Miracle-dz/UniFAR-FTUs huggingface dataset:
Small scale direct dark matter search experiments

Experiments based on noble liquids and solid state cryogenic detectors have had a leading role in the direct detection of dark matter. But smaller scale projects can help to explore the new dark matter landscape with advanced, ultra-sensitive detectors based on recently developed technologies. Here, the physics case of different types of small scale dark matter experiments will be presented and many of them will be reviewed, highlighting the detection techniques and summarizing their properties, results and status.
Normal Limit Laws for Vertex Degrees in Randomly Grown Hooking Networks and Bipolar Networks

We consider two types of random networks grown in blocks. Hooking networks are grown from a set of graphs as blocks, each with a labelled vertex called a hook. At each step in the growth of the network, a vertex called a latch is chosen from the hooking network and a copy of one of the blocks is attached by fusing its hook with the latch. Bipolar networks are grown from a set of directed graphs as blocks, each with a single source and a single sink. At each step in the growth of the network, an arc is chosen and is replaced with a copy of one of the blocks. Using Pólya urns, we prove normal limit laws for the degree distributions of both networks. We extend previous results by allowing for more than one block in the growth of the networks and by studying arbitrarily large degrees.
"Tuning Biomaterial Properties to Enhance Pluripotent Stem Cell Self Renewal“

Objective: To study the mechanisms of salivary gland inflammation. Background: In pathological salivary gland disorders such as the autoimmune disease Sjögren’s syndrome and sialadenitis, inflammation is seen within the glandular epithelium, which affects the salivary gland acini and ducts. Tumor Necrosis Factor-α (TNF-α) is a major cytokine upregulated early during inflammation of the salivary glands. Acinar cells employ aquaporins, a family of protein water channels to transport water and small molecules to cross the apical membrane. Aquaporin-5 (AQP5), in particular, is responsible for water transport in the salivary gland and its localization to the apical membrane can be affected by inflammation. Methods: To study the mechanisms of salivary gland inflammation, we created an AQP5 Cre transgenic line, where the AQP5 promoter is used to drive expression of Cre primarily within the salivary gland. The AQP5 Cre mice allow for conditional deletion or overexpression of genes involved in the inflammatory process in order to examine their effect on the salivary gland architecture. Although AQP5 is found in organs other than the salivary glands, our double fluorescent studies showed a robust Cre-driven expression in the secretory cells of lacrimal and salivary glands. The resulting AQP5 Cre mice were bred to another mouse line that requires Cre-mediated recombination of the transgene to promote conditional overexpression of TNF-α. Results: The resulting double transgenic mice have overexpression of TNF-α localized to the salivary gland epithelium as seen by immunohistochemistry. In addition, increased amount of macrophages and inflammatory infiltrates were seen in the salivary gland epithelium as a result of the TNF-α expression. These results revealed that TNF-α is expressed in the salivary glands in the double transgenic mice, which, in turn causes recruitment of macrophages to the glandular epithelium. Conclusion: The inflammation seen in these mice mimics the inflammatory process that occurs in Sjögren’s syndrome and sialadenitis. Future Directions: In continuing this project, immunohistochemistry staining for macrophage identification in the salivary gland will be performed, and an ELISA will be performed to detect other cytokines (in addition to TNF-α) that may be up-regulated during an inflammatory response. Furthermore, a peripheral blood smear and white blood cell differential for wild type mice will be compared to that of AQP5-Cre positive mice as well as saliva collection to investigate potential for dry mouth in wild type and affected mice. Support: NIDCR Summer Dental Student Award and UConn School of Dental Medicine.
Let Me Know What to Ask: Interrogative-Word-Aware Question Generation

Question Generation (QG) is a Natural Language Processing (NLP) task that aids advances in Question Answering (QA) and conversational assistants. Existing models focus on generating a question based on a text and possibly the answer to the generated question. They need to determine the type of interrogative word to be generated while having to pay attention to the grammar and vocabulary of the question. In this work, we propose Interrogative-Word-Aware Question Generation (IWAQG), a pipelined system composed of two modules: an interrogative word classifier and a QG model. The first module predicts the interrogative word that is provided to the second module to create the question. Owing to an increased recall of deciding the interrogative words to be used for the generated questions, the proposed model achieves new state-of-the-art results on the task of QG in SQuAD, improving from 46.58 to 47.69 in BLEU-1, 17.55 to 18.53 in BLEU-4, 21.24 to 22.33 in METEOR, and from 44.53 to 46.94 in ROUGE-L.
The Effective Number of Risk Factors in UK Property Portfolios

