers in aqueous medium. Synthesis of the hydrogels was accomplished by in situ radical polymerization of the methacrylic groups of the adsorbed coating to form a network of poly(2-hydroxyethylmethacrylate) (PHEMA) matrix reinforced with cellulose whiskers. The mechanical, swelling and viscoelastic properties, of water-swollen hydrogels were investigated. Results indicated that the number of effective crosslinks between polymer chains
Toward an Integration of Constructivism and Existential Psychotherapy

Constructivism and existential psychology are distinct approaches to therapy that share many important commonalities. Included in the commonalities are a focus on meaning, relationships, change, and experience. We maintain that an existential-constructivist foundation can provide an ideal foundation for integration that can deepen approaches to therapy that resonate with aspects of existential and constructivist therapies. Three approaches to therapy highlight possibilities for integration: caregiver family therapy, equine-facilitated therapy, and substance abuse therapy. Building from these examples, various approaches to integration emerging from constructivism and existentialism can prove fruitful for developing a constructivism-existential psychology integration.
Detection of orthogonal concepts in subspaces of high dimensional data

In the knowledge discovery process, clustering is an established technique for grouping objects based on mutual similarity. However, in today's applications for each object very many attributes are provided. As multiple concepts described by different attributes are mixed in the same data set, clusters do not appear in all dimensions. In these high dimensional data spaces, each object can be clustered in several projections of the data. However, recent clustering techniques do not succeed in detection of these orthogonal concepts hidden in the data. They either miss multiple concepts for each object by partitioning approaches or provide redundant clusters in very similar subspaces. In this work we propose a novel clustering method aiming only at orthogonal concept detection in subspaces of the data. Unlike existing clustering approaches, OSCLU (Orthogonal Subspace CLUstering) detects for each object the orthogonal concepts described by differing attributes while pruning similar concepts. Thus, each detected cluster in an orthogonal subspace provides novel information about the hidden structure of the data. Thorough experiments on real and synthetic data show that OSCLU yields substantial quality improvements over existing clustering approaches.
Automated Adaptation Between Kiranti Languages

Minority language communities that are seeking to develop their language may be hampered by a lack of vernacular materials. Large volumes of such materials may be available in a related language. Automated adaptation holds potential to enable these large volumes of materials to be efficiently translated into the resource-scarce language. I describe a project to assess the feasibility of automatically adapting text between Limbu and Yamphu, two languages in Nepal's Kiranti grouping. The approaches taken—essentially a transfer-based system partially hybridized with a Kiranti-specific interlingua—are placed in the context of machine translation efforts worldwide. A key principle embodied in this strategy is that adaptation can transcend the structural obstacles by taking advantage of functional commonalities. That is, what matters most for successful adaptation is that the languages " care about the same kinds of things. " I examine various typological phenomena of these languages to assess this degree of functional commonality. I look at the types of features marked on the finite verb, case-marking systems, the encoding of vertical deixis, object-incorporated verbs, and nominalization issues. As this Kiranti adaptation goal involves adaptation into multiple target languages, I also present a disambiguation strategy that ensures that the manual disambiguation performed for one target language is fed back into the system, such that the same disambiguation will not need to be performed again for other target languages. iii ACKNOWLEDGEMENTS It is a blessing to be able to work with people that you really enjoy, and in this regard I have been blessed at every turn. This project could have never happened without the months of work poured into it by Marius Doornenbal and Jeffrey Webster, who have tirelessly dug to find answers and solutions, and coaxed or prodded me along when I needed it. –Thank you! I cannot imagine how this thesis would have come to be but for Dr. Tony Mattina's guidance and his time invested in me over these years. I was already indebted to him for setting my wife on the path where I consequently met her. I am now doubly indebted. –Thank you! It has a been a privilege, too, to receive much-needed help from such a master of Himalayan typology as Dr. David Watters. Not that I've grasped a fraction yet of what's really going on in these languages, but what little of it I've begun to understand is owing in great measure to him. –Thank you! Nowhere …
Multiplicative update for fast optimization of information retrieval based neighbor embedding

Dimensionality reduction of high-dimensional data for visualization has recently been formalized as an information retrieval task where original neighbors of data points are retrieved from the low-dimensional display, and the visualization is optimized to maximize flexible tradeoffs between precision and recall of the retrieval, avoiding misses and false neighbors. The approach has yielded well-performing visualization methods as well as information retrieval interpretations of earlier neighbor embedding methods. However, most of the methods are based on slow gradient search approaches, whereas fast methods are crucial for example in interactive applications. In this paper we propose a fast multiplicative update rule for visualization optimized for information retrieval, and show in experiments it yields equally good results as the previous state of the art gradient based approach but much faster.
Multi-Modal Preemptive Analgesia With Pregabalin, Acetaminophen, Naproxen, and Dextromethorphan in Radical Neck Dissection Surgery: A Randomized Clinical Trial

