Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyclical Log Annealing as a Learning Rate Scheduler

A learning rate scheduler is a predefined set of instructions for varying search stepsizes during model training processes. This paper introduces a new logarithmic method using harsh restarting of step sizes through stochastic gradient descent. Cyclical log annealing implements the restart pattern more aggressively to maybe allow the usage of more greedy algorithms on the online convex optimization framework. The algorithm was tested on the CIFAR-10 image datasets, and seemed to perform analogously with cosine annealing on large transformer-enhanced residual neural networks. Future experiments would involve testing the scheduler in generative adversarial networks and finding the best parameters for the scheduler with more experiments.
Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future

As Natural Language Processing (NLP) systems become increasingly integrated into human social life, these technologies will need to increasingly rely on social intelligence. Although there are many valuable datasets that benchmark isolated dimensions of social intelligence, there does not yet exist any body of work to join these threads into a cohesive subfield in which researchers can quickly identify research gaps and future directions. Towards this goal, we build a Social AI Data Infrastructure, which consists of a comprehensive social AI taxonomy and a data library of 480 NLP datasets. Our infrastructure allows us to analyze existing dataset efforts, and also evaluate language models' performance in different social intelligence aspects. Our analyses demonstrate its utility in enabling a thorough understanding of current data landscape and providing a holistic perspective on potential directions for future dataset development. We show there is a need for multifaceted datasets, increased diversity in language and culture, more long-tailed social situations, and more interactive data in future social intelligence data efforts.
Outcome Preferences in Patients With Noninfectious Uveitis: Results of a Best-Worst Scaling Study.

PURPOSE To estimate patient preferences regarding potential adverse outcomes of local versus systemic corticosteroid therapies for noninfectious uveitis by using a best-worst scaling (BWS) approach. METHODS Local and systemic therapies are alternatives for noninfectious uveitis that have different potential adverse outcomes. Patients participating in the Multicenter Uveitis Steroid Treatment Trial Follow-up Study (MUST FS) and additional patients with a history of noninfectious uveitis treated at two academic medical centers (Johns Hopkins University and University of Pennsylvania) were surveyed about their preferences regarding six adverse outcomes deemed important to patients. Using "case 1" BWS, patients were asked to repeatedly select the most and least worrying from a list of outcomes (in the survey three outcomes per task). RESULTS Eighty-two patients in the MUST FS and 100 patients treated at the academic medical centers completed the survey. According to BWS, patients were more likely to select vision not meeting the requirement for driving (individual BWS score: median = 3, interquartile range, 0-5), development of glaucoma (2, 1-4), and needing eye surgery (1, 0-3) as the most worrying outcomes as compared to needing medicine for high blood pressure/cholesterol (-2, -4 to 0), development of cataracts (-2, -3 to -1), or infection (sinusitis) (-3, -5 to 0). Larger BWS scores indicated the outcomes were more worrying to patients. CONCLUSIONS Patients with noninfectious uveitis considered impaired vision, development of glaucoma, and need for eye surgery worrying adverse outcomes, which suggests that it is especially desirable to avoid these outcomes if possible. (ClinicalTrials.gov number, NCT00132691.)
A genetic algorithm for the identification of conformationally invariant regions in protein molecules.

Understanding macromolecular function often relies on the comparison of different structural models of a molecule. In such a comparative analysis, the identification of the part of the molecule that is conformationally invariant with respect to a set of conformers is a critical step, as the corresponding subset of atoms constitutes the reference for subsequent analysis for example by least-squares superposition. A method is presented that categorizes atoms in a molecule as either conformationally invariant or flexible by automatic analysis of an ensemble of conformers (e.g. crystal structures from different crystal forms or molecules related by non-crystallographic symmetry). Different levels of coordinate precision, both for different models and for individual atoms, are taken explicitly into account via a modified form of Cruickshank's DPI [Cruickshank (1999), Acta Cryst. D55, 583-601] and are propagated into error-scaled difference distance matrices [Schneider (2000), Acta Cryst. D56, 715-721]. All pairwise error-scaled difference distance matrices are then analysed simultaneously using a genetic algorithm. The algorithm has been tested on several well known examples and has been found to converge rapidly to reasonable results using a standard set of parameters. In addition to the description of the algorithm, a criterion is suggested for testing the identity of two three-dimensional models within experimental error without any explicit superposition.
End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear MPC

