Miracle-dz/UniFAR-FTUs huggingface dataset:
[Panel Discussion] Mobile, Cloud, and Crowd Computing

This is a story about evolution paths of mobile computing architectures after the emergence of cloud computing. As a result, the story gives us the deﬁnition of “cloud. ” We ﬁrstly summarize the current progress of cloud computing in view of broadband cellular networks which oﬀers Mbps-order, low-latency, and always-on packet connections. Many cell-phones are now being equipped with many I/O devices including GPS, microphones, motion sensors, and CCD cameras. Thus mobile “cloud” applications are going to be associated with real environments that will bring us context-aware capability. Moreover, cell-phones are now becoming crossroads of personal information such as mail, schedule, address, picture, and music, and consequently those connections facilitate new information hubs with the context-awareness. That mobile infrastructure has brought a highly-personalized and community-based popular communication culture that was never seen. The integration of the sensor hubs and the information hubs over people beyond individuals is now becoming a killer application incubation environment. We call it “crowd computing. ”
Explainable Data Decompositions

Our goal is to discover the components of a dataset, characterize why we deem these components, explain how these components are different from each other, as well as identify what properties they share among each other. As is usual, we consider regions in the data to be components if they show significantly different distributions. What is not usual, however, is that we parameterize these distributions with patterns that are informative for one or more components. We do so because these patterns allow us to characterize what is going on in our data as well as explain our decomposition. We define the problem in terms of a regularized maximum likelihood, in which we use the Maximum Entropy principle to model each data component with a set of patterns. As the search space is large and unstructured, we propose the deterministic Disc algorithm to efficiently discover high-quality decompositions via an alternating optimization approach. Empirical evaluation on synthetic and real-world data shows that efficiently discovers meaningful components and accurately characterises these in easily understandable terms.
BrainAGE and regional volumetric analysis of a Buddhist monk: a longitudinal MRI case study

ABSTRACT Yongey Mingyur Rinpoche (YMR) is a Tibetan Buddhist monk, and renowned meditation practitioner and teacher who has spent an extraordinary number of hours of his life meditating. The brain-aging profile of this expert meditator in comparison to a control population was examined using a machine learning framework, which estimates “brain-age” from brain imaging. YMR’s brain-aging rate appeared slower than that of controls suggesting early maturation and delayed aging. At 41 years, his brain resembled that of a 33-year-old. Specific regional changes did not differentiate YMR from controls, suggesting that the brain-aging differences may arise from coordinated changes spread throughout the gray matter.
Evolution or Revolution? Disparities in Earnings and Family Income in the Czech Republic 1988-2002

This paper draws on income surveys covering the period 1988-2002 to illustrate the changes in inequality of earnings and family incomes, the main factors behind their disparities, and the connections between these two distributions. The first part suggests a systemic change occurred, leading from the application of the “need principle” to the assertion of the “market principle”, and in the second part, the data sources used in the analysis are introduced. In the third part, the changing importance of individual factors of earnings in favour of education and occupation is demonstrated. The decreasing significance of demographic factors in the determination of earnings, along with increasing returns to human capital and professions, indicate the qualitative change in the system. In the fourth part, the intermediating factors between earnings and family income are presented and income packaging is analyzed. More income is collected from the labour market and more of it is redistributed by the state. Given to wider disparities earnings, the effect of second active member attenuated and the effect of the life cycle almost disappeared. The former two-earnings model has diminished in favour of a dominant role being taken up by the household head’s earnings. A systemic change occurred on one principal axis – the much greater role of education in the entire process.
On the variation of interaural time di erences with frequency 1

Interaural time di erence (ITD) is a major cue to sound localization in humans and animals. For a given subject and position in space, ITD depends on frequency. This variation is analyzed here using an HRTF database collected from the literature and comprising human HRTFs from 130 subjects and animal HRTFs from six specimens of di erent species. For humans, the ITD is found to vary with frequency, in a way that shows consistent di erences with respect to a spherical head model. Maximal ITD values were found to be about 800 μs in low frequencies and 600 μs in high frequencies. The ITD variation with frequency (up to 200 μs for some positions) occurs within the frequency range where ITD is used to judge the lateral position of a sound source. In addition, ITD varies substantially within the bandwidth of a single auditory lter, leading to systematic di erences between envelope and ne-structure ITDs. Because the frequency-dependent pattern of ITD does not display spherical symmetries, it potentially provides cues to elevation and resolves front/back confusion. The fact that the relation between position and ITDs strongly depends on the sound's spectrum in turn suggests that humans and animals make use of this relationship for the localization of sounds. PACS numbers: 4366Qp, 4366Pn, 4380Lb 19
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies

Multimodal image registration is a very challenging problem for deep learning approaches. Most current work focuses on either supervised learning that requires labelled training scans and may yield models that bias towards annotated structures or unsupervised approaches that are based on hand-crafted similarity metrics and may therefore not outperform their classical non-trained counterparts. We believe that unsupervised domain adaptation can be beneficial in overcoming the current limitations for multimodal registration, where good metrics are hard to define. Domain adaptation has so far been mainly limited to classification problems. We propose the first use of unsupervised domain adaptation for discrete multimodal registration. Based on a source domain for which quantised displacement labels are available as supervision, we transfer the output distribution of the network to better resemble the target domain (other modality) using classifier discrepancies. To improve upon the sliced Wasserstein metric for 2D histograms, we present a novel approximation that projects predictions into 1D and computes the L1 distance of their cumulative sums. Our proof-of-concept demonstrates the applicability of domain transfer from mono- to multimodal (multi-contrast) 2D registration of canine MRI scans and improves the registration accuracy from 33% (using sliced Wasserstein) to 44%.
Topics in Predictive Modeling in Marketing Research

—Identifying the best client is a significant factor to rising business as well as reaching triumph. Within the customer data reclines the legend of the customer’s buying behaviors, interests, desires, orientations, preferences, socio-demographic as well as geographic structure. Identifying how to furnish that database and decipher that story into one of actionable and triumphant marketing activities is the science and art of statistical modeling: the statistics constrain the conclusions and the art is the intertwining of the particular business rules connected to the ambitions. The current research enlists significant topics of predictive modeling: choice of variables, data analysis, management of missing data, as well as estimation of models. Issues roofed embrace stepwise choice and principal components approach of variable choice; imputation techniques, missing variables, and data fusion methods for missing data; and validation methods and metrics for assessing predictive models. 
Hierarchical Tensor Fusion Network for Deception Handling Negotiation Dialog Model

Handling deceptive information of conversation partner is effective to lead a successful negotiation. However, capturing deceptive information requires to consider multi-modal information of conversation partner. In this paper, we propose a multi-modal tensor fusion network for deception detection to be used in deception handling dialogue manager of negotiation dialog. Our method can be viewed as a combination of two existing integration methods of multi-modal features, hierarchical fusion, and tensor fusion. Experimental results showed that the proposed hierarchical tensor fusion network outperformed existing fusion methods in the accuracy of deception detection. Moreover, the negotiation dialog manager that utilizes the proposed method for deception detection achieved good performance in terms of the system’s action selection accuracy.
Native generalist consumers interact strongly with seeds of the invasive wild cucumber (Echinocystis lobata)

When alien plant species arrive in a new environment, they develop novel interactions with native biota that can range from negative to positive. Determining the nature and strength of these interactions is integral to understanding why some aliens are suppressed and others become highly invasive pests. For introduced terrestrial plants, seed and seedling interactions with native biota are crucial, because most nascent populations start from seed. Herein, we explored interactions between native generalist rodent and bird consumers and seeds of the invasive wild cucumber Echinocystis lobata by conducting seed-offering experiments in Poland. We also evaluated how interspecific competition from native plants and intraspecific competition from clustering of E. lobata seed (clustering resembling consumer seed caching) affected survival of seedlings and young plants. Native consumers interacted strongly with E. lobata seeds, with rodents removing 98% of seeds from ground locations and birds removing 24% of elevated seeds. Camera and live traps indicated that striped field mice Apodemus agrarius were the predominant rodent removing seeds. Camera traps and visual observations indicated that great tits Parus major and European jays Garrulus glandarius were the primary bird species removing elevated seeds. While some level of seed removal was likely attributable to seed predation, as indicated by seed coat remains, we also observed evidence that rodents may cache E. lobata seeds and Garrulus glandarius are known to cache and disperse seeds. Monitoring of seedlings indicated that increasing cover of native plants and clustering of E. lobata seedlings both reduced survival of seedlings and young plants due to interand intraspecific competition, respectively. Hence, caching by generalist consumers may disperse E. lobata seeds, which are heavy and lack dispersal adaptations, but such caching may also reduce individual seedling survival rates. Fully understanding invasion success of the E. lobata will require evaluating the net effects of generalist consumers on its recruitment and dispersal. Copyright Łukasz Dylewski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. NeoBiota 53: 25–39 (2019) doi: 10.3897/neobiota.53. 37431 http://neobiota.pensoft. net RESEARCH ARTICLE Advancing research on alien species and biological invasions A peer-reviewed open-access journal
C OMMON G EN : Towards Generative Commonsense Reasoning via A Constrained Text Generation Challenge

