Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Simulated Martian Regolith Using an Array of Ion Selective Electrodes

A prototype miniature array of polymer membrane and solid state ion selective electrodes was developed for the purpose of performing an in-situ analysis of the soluble ionic species in Martian regolith (soil). The array contains a total of 27 electrodes for K þ ,N a þ ,C a 2þ ,M g 2þ ,N H þ 4 ,B a 2þ ,N O � 3 ,C l � , and Li þ , each in triplicate. Barium electrodes were used to indirectly monitor sulfate through precipitation by the addition of barium chloride while the lithium electrodes served as a reference for the array by having a constant lithium concentration as a background for all solutions. The array was tested with several types of simulants, soils, and sawdust from a Mars meteorite, all with varying salt content, meant to approximate the various hypotheses regarding the ionic composition of the Martian soil. The activities of anions and cations determined with the array were compared to ion chromatography data.
A study of feature extraction using supervised independent component analysis

Recently, independent component analysis (ICA) has been applied to not only problems of blind signal separation, but also feature extraction of images and sounds. In this paper, we study the effectiveness of Umeyama's (1999) supervised ICA (SICA) for feature extraction of handwritten characters. Two types of control vectors (supervisor) are proposed for SICA: 1) average patterns (Type-I); and 2) eigen-patterns (Type-II). To demonstrate the usefulness of SICA, recognition performance is evaluated for handwritten digits that are included in the MNIST database. From the results of recognition experiments, we certify that SICAs with both types of control vectors work effective for feature extraction. Actually, the within-class variance between-class variance ratio of SICA features with Type-I control vectors becomes slightly larger as compared with a conventional ICA.
Annotating Topics of Opinions

Fine-grained subjectivity analysis has been the subject of much recent research attention. As a result, the field has gained a number of working definitions, technical approaches and manually annotated corpora that cover many facets of subjectivity. Little work has been done, however, on one aspect of fine-grained opinions - the specification and identification of opinion topics. In particular, due to the difficulty of manual opinion topic annotation, no general-purpose opinion corpus with information about topics of fine-grained opinions currently exists. In this paper, we propose a methodology for the manual annotation of opinion topics and use it to annotate a portion of an existing general-purpose opinion corpus with opinion topic information. Inter-annotator agreement results according to a number of metrics suggest that the annotations are reliable.
Adaptive Index Utilization in Memory-Resident Structural Joins

We consider adaptive index utilization as a fine-grained problem in autonomic databases in which an existing index is dynamically determined to be used or not in query processing. As a special case, we study this problem for structural joins, the core operator in XML query processing, in the main memory. We find that index utilization is beneficial for structural joins only under certain join selectivity and distribution of matching elements. Therefore, we propose adaptive algorithms to decide whether to use an index probe or a data scan for each step of matching during the processing of a structural join operator. Our adaptive algorithms are based on the history, the look-ahead information, or both. We have developed a cost model to facilitate this adaptation and have conducted experiments with both synthetic and real-world data sets. Our results show that adaptively utilizing indexes in a structural join improves the performance by taking advantage of both sequential scans and index probes.
Cognitive abilities that predict success in a computer-based training program.

PURPOSE The purposes of this study were (a) to identify cognitive abilities and other factors related to successful completion of training for computer-based tasks that simulated real jobs and (b) to create a brief assessment battery useful in assessing older adults for these kinds of jobs. DESIGN AND METHODS Participants from three age groups (young, middle-aged, and older) completed a battery of cognitive measures. They then trained on one of three computer-based tasks that simulated actual jobs and were asked to perform the tasks for 3 days. We recorded whether they completed training and whether and how well they did the tasks. In a series of logistic regressions, we evaluated the ability of a subset of cognitive measures drawn from a larger battery to predict participants' ability to successfully complete training and go on to task performance. RESULTS Results confirmed theory-based expectations that measures of domain knowledge, crystallized intelligence, memory, and psychomotor speed would predict success in computer-based activities. A brief battery was able to predict older adults' successful completion of training for one task but was less useful for another. IMPLICATIONS A brief battery of cognitive measures may be useful in evaluating individuals for job selection. Different measures are related to job-related criteria depending on task and group evaluated, although it was not possible to identify a reduced battery for one task. The specific cognitive abilities related to participants' success have implications for task and interface design for the elderly population.
Robust Feature Extraction and Temporal Analysis for Partial Fingerprint Identification