(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic system models that are sufficiently accurate in all relevant state-space regions. These models must also be computationally cheap enough to ensure real-time tractability. Data-driven surrogate models for mechanistic models can be used to reduce the computational burden of (e)NMPC; however, such models are typically trained by system identification for maximum average prediction accuracy on simulation samples and perform suboptimally as part of actual (e)NMPC. We present a method for end-to-end reinforcement learning of dynamic surrogate models for optimal performance in (e)NMPC applications, resulting in predictive controllers that strike a favorable balance between control performance and computational demand. We validate our method on two applications derived from an established nonlinear continuous stirred-tank reactor model. We compare the controller performance to that of MPCs utilizing models trained by the prevailing maximum prediction accuracy paradigm, and model-free neural network controllers trained using reinforcement learning. We show that our method matches the performance of the model-free neural network controllers while consistently outperforming models derived from system identification. Additionally, we show that the MPC policies can react to changes in the control setting without retraining.
Minimum segmentation error based discriminative training for speech synthesis application

In the conventional HMM-based segmentation method, the HMM training is based on MLE criteria, which links the segmentation task to the problem of distribution estimation. The HMM are built to identify the phonetic segments, not to detect the boundary. This kind of inconsistency between training and application limited the performance of segmentation. In this paper, we adopt the discriminative training method and introduce a new criterion, named minimum segmentation error (MSGE), for HMM training. In this method, a loss function directly related to the segmentation error is defined. By minimizing the overall empirical loss with the generalized probabilistic descent (GPD) algorithm, the segmentation error is also minimized. From the results on both Chinese and Japanese data, the accuracy of segmentation is improved. Moreover, this method is robust even when we do not have enough knowledge on HMM modeling, e.g. the number of states is not optimized.
Comparison of two assay methods for patterns of adherence to HEp-2 cells of Escherichia coli from patients with diarrhea

To determine whether methodological differences in the HEp-2 adherence assay could explain conflicting results of field studies, 244 strains of Escherichia coli from Mexican children with diarrhea were tested for patterns of adherence by the method used at the Center for Vaccine Development, University of Maryland (CVD), and at the Center for Infectious Diseases, University of Texas Medical School and School of Public Health (UTH). The CVD assay differentiated three phenotypes of adherent E. coli, including localized, diffuse, or aggregative adherence (LA, DA, or AA, respectively). There was agreement on pattern of adherence in 241 of the 244 strains (98.8%) tested by the CVD method in both Baltimore and Houston, and AA+ was the most common phenotype (28.5% of isolates). Among these isolates, the UTH assay detected only two adherent phenotypes (LA and DA), since it did not distinguish the AA pattern. The LA+ strains detected by each assay were compared for positivity with the enteropathogenic E. coli adherence factor (EAF) gene probe. Of the 16 strains LA+ by the CVD method, 100% were EAF+; in contrast, only 11 of 22 strains LA+ by the UTH method were EAF+ (P = 0.00074). These results help explain why in pediatric field studies in Mexico where isolates were tested by the UTH method (J. J. Mathewson, R. A. Oberhelman, H. L. Dupont, F. J. de la Cabada, and E. V. Garibay, J. Clin. Microbiol. 25:1917-1919, 1987) LA+ strains often did not belong to enteropathogenic E. coli O serogroups and why the AA pattern was not observed; the opposite was found in studies of pediatric diarrhea in Chile in which the CVD assay was used (M. M. Levine, V. Prado, R. M. Robins-Browne, H. Lior, J. B. Kaper, S. Moseley, K. Gicquelais, J. P. Nataro, P. Vial, and B. Tall, J. Infect. Dis. 158:224-228, 1988). Since it appears that both assays identify E. coli strains associated with diarrheal illness, the genetic relationships among these strains should be examined in future studies.
Enhanced Working Memory Representations for Rare Events