Given a set of common concepts like “ { apple (noun), pick (verb), tree (noun) } ”, humans ﬁnd it easy to write a sentence describing a grammatical and logically coherent scenario that covers these concepts, for example: “a boy picks an apple from a tree ”. The process of generating these sentences requires humans to use commonsense knowledge. We denote this ability as generative commonsense reasoning . Recent work in commonsense reasoning has focused mainly on discriminating the most plausible scenes from distractors via natural language understanding (NLU) settings such as multi-choice question answering. However, generative commonsense reasoning is a relatively unexplored research area, primarily due to the lack of a specialized benchmark dataset. In this paper, we present a constrained natu-ral language generation (NLG) dataset, named C OMMON G EN , to explicitly challenge machines in generative commonsense reasoning. It consists of 30 k concept-sets with human-written sentences as references. Crowd-workers were also asked to write the rationales (i.e. the commonsense facts) used for generating the sentences in the development and test sets. We conduct experiments on a variety of generation models with both automatic and human evaluation. Experimental results show that there is still a large gap between the current state-of-the-art pre-trained model, UniLM, and human performance. 1
SUOD: A Scalable Unsupervised Outlier Detection Framework

Outlier detection is a key data mining task for identifying abnormal objects from massive data. Due to the high expense of acquiring ground truth, practitioners lean towards building a large number of unsupervised models for further combination and analysis, rather than relying on a single model out of reliability consideration. However, this poses scalability challenge to high-dimensional, large datasets. In this study, we propose a three-module framework called SUOD to address the challenge. It can accelerate outlier model building and scoring when a large number of base models are used. It focuses on three complementary levels to speed up the process while controlling prediction performance degradation at the same time. At the data level, its Random Projection module projects high-dimensional data onto diversified low-dimensional subspaces while preserving the pairwise distance relationship. At the model level, SUOD's Pseudo-supervised Approximation module can approximate and replace fitted unsupervised models by low-cost supervised regressors, leading to fast offline scoring on new-coming samples with better interpretability. At the system level, Balanced Parallel Scheduling module mitigates the workload imbalance within distributed systems, which is helpful for heterogeneous outlier ensembles. As the three modules are independent with different focuses, they have great flexibility to "mix and match". The framework is also designed with extensibility in mind. One may customize each module based on specific use cases, and the framework may be generalized to other learning tasks as well. Extensive experiments on more than 20 benchmark datasets demonstrate SUOD's effectiveness. In addition, a real-world deployment system on fraudulent claim analysis by IQVIA is also discussed. The full framework, documentation, and examples are openly shared at this https URL.
LIMITING DISTRIBUTIONAL FIXED POINTS IN SYSTEMIC RISK GRAPH MODELS

We analyse the equilibrium behaviour of a large network of banks in presence of incomplete information, where inter-bank borrowing and lending is allowed, and banks suffer shocks to assets. In a two time period graphical model, we show that the equilibrium wealth distribution is the unique fixed point of a complex, high dimensional distribution-valued map. Fortunately, there is a dimension collapse in the limit as the network size increases, where the equilibriated system converges to the unique fixed point involving a simple, one dimensional distribution-valued operator, which, we show, is amenable to simulation. Specifically, we develop a Monte-Carlo algorithm that computes the fixed point of a general distribution-valued map and derive sample complexity guarantees for it. We numerically show that this limiting one-dimensional regime can be used to obtain useful structural insights and approximations for networks with as low as a few hundred banks.
Prospective Randomized Study Comparing the External Fixator and Volar Locking Plate in Intraarticular Distal Radius Fractures: Which Is Better?

Introduction Various treatment options for patients with an intraarticular distal radius fracture are available, but in cases of comminuted fractures, these narrow down to either a volar locking plate, an external fixator, or a combination of these two. We conducted this prospective study to compare the external fixation and internal fixation of intraarticular fractures of the distal radius in terms of clinical/functional outcome and complications and with the available literature. Material and method This prospective randomized study consisted of a total number of 30 patients with intraarticular fractures of the distal end of the radius divided randomly into two groups (A and B), treated by external fixation (Group A) and volar plating (Group B), in a tertiary care institute during the study period. Result The most patients were males >50 years of age, with injury to the right dominant hand most commonly caused by a fall on an outstretched hand. As per the modified Green & O’Brien scoring system, the volar plating group showed the final result as excellent in two (13.33%), good in seven (46.6%), fair in four (26.6%), and poor in two (13.3%) whereas an excellent outcome was seen in one (6.66%), good and fair in five patients each (33.3%), and poor in four (26.66%) patients at the six months follow-up. Conclusion Overall, both fixation techniques seem to apply sufficient stabilization to restore function and retain anatomy; however, volar locking plates have certain advantages over external fixator in the early postop period in terms of earlier recovery and mobilization.
Counting Graph Minors in Data Streams