Identification of an individual from discriminating features of the friction ridge surface is one of the oldest and most commonly used biometric techniques. Methods for identification span from tedious, although highly accurate, manual examination to much faster Automated Fingerprint Identification Systems (AFIS). While automatic fingerprint recognition has grown in popularity due to the speed and accuracy of matching minutia features of good quality plain-to-rolled prints, the performance is less than impressive when matching partial fingerprints. For some applications, including forensic analysis where partial prints come in the form of latent prints, it is not always possible to obtain high-quality image samples. Latent prints, which are lifted from a surface, are typically of low quality and low fingerprint surface area. As a result, the overlapping region in which to find corresponding features in the genuine matching ten-print is reduced; this in turn reduces the identification performance. Image quality also can vary substantially during image capture in applications with a high throughput of subjects having limited training, such as in border control. The rushed image capture leads to an overall acceptable sample being obtained where local image region quality may be low. We propose an improvement to the reliability of features detected in exemplar prints in order to reduce the likelihood of an unreliable overlapping region corresponding with a genuine partial print. A novel approach is proposed for detecting minutiae in low quality image regions. The approach has demonstrated an increase in match performance for a set of fingerprints from a well-known database. While the method is effective at improving match performance for all of the fingerprint images in the database, a more significant improvement is observed for a subset of low quality images.
ETL queues for active data warehousing

Traditionally, the refreshment of data warehouses has been performed in an off-line fashion. Active Data Warehousing refers to a new trend where data warehouses are updated as frequently as possible, to accommodate the high demands of users for fresh data. In this paper, we propose a framework for the implementation of active data warehousing, with the following goals: (a) minimal changes in the software configuration of the source, (b) minimal overhead for the source due to the active nature of data propagation, (c) the possibility of smoothly regulating the overall configuration of the environment in a principled way. In our framework, we have implemented ETL activities over queue networks and employ queue theory for the prediction of the performance and the tuning of the operation of the overall refreshment process. Due to the performance overheads incurred, we explore different architectural choices for this task and discuss the issues that arise for each of them.
Image Captioning with an Intermediate Attributes Layer

Many recent studies in image captioning rely on an architecture which learns the mapping from images to sentences in an end-to-end fashion. However, generating an accurate and complete description requires identifying all entities, their mutual interactions and the context of the image. In this work, we show that an intermediate image-to-attributes layer can dramatically improve captioning results over the current approach which directly connects an RNN to a CNN. We propose a two-stage procedure for training such an attribute-based approach: in the first stage, we mine a number of keywords from the training sentences which we use as semantic attributes for images, and learn the mapping from images to those attributes with a CNN; in the second stage, we learn the mapping from detected attribute occurrence likelihoods to sentence description using LSTM. We then demonstrate the effectiveness of our two-stage model with captioning experiments on three benchmark datasets, which are Flickr8k, Flickr30K and MS COCO.
Temporal Stability of Repeated Assessments of Problematic Internet Use Among Adolescents: A Prospective Cohort Study

The growth of Internet usage has been phenomenal in the last decade. According to the latest statistics on worldwide usage of the Internet, there has been a 4-times increase in the number of users.1 In terms of regional distributions by geographic locations, Asia is the area with the highest number of Internet users in the world in 2009 with more than 764.4 millions.1 With such a large population of Internet users, misuse and loss of control in the use of the Internet should not be expected rare events. Problematic Internet use has been identified as a potential mental health issue that exhibits some signs and symptoms similar to other established additions since the mid-90s.2-5 Since then the term “Internet Addiction” has been proposed and different opinions have been put forward as to whether problematic Internet use should be considered as a psychiatric disorder or a mental illness similar to other well established addictive disorders.6-11 Until now, “Internet Addiction” is not included as a disorder in the latest version of the Diagnostic and Statistical Manual of Mental Disorders-IV Text Revision (DSM-IV-TR) as well as in the International Classification of Diseases-10 (ICD-10). 12-13 The major contention of whether the problematic use of the Internet should be considered as an addiction in the conventional sense, such as in the case of substance dependent disorder, is how well Internet addiction can fulfil the validation criteria as a psychiatric disorder.14 Robins and Guze, who first proposed a set of formal criteria for establishing the validity of psychiatric diagnoses, suggested five criteria.15 These include: a clear clinical description of the disorder; evidence from laboratory studies; exclusion of other disorders; follow-up studies; and family studies.15 In terms of the first criterion, a volume of work by many researchers has provided an informative debate.6-11 Evidence from laboratory studies implies the possibility of bio-markers and validated psychiatric and psychological assessments. Recent studies using imaging techniques have provided some results for supporting differences in brain activities between problematic and normal Internet users.1618 Studies on the co-morbidities of problematic Internet use have been paving the way for the establishment of a clear picture for differential diagnoses of “Internet Addiction”. 19 Follow-up studies, as suggested by George, reflect the ability of demonstrating temporal
The Evolutionary Perks of Being Irrational (Extended Abstract)