Rare events (oddballs) produce a variety of enhanced physiological responses relative to frequent events (standards), including the P3b component of the event-related potential (ERP) waveform. Previous research has suggested that the P3b component is related to working memory, which implies that working memory representations will be enhanced for rare stimuli. To test this hypothesis, we devised a modified oddball paradigm in which the target was a disk presented at one of 16 different locations, which were divided into a rare set and a frequent set. Participants made a binary response on each trial to report whether the target appeared in the rare set or the frequent set. As expected, the P3b was much larger for stimuli appearing at a location within the rare set. We also included occasional probe trials in which the subject reported the exact location of the target. We found that these reports were more accurate for locations within the rare set than for locations within the frequent set. Moreover, the mean accuracy of these reports was correlated with the mean amplitude of the P3b. We also applied multivariate pattern analysis to the ERP data to “decode” the remembered location of the target. Decoding accuracy was greater for locations within the rare set than for locations within the frequent set. These behavioral and electrophysiological results demonstrate that although both frequent and rare events are stored in working memory, the representations are enhanced for rare events. Significance Statement For many decades, researchers have observed that rare events elicit a broad range of physiological responses, and there has been much speculation about the functional significance of these responses. One such response is the P3b component, which is a large voltage deflection in scalp EEG recordings. Over 40 years ago, the P3b was hypothesized to reflect “context updating” (now often called “working memory updating”). However, there has been no direct evidence that working memory is actually enhanced for rare, P3b-eliciting events. In the present study, we found that both behavioral and electrophysiological measures of working memory were enhanced for rare events. This is potentially related to the release of norepinephrine across the cortex.
The autophagy protein, ATG14 safeguards against unscheduled pyroptosis activation to enable embryo transport during early pregnancy

Recurrent pregnancy loss (RPL), characterized by two or more failed clinical pregnancies, poses a significant challenge to reproductive health. In addition to embryo quality and endometrial function, proper oviduct function is also essential for successful pregnancy establishment. Therefore, structural abnormalities or inflammation resulting from infection in the oviduct may impede the transport of embryos to the endometrium, thereby increasing the risk of miscarriage. However, the precise cellular mechanisms that maintain the structural and functional integrity of the oviduct are not studied yet. Here, we report that autophagy is critical for maintaining the oviduct homeostasis and keeping the inflammation under check to enable embryo transport. Specifically, the loss of the autophagy-related gene, Atg14 in the oviduct causes severe structural abnormalities compromising its cellular plasticity and integrity leading to the retention of embryos. Interestingly, the selective loss of Atg14 in oviduct ciliary epithelial cells did not impact female fertility, highlighting the specificity of ATG14 function in distinct cell types within the oviduct. Mechanistically, loss of Atg14 triggered unscheduled pyroptosis leading to inappropriate embryo retention and impeded embryo transport in the oviduct. Finally, pharmacological activation of pyroptosis in pregnant mice led to an impairment in embryo transport. Together, we found that ATG14 safeguards against unscheduled pyroptosis activation to enable embryo transport from the oviduct to uterus for the successful implantation. Of clinical significance, these findings provide possible insights on the underlying mechanism(s) of early pregnancy loss and might aid in developing novel prevention strategies using autophagy modulators.
Syntactic Information Retrieval

Natural language processing (NLP) techniques are believed to have the potential to aid information retrieval (IR) in terms of retrieval accuracy. In this paper we report a proof of concept study on a new approach to NLP-based IR that we propose. Documents and queries are represented as syntactic parse trees, which are generated by a natural language parser. Based on this tree structured representation of documents and queries, the matching between a document and a query is executed on their tree representations, with tree comparison as the key operation. An IR experiment is designed to test if this approach is feasible. Experimental results show that this approach is promising and has the potential to outperform the standard bag of words approach to information retrieval, especially in response to long queries.
Methodologies : ORM and NIAM Revisited : DOGMA in detail