We present two space bounded algorithms that with probability at least 1 − δ compute a ( 1 ± ǫ ) - approximation of the number of triangles in an undirected graph given as a stream of edges. Both algorithms are based on random sampling. Our ﬁrst algorithm does not make any assumptions on the order of edges in the stream. It uses space that is inversely related to the ratio between the number of triangles and the number of triples with at least one edge in the induced subgraph, and constant expected update time per edge. Our second algorithm is designed for incidence streams (all edges incident to the same vertex appear consecutively). It uses space that is inversely related to the ratio between the number of triangles and length 2 paths in the graph and expected update time O ( log | V | · ( 1 + s · | V | / | E | )) ,where s is the space requirement of the algorithm. These results signiﬁcantly improve over previous work [20, 7]. We also show how to extend our algorithms to cliques of arbitrary size and to bipartite cliques of small size. Since the space complexity depends only on the structure of the input graph and not on the number of nodes, our algorithms scale very well with increasing graph size and so they provide a basic tool to analyze the structure of large graphs. They have many applications, for example, in the discovery of Web communities, the computation of clustering and transitivity coefﬁcient, and discovery of frequent patterns in large graphs. We implemented both algorithms and evaluated their performance on networks from different application domains. The sizes of the considered input graphs varied from about 8, 000 nodes and 40, 000 edges about 135 million nodes and more than 1 billion edges. For both algorithms we run experiments with parameter s = 1, 000 , 10, 000 , 100, 000 , 1, 000, 000 to evaluate running time and approximation guarantee. Both algorithms appear to be time efﬁcient for these sample sizes. The approximation quality of the ﬁrst algorithm was varying signiﬁcantly and even for s = 1, 000, 000 we had more than 10 % deviation for more than half of the instances. The second algorithm performed much better and even for s = 10, 000 we had an average deviation of less than 6 % (taken over all but the largest instance for which we could not compute the number of triangles exactly).
Dzongkha Text Corpus

In this paper, we present the methodology for collecting and compiling text corpus in Dzongkha. The corpus resources is essential for developing applications such as Automatic Part of Speech Detection, Word Segmentation, Text to Speech and Morphological Analysis. This study resulted in building a corpus database containing at least 5 million words collected from relevant websites, print media and manually typed from printed documents. The corpus is tagged with automatic part of speech tagger to enrich the data and to make it more resourceful. In addition, the text corpus is transduced from their original format to an XML format compliant with the XML Corpus Encoding Standard (XCES).
GENETIC ALGORITHM APPLIED TO MULTILAYER STRUCTURE DETERMINATION

This paper shows an alternative way of the multilayer structure refinement to the well established least-squares method. If calculated for the low-angle x-ray reflectivity, the residual function (based on squared differences between the measured and the calculated reflectivity curves) is oscillating with respect to some parameters. This makes the refinement by the linearised least-squares method complicated and ineffective. Genetic algorithm can be an efficient substitute or at least a complementary technique to the linearised least-squares method.
Study on Phytochemical and Antioxidant Properties of Water Apple (Syzygium aqueum) and Chappathikalli (Opuntia ficus-indica)

The aim of this study is to determine the phytochemical and antioxidants properties, moisture, protein, fat, ash, crude fibre, acidity, TSS, pH and calcium content of wild fruits namely water apple (Syzygium aqueum) and chappathikalli (Opuntia ficusindica) and it was collected from Kodaikanal hills, Tamilnadu, India. The total phenolic contents from water apple and chappathikalli fruits extract, determined following the Folin-ciocalteu assay were found in the range of 28.80 –30.70 mg/100g and 55.40 to 55.10 mg/100g. The total flavonoids, antioxidant activity and vitamin C contents from water apple (Syzygium aqueum) and chappathikalli (Opuntia ficus-indica) fruit extract were found in the range of 62.03 to 62. 07μg/g and 69.50 to 69.51 144.50 μg/g, 144.50 to 138.40 to 144.50 mg AAeq/100g and 39.00 39.02 mg AAeq/100g and 13.06 to 13.08 mg/100g) and 30.00 to 30.03 mg/100g respectively. Whereas, the other chemical constituent’s moisture in the range was 91.70 g%, protein 0.31g%, fat 0.29 g%, Ash 1.24g%, Crude fibre 1.37g%, Acidity 0.07 g%, TSS 2.3 bx, pH 4.14 and calcium 0.64mg in water apple. The moisture content in chappathikalli ranged was 83.25 g%, protein 0.52g%, fat 0.38g%, ash 1.98g%, crude fibre 0.89g%, acidity 0.05g%, TSS 6.4bx, pH 4.28 and calcium 18.02mg respectively.