Typical implementations of articial intelligent agents suggest that actions should be chosen in order to maximise some reward function. This naturally complies the philosophy behind rational choice theory. Yet, this heuristic may not always provide long-term success to the considered agents. In this paper, we stress the need to consider the self-organised and frequency-dependent nature of the environment, when designing agents that act in complex adaptive systems. We resort to the tools of evolutionary game theory, combined with a paradigmatic scenario of a population of self-regarding agents playing Ultimatum Game, to describe the dynamical impact of individual mistakes on collective behaviour. We resort to agent based simulations to show that that seemingly disadvantageous and irrational errors become the source of individual and collective long-term success.
Challenges in Disentangling Independent Factors of Variation

We study the problem of building models that disentangle independent factors of variation. Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis. As data we use a weakly labeled training set. Our weak labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown. This labeling is of particular interest as it may be readily available without annotation costs. To make use of weak labels we introduce an autoencoder model and train it through constraints on image pairs and triplets. We formally prove that without additional knowledge there is no guarantee that two images with the same factor of variation will be mapped to the same feature. We call this issue the reference ambiguity. Moreover, we show the role of the feature dimensionality and adversarial training. We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.
CYTOCHEMICAL LOCALIZATION OF ACID PHOSPHATASE ACTIVITY IN GRANULE FRACTIONS FROM RABBIT POLYMORPHONUCLEAR LEUKOCYTES

When rabbit peritoneal exudates (97% polymorphonuclear [PMN] leukocytes, 2% mononuclear cells) were fractionated by zonal sedimentation or isopycnic centrifugation, four fractions (A, B, C, and D) were obtained, as reported earlier. "A" consisted largely of PMN azurophil granules, "B" of PMN specific granules, and "D" of membranous elements. The source of the more heterogeneous "C" fraction (containing acid hydrolases) was uncertain. To gain further information on the nature of this fraction, cytochemical tests for acid phosphatase (AcPase) were carried out on the starting cells and on the fractions. In intact PMN, lead phosphate reaction product was found in Golgi complexes, perinuclear cisternae, and some azurophil granules (immature forms or disrupted mature forms) of a few cells. The specifics and the intact azurophils were not reactive. Reaction product was also found within Golgi cisternae, secondary lysosomes, and some of the azurophil granules of mononuclear cells. Observations on the A and B fractions confirmed those in situ regarding the localization of reaction product in disrupted PMN azurophils, its absence from specifics, and the latency of the enzyme activity in intact azurophils. In the C fraction, AcPase was found in three structures (a) Golgi cisternae, (b) dense bodies, and (c) small pleomorphic granules Comparison with the starting cells indicates that the Golgi complexes are probably derived from both PMN leukocytes and mononuclear cells, whereas the remaining elements resemble (in size, shape, and density) secondary lysosomes and azurophil granules of mononuclear cells. The results indicate that the bulk of the cytochemically detectable AcPase present in the C fraction is derived from mononuclear cells, rather than from PMN leukocytes
A New Sentence Compression Dataset and Its Use in an Abstractive Generate-and-Rank Sentence Compressor

Sentence compression has attracted much interest in recent years, but most sentence compressors are extractive, i.e., they only delete words. There is a lack of appropriate datasets to train and evaluate abstractive sentence compressors, i.e., methods that apart from deleting words can also rephrase expressions. We present a new dataset that contains candidate extractive and abstractive compressions of source sentences. The candidate compressions are annotated with human judgements for grammaticality and meaning preservation. We discuss how the dataset was created, and how it can be used in generate-and-rank abstractive sentence compressors. We also report experimental results with a novel abstractive sentence compressor that uses the dataset.
Baff Binds to the Tumor Necrosis Factor Receptor–Like Molecule B Cell Maturation Antigen and Is Important for Maintaining the Peripheral B Cell Population

The tumor necrosis factor (TNF) family member B cell activating factor (BAFF) binds B cells and enhances B cell receptor–triggered proliferation. We find that B cell maturation antigen (BCMA), a predicted member of the TNF receptor family expressed primarily in mature B cells, is a receptor for BAFF. Although BCMA was previously localized to the Golgi apparatus, BCMA was found to be expressed on the surface of transfected cells and tonsillar B cells. A soluble form of BCMA, which inhibited the binding of BAFF to a B cell line, induced a dramatic decrease in the number of peripheral B cells when administered in vivo. Moreover, culturing splenic cells in the presence of BAFF increased survival of a percentage of the B cells. These results are consistent with a role for BAFF in maintaining homeostasis of the B cell population.
Towards feature selection for disk-based multirelational learners: a case study with a boosting algorithm.