Background Preemptive analgesia may be considered as a method not only to alleviate postoperative pain but also to decrease analgesic consumption. Different regimens are suggested, but there is currently no standard. Objectives The aim was to measure the efficacy of preemptive analgesia with pregabalin, acetaminophen, naproxen, and dextromethorphan in radical neck dissection surgery for reducing the intensity of pain and morphine consumption. Patients and Methods This study was conducted as a randomized double-blind clinical trial. Eighty adult patients (18 to 60 years of age) under the American society of anesthesiologists (ASA) physical status I and II undergoing elective radical neck dissection were enrolled. Patients were randomized into two groups of 40 with a simple randomization method. The case group received a combination of 15 mg/kg acetaminophen, 2.5 mg/kg pregabalin, 7 mg/kg naproxen, and 0.3 mg/kg dextromethorphan administered orally one hour prior to surgery. Postoperative pain was assessed with the universal pain assessment tool (UPAT) at 0, 2, 4, 6, 12, and 24 hours after surgery. Subjects received morphine based on postoperative pain control protocol. Total administered morphine doses were noted. Results Postoperative pain rates at 0, 2, 4, 6, 12, and 24 hours after surgery were significantly lower for the case group than the control group (P values = 0.014, 0.003, 0.00, 0.00, and 0.00, respectively). Total morphine doses for the preemptive analgesia group were 45% lower than those of the other group. Side effects were similar for both groups. Conclusions A single preoperative oral dose of pregabalin, acetaminophen, dextromethorphan, and naproxen one hour before surgery is an effective method for reducing postoperative pain and morphine consumption in patients undergoing radical neck dissection.
Shouldering the Burden of Evidence-Based Practice: The Experiences of Physiotherapists Partaking in a Community of Practice

The study aim was to elicit the motivators, barriers, and benefits of participation in a Community of Practice (CoP) for primary care physiotherapists. We used a qualitative approach using semistructured interviews. The participants were twelve physiotherapists partaking in a newly formed Shoulder CoP. A desire for peer support was the strongest motivator for joining, with improving clinical practice being less apparent. Barriers to participation included time and work pressures and poor research skills. The structure of the CoP, in terms of access to meetings and the provision of preparation work and deadlines for the journal clubs, was reported to be a facilitator. Multiple benefits ensued from participation. The role of teamwork was emphasised in relation to reducing isolation and achieving goals. The majority of participants reported positive clinical practice changes in terms of improved patient education, increased confidence, and availability of new resources. All participants reported some element of personal growth and development, in particular in their evidence-based practice skills. The results provide support for the use of CoPs as a means of continuing professional development for physiotherapists in the workplace, as significant benefits are gained in terms of evidence-based practice (EBP), patient care, and therapist personal development.
Cyclone: Unified Stream and Batch Processing

Due to the rising demand for large-scale data processing, there is a growing interest in both batch processing, where large volumes of data are processed offline, and stream processing, where large quantities of streaming data are processed online. The dichotomy between these vastly different computing paradigms has led to the development of substantially different methodologies and systems. As there is an increasing number of applications requiring stream and batch processing, there is a need to bridge this gap and offer support for both paradigms. We introduce a new direction for the unification of stream and batch processing, which, contrary to other proposed approaches, uses a stream processing platform as its foundation and supports batch processing on top. Our proof-of-concept implementation of such a middleware layer, called Cyclone, offers the widely popular MapReduce programming model and translates MapReduce jobs for execution on the underlying streaming platform. Cyclone not only achieves a tight integration of batch and stream processing, our evaluation further shows significant performance gains, in particular for sequential and iterative jobs, which naturally arise in many applications.
Parsing TCT with Split Conjunction Categories

We demonstrate that an unlexicalized PCFG with refined conjunction categories can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar and reflect the Chinese idiosyncratic grammatical property. Indeed, its performance is the best result in the 3nd Chinese Parsing Evaluation of single model. This result has showed that refine the function words to represent Chinese subcat frame is a good method. An unlexicalized PCFG is much more compact, easier to replicate, and easier to interpret than more complex lexical models, and the parsing algorithms are simpler, more widely understood, of lower asymptotic complexity, and easier to optimize.
Ultra Low-Loss and Wideband Photonic Crystal Waveguides for Dense Photonic Integrated Systems

iii ACKNOWLEDGEMENTS I would like to express my deep appreciation to my thesis advisor, Prof. Ali Adibi for all his support, encouragement, and research directions. I would also like to thank my co-and Pablo for their technical helps. I had the pleasure of having useful discussions with and receiving technical helps and Arash for their helps and being there for me, when needed. I had the pleasure of having nice colleagues like thank you all for your helps and supports. Finally, my wordless gratitude goes to my beloved family, especially my dear parents, for all their love and support.
Block-interleaving based parallel CRC computation for multi-processor systems

A coarse-grained parallel computation method for cyclic redundancy check (CRC) is developed for efficient multiprocessor implementation. A received message is divided into a number of blocks, and several interleaved blocks are processed concurrently by multiple processing units. The synchronization overhead is minimized by resolving the dependency problem such that the individual results of multiple processing units can be combined at the last stage only. The block-interleaved parallel CRC computation method can be applied for variable length messages. The proposed coarsegrained parallel computation method can be easily combined with fine-grained parallel CRC computation algorithms to further increase the processing speed. The performance of the developed method is measured by an OpenMP-based parallel software implementation.