Ang (2012) argues that a meal consisting of different foods with very similar nutrients might not be considered balanced and so a portfolio cannot be considered well-diversified if it is dominated by very few risk factors. In other words, while most investors would consider a portfolio that is spread across a large number of properties to be well diversified it may be undiversified if it is effectively driven by only a few risk factors. Using the diversification ratio of Choueifaty and Coignard (2008) we examine the effective number of risk factors in the UK from holding an increasing number of Local Authorities, using the annual returns of 210 ‘properties’, over the period 1981 to 2010. The main findings show that the effective number is very small, as a result of the high correlation between the various property-types and regions. The study therefore raises the question as to how welldiversified are current institutional portfolios in the UK.
Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products

In the last decade, it has been shown that many hard AI tasks, especially in NLP, can be naturally modeled as extreme classification problems leading to improved precision. However, such models are prohibitively expensive to train due to the memory blow-up in the last layer. For example, a reasonable softmax layer for the dataset of interest in this paper can easily reach well beyond 100 billion parameters (>400 GB memory). To alleviate this problem, we present Merged-Average Classifiers via Hashing (MACH), a generic K-classification algorithm where memory provably scales at O(logK) without any strong assumption on the classes. MACH is subtly a count-min sketch structure in disguise, which uses universal hashing to reduce classification with a large number of classes to few embarrassingly parallel and independent classification tasks with a small (constant) number of classes. MACH naturally provides a technique for zero communication model parallelism. We experiment with 6 datasets; some multiclass and some multilabel, and show consistent improvement over respective state-of-the-art baselines. In particular, we train an end-to-end deep classifier on a private product search dataset sampled from Amazon Search Engine with 70 million queries and 49.46 million products. MACH outperforms, by a significant margin,the state-of-the-art extreme classification models deployed on commercial search engines: Parabel and dense embedding models. Our largest model has 6.4 billion parameters and trains in less than 35 hours on a single p3.16x machine. Our training times are 7-10x faster, and our memory footprints are 2-4x smaller than the best baselines. This training time is also significantly lower than the one reported by Google's mixture of experts (MoE) language model on a comparable model size and hardware.
The 3D Structure of CO Depletion in High-mass Prestellar Regions

Disentangling the different stages of the star formation process, in particular in the high-mass regime, is a challenge in astrophysics. Chemical clocks could help alleviate this problem, but their evolution strongly depends on many parameters, leading to degeneracy in the interpretation of the observational data. One of these uncertainties is the degree of CO depletion. We present here the first self-consistent magneto-hydrodynamic simulations of high-mass, star-forming regions at different scales, fully coupled with a nonequilibrium chemical network, which includes C–N–O bearing molecules. Depletion and desorption processes are treated time dependently. The results show that full CO depletion (i.e., all gas-phase CO frozen-out on the surface of dust grains) can be reached very quickly, in one-third or even smaller fractions of the freefall time, whether the collapse proceeds on slow or fast timescales. This leads to a high level of deuteration in a short time, both for typical tracers like N2H+, as well as for the main ion H 3 + , the latter being in general larger and more extended. N2 depletion is slightly less efficient, and no direct effects on N-bearing molecules and deuterium fractionation are observed. We show that CO depletion is not the only driver of deuteration, and that there is a strong impact on Dfrac when changing the grain size. We finally apply a two-dimensional Gaussian point-spread function to our results to mimic observations with single-dish and interferometers. Our findings suggest that the low-values observed in high-mass star-forming clumps are in reality masking a full-depletion stage in the inner 0.1 pc region.
Conceptual Framework on Psychological Ownership as Predictor of Turnover Intentions

People develop feelings of ownership for a variety of objects, material and immaterial and this state is referred to as psychological ownership. In the light of various theories and broad discussions with faculty members of various technical institutions, psychological ownership acts as a positive source of job satisfaction and turnover intention. This conceptual study investigates the links between promotion focused and preventative focused factors of psychological ownership on turnover intentions in context with faculties of technical education institutes of India. On the basis of various research and published literature, the study proposes that this state finds its roots in a set of eight individual motives, which are identified as factors of psychological ownership and predictors of turnover intention. From the review of literature and group discussions, the seven theory-driven domains determined to best constitute the dimensions of promotion-oriented psychological ownership included self-efficacy, accountability, sense of belonging, self-identity, association with the organization, controlling and investing the self. The domain of territoriality was identified as a dimension of a preventative form of ownership. On the basis of various research and published literature, a 36-item research instrument was generated representing the eight theory-driven components of psychological ownership. The work provides a foundation for the development of a comprehensive theory of psychological ownership and the conceptual underpinnings for empirical testing.
When Does Diversity Help Generalization in Classification Ensembles?

Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element—“diversity. ” The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization of classification ensembles, we investigate three issues on diversity, that is, the measurement of diversity, the relationship between the proposed diversity and the generalization error, and the utilization of this relationship for ensemble pruning. In the diversity measurement, we measure diversity by error decomposition inspired by regression ensembles, which decompose the error of classification ensembles into accuracy and diversity. Then, we formulate the relationship between the measured diversity and ensemble performance through the theorem of margin and generalization and observe that the generalization error is reduced effectively only when the measured diversity is increased in a few specific ranges, while in other ranges, larger diversity is less beneficial to increasing the generalization of an ensemble. Besides, we propose two pruning methods based on diversity management to utilize this relationship, which could increase diversity appropriately and shrink the size of the ensemble without much-decreasing performance. The empirical results validate the reasonableness of the proposed relationship between diversity and ensemble generalization error and the effectiveness of the proposed pruning methods.
Turbostratic Orientations, Water Confinement and Ductile-Brittle Fracture in Bi-layer Graphene

Bi-layer graphene (BLG) can be a cheaper and more stable alternative to graphene in several applications. With its mechanical strength being almost equivalent to graphene, BLG also brings advanced electronic and optical properties to the table. Furthermore, entrapment of water in graphene-based nano-channels and devices has been a recent point of interest for several applications ranging from energy to bio-physics. Therefore, it is crucial to study the over-all mechanical strength of such structures in order to prevent system failures in future applications. In the present work, Molecular Dynamics simulations have been used to study crack propagation in BLG with different orientations between the layers. There is a major thrust in analyzing how the angular orientation between the layers affect the horizontal and vertical crack propagation in individual layers of graphene. The study has been extended to BLG with confined water in interfaces. Over-all strength of graphene sheets when in contact with water content has been determined, and prominent regional conditions for crack initiation are pointed out. It was seen that in the presence of water content, graphene deviated from its characteristic brittle failure and exhibited the ductile fracture mechanism. Origin of cracks in graphenes was located at the region where the density of water dropped near the graphene surface, suggesting that the presence of hydroxyl groups decelerate the crack formation and propagation in straining graphenes.
Short-term effects of biogas digestate and cattle slurry

18 The change in the German energy policy has resulted in a strong increase in the 19 number of biogas plants in Germany. As a consequence, huge amounts of nutrient 20 rich residues, the by-products of the fermentative process, are used as organic 21 fertilizers. Drained peatlands are increasingly used to satisfy the huge demand for 22 fermentative substrates (e.g. energy-crops, grass silage) and the digestate is 23 returned to the peatlands. However, drained organic soils are considered as hot 24 spots for nitrous oxide (N2O) emissions and organic fertilization is additionally known 25 to increase N2O emissions from managed grasslands. Our study addressed the 26 questions a) to what extent biogas digestate and cattle slurry application increase 27 N2O and methane (CH4) fluxes as well as the mineral nitrogen use efficiency (NUEmin) 28 and grass yield, and b) how different soil organic matter contents (SOM) and nitrogen 29 (N) contents promote the production of N2O. In addition NH3 volatilization was 30 determined at one application event to obtain first clues with respect to the effects of 31 soil and fertilizer types. The study was conducted at two sites within a grassland 32 parcel, which differed in their soil organic carbon (SOC) and N contents. At each site 33 (named Corg-medium and Corg-high) three plots were established, one was fertilized 34 five times with biogas digestate, one with cattle slurry and the third served as control 35 plot. On each plot, fluxes of N2O and CH4 were measured on three replicates over 36 two years using the closed chamber method. For NH3 measurements we used the 37 calibrated dynamic chamber method. On an annual basis, the application of biogas 38 digestate significantly enhanced the N2O fluxes compared to the application of cattle 39 slurry and additionally increased the plant N-uptake and NUEmin. Furthermore, N2O 40 fluxes from the Corg-high site significantly exceeded N2O fluxes from the Corg-medium 41 sites. Annual cumulative emissions ranged from 0.91 ± 0.49 kg N ha yr to 3.14 ± 42 0.91 kg N ha yr. Significantly different CH4 fluxes between the investigated 43 treatments or the different soil types were not observed. Cumulative annual CH4 44 exchange rates varied between −0.21 ± 0.19 kg C ha yr and −1.06 ± 0.46 kg C 45 ha yr. Significantly higher NH3 losses, NUEmin and grass yields from treatments 46 fertilized with biogas digestate compared to those fertilized with cattle slurry were 47 observed. The total NH3 losses following the splash plate application were 18.17 kg 48 N ha for the digestate treatments and 3.48 kg N ha for the slurry treatments (36% 49 and 15% of applied NH4 -N). The observed linear increase of 16 days cumulative 50 N2O-N exchange or rather annual N2O emissions, with mean groundwater level and 51 ammonium application rate, reveal the importance of site adapted N fertilization and 52 the avoidance of N surpluses in Corg rich grasslands. 53
Weakly-supervised Deep Anomaly Detection with Pairwise Relation Learning