Feature selection is an important issue for any learning algorithm, since reduced feature sets lead to an improvement in learning time, reduced model complexity and, in many cases, a reduced risk of overfitting. When performing feature selection for RAM-based learning algorithms, we typically assume that the cost of accessing each feature is uniform. In multirelational data mining, especially when data are to be held in a relational database management system (RDBMS), this is no longer the case. The dominant cost in such a setting is the scan of a relation, so that the cost of using a feature from a relation that needs to be scanned anyway is comparatively small, whereas adding a feature from a relation that has not been used before is high. This means that existing work on feature selection using the uniform cost assumption may not be applicable in a disk-based setting. In this paper, we report the results of a case study that extends prior work on multirelational feature selection, in particular, in the context of a boosting algorithm. As shown by our study, using the previously developed strategies on average leads to larger numbers of relations that need to be considered and loaded into memory, and thus higher cost in a disk-based setting. Instead, a simple relation-oriented strategy can be used to minimize cost of accessing additional relations. We describe experimental results to show how this basic strategy interacts with the feature selection variants proposed previously, and show that significant gains are made even in a main-memory setting.
The biogeochemistry of tropical lakes: A case study from Lake Matano, Indonesia

We examined the chemical composition of the water column of Lake Matano, Sulawesi Island, Indonesia, to document how the high abundances of Fe (hydr)oxides in tropical soils and minimal seasonal temperature variability affect biogeochemical cycling in lakes. Lake Matano exhibits weak thermal stratification, yet a persistent pycnocline separates an oxic epilimnion from anoxic meta‐ and hypolimnions. The concentration of soluble P in the epilimnetic waters is very low and can be attributed to scavenging by Fe (hydr)oxides. Chromium concentrations in the epilimnion are high (up to 180 nmol L−1), but below U.S. Environmental Protection Agency guidelines for aquatic ecosystems. The concentration of chromium decreases sharply across the oxic‐anoxic boundary, revealing that the hypolimnion is a sink for Cr. Flux calculations using a one‐dimensional transportreaction model for the water column fail to satisfy mass balance requirements and indicate that sediment transport and diagenesis play an important role in the exchange of Fe, Mn, P, and Cr between the epilimnion and hypolimnion. Exchange of water between the epilimnion and hypolimnion is slow and on a time scale similar to temperate meromictic lakes. This limits recycling of P and N to the epilimnion and removal of Cr to the hypolimnion, both of which likely restrict primary production in the epilimnion. Owing to the slow exchange, steep concentration gradients in Fe and Mn species develop in the metalimnion. These concentration gradients are conducive to the proliferation of chemoautotrophic and anoxygenic phototrophic microbial communities, which may contribute a significant fraction to the total primary production in the lake.
Dobrava virus as a new hantavirus: Evidenced by comparative sequence analysis

Dobrava virus, recently isolated from a yellow-neck mouse (Apodemus flavicollis), captured in a northern Slovenian village where severe cases of hemorrhagic fever with renal syndrome were recognized, was shown by serology and restriction enzyme digestion of PCR-amplified gene segments to be related to previously recognized hantaviruses. To investigate further the relationship of this new isolate to other hantaviruses, a portion of the medium (M) genome segment of Dobrava virus was amplified by PCR and the nucleotide sequence determined. Comparing the nucleotide sequence with the same gene region of other hantaviruses revealed an overall homology of 41.7%. A phylogenetic tree based on pairwise sequence similarity clearly showed that Dobrava virus is genetically distinct, and probably represents a new virus in the genus Hantavirus of the family Bunyaviridae.
Extracting Keyphrases from Chinese News Articles Using TextRank and Query Log Knowledge

Keyphrases extracted from articles are beneficial in helping people boost brows- ing speed, but unfortunately keyphrases are rarely available for news articles due to the high expense of labor and time for manual annotation. This paper proposes a practical approach to extracting keyphrases for Chinese news articles using the TextRank and query log knowl- edge. Previous work is word based, while our approach uses phrase as its basic element. We generate phrases by employing several statistical criteria with the huge amount of queries as a training corpus. We use TextRank, a graph-based learning algorithm, for extracting keyphrases from Chinese news articles. In addition, two instructive features, lengths and positions of phrases, are incorporated into the TextRank model. Experimental results demon- strate that our methods improve the performance significantly.