This paper studies a rarely explored but critical anomaly detection problem: weakly-supervised anomaly detection with limited labeled anomalies and a large unlabeled data set. This problem is very important because it (i) enables anomaly-informed modeling which helps identify anomalies of interests and address the notorious high false positives in unsupervised anomaly detection, and (ii) eliminates the reliance on large-scale and complete labeled anomaly data in fully-supervised settings. However, the problem is especially challenging since we have only limited labeled data for a single class, and moreover, the seen anomalies often cannot cover all types of anomalies (i.e., unseen anomalies). We address this problem by formulating the problem as a pairwise relation learning task. Particularly, our approach defines a two-stream ordinal regression network to learn the relation of randomly selected instance pairs, i.e., whether the instance pair contains labeled anomalies or just unlabeled data instances. The resulting model leverages both the labeled and unlabeled data to effectively augment the data and learn generalized representations of both normality and abnormality. Extensive empirical results show that our approach (i) significantly outperforms state-of-the-art competing methods in detecting both seen and unseen anomalies and (ii) is substantially more data-efficient.
USING SAS@SOFTWARE TO EVALUATE THE POWER OF ALTERNATNE DATA ANALYTIC METHODS FOR REPEATED MEASURES DESIGNS

When performing a data analysis, there are many situations where more than one possible analytic method is available to the researcher. For example, when comparing the means of two or more populations, often one can choose between utilizing a parametric or non parametric procedure. Another common example of the availability of multiple data analytic methods is in the analysis of data obtained from repeated measures designs. In this case a researcher may choose to perform a univariate procedure, a multivariate procedure, or even a combination of both the univariate and multivariate methods.
Bounding Data-driven Model Errors in Power Grid Analysis

Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios, and proposes an evaluation implementation based on Rademacher complexity theory. We answer key questions for data-driven models: how much training data is required to guarantee a certain error bound, and how partial physical knowledge can be utilized to reduce the required amount of data. Our results are crucial for the evaluation and application of data-driven models in power grid analysis. We demonstrate the proposed method by finding generalization error bounds for two applications, i.e. branch flow linearization and external network equivalent under different degrees of physical knowledge. Results identify how the bounds decrease with additional power grid physical knowledge or more training data.
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input

Existing deep learning-based interactive image segmentation approaches typically assume the target-of-interest is always a single object and fail to account for the potential diversity in user expectations, thus requiring excessive user input when it comes to segmenting an object part or a group of objects instead. Motivated by the observation that the object part, full object, and a collection of objects essentially differ in size, we propose a new concept called scale-diversity, which characterizes the spectrum of segmentations w.r. t. different scales. To address this, we present MultiSeg, a scale-diverse interactive image segmentation network that incorporates a set of two-dimensional scale priors into the model to generate a set of scale-varying proposals that conform to the user input. We explicitly encourage segmentation diversity during training by synthesizing diverse training samples for a given image. As a result, our method allows the user to quickly locate the closest segmentation target for further refinement if necessary. Despite its simplicity, experimental results demonstrate that our proposed model is capable of quickly producing diverse yet plausible segmentation outputs, reducing the user interaction required, especially in cases where many types of segmentations (object parts or groups) are expected.
CANINE EHRLICHIOSIS - A CASE REPORT

Several especies of the genus Ehrlichia have been recognized as causing in dogs a clinical disease known as ehrlichiosis. Canine ehrlichiosis is a rickettsial disease caused by Ehrlichia canis transmitted by the brown dog tick Rhipicephalus sanguineus. Ehrlichiosis is an important infectious disease of dogs and other members of the Canidae family. Ehrlichia sp. is an obligate intracellular parasite that infects canine leukocytes. Diagnosis is made on the basis of sorologic tests (the indirect immunofluorescent antibody) and also by the evaluation of blood and buffycoat smears. Clinical signs of disease include fever, lethargy, anorexia, weight loss and petechial to ecchymotic hemorrhages of the skin and mucous membranes. The objective of this work is to report a case of a eight year-old, male, mixed breed dog, admitted to the UFPR Veterinary Hospital. The owner reported that the patient had vomiting and diarrhea. Blood collection was performed. The CBC showed regenerative anemia (RBC 3,38 x 106/μl, PCV 30%; Hgb 8 g/dL; polychromatophilic cells, Howell-jolly bodies, hypocromic cells and anisocytosis); neutrophilic leukocytosis (WBC 19.000 /μl; Seg neut 16.530 /μl; Band neut 570 /μl; Lymphs 1.140 /μl; Eos 190 /μl; Mon 570 /μl), total plasma proteins 5,2 g/dL. A morulae (an intracytoplasmatic azurophilic cluster), compatible with Ehrlichia sp, was found in a blood neutrophil from a blood cell smear stained with Wright stain. This case is being reported to alert clinicians to the possibility of occurence of canine ehrlichiosis in Curitiba PR.