Formal ontologies can be seen as mathematical objects that form the range of a classical "Tarskian" semantics interpretation mapping of first-order language constructs that could represent situations, functions or procedures related to a given domain. Some design methods and techniques such as view integration that were originally developed for large databases, where the "data models" and their semantics typically are limited to a particular application, could be relevant for this purpose, and we analyze parallelisms and fundamental differences between databases and ontologies. In particular the ORM (Ob jectRole Modeling) method, or rather its precursor NIAM, with its rigorous distinction and handling of so-called lexical and non-lexical knowledge proved to be an interesting candidate to help identify and clarify a number of these issues, and a number of examples are given. We report on research within STARLab’s DOGMA Project that indicates how such methods may or must be adapted to be usable in the context of ontologies, and how they may then help to define ontology updates, the role of domain constraints, and future tools that assist in e.g. the alignment of ontologies.
Modelling on web summarisation based on structure analysis and vectorisation similarity

With the rapid development of the internet, the useful information extraction has become increasingly important. Web summarisation, as the art of abstracting key contents from huge web data, has become an integral part of search engines and digital libraries. As the weighted keywords can be considered as condensed versions of the content, on the basis of the statistics, this paper proposes a novel summarisation approach based on structure analysis and keyword vectorisation similarity. The structure vector space model, the candidate selection and the summarisation generation are also applied in this novel approach. The experimental results show that this approach is feasible. Existing problems and further works are also presented at the end of the paper.
Identification of the zebrafish red nucleus using Wheat Germ Agglutinin transneuronal tracing

The red nucleus is located in the rostral midbrain of the vertebrate brain and controls motor coordination during locomotion. It receives input from the cerebellum and sends its output to the spinal cord. The presence of the red nucleus is well established in tetrapods, and its existence has also been suggested in teleosts but its presence and position has still been under discussion. By using wheat germ agglutinin (WGA) as a genetically encoded anterograde tracer, we recently identified contralateral projections from the cerebellum to a putative red nucleus in the zebrafish midbrain tegmentum. In this report we further revealed red nucleus derived from this contralateral afferent from the cerebellum using WGA and contralateral projections to the hindbrain-spinal cord junction site using DiI-mediated retrograde tracing. Thus the structure that we have identified by anterograde and retrograde tracing fulfills the anatomical demands for the red nucleus: the location in the midbrain tegmentum, contralateral afferent from the cerebellum (cerebello-ruber projection) and contralateral efferent to the spinal cord (rubro-spinal projection).
GIS- and RS-based investment projects information system for National Bureau of Statistics

The investment in fixed assets has shown a sustained and rapid growth in China. There is an urgent need for statistics department to establish a stable and efficient fixed asset investment Projects monitoring and management information system. On the basis of in-depth analyze on statistical business process, the details of system framework, database design, and the function modules are described. A set of key technologies were proposed and researched on, include investment projects identification and information extraction from remote sensing image, change detection from multi-temporal remote sensing image and quick browse of massive remote sensing data based on multi-resolution image pyramid structure. The system is put into effect in National Bureau of Statistics of China (NBS) and has scored satisfied both economic and social benefits.
Identification of Annotations for Circuit Symbols in Electrical Diagrams of Document Images

Though annotations are integral part of symbols in drawings, due attention is yet to be given for their identification, interpretation, and storage. A reconstructed drawing from the vector format is thus deficient in the complete description of the original, and hence requires costly and time-consuming manual intervention. This paper presents a method for segmentation and identification of annotations associated with electrical symbols in a circuit diagram, which may be used with the vectorizer to make it complete. The proposed method first separates the text region around an intended circuit symbol, and then identifies the annotation part of the segmented text corresponding to that particular symbol. Morphological operations are used in identification phase. Finally, an efficient OCR is used to get the numerical values of the symbols along with their units. The performance of the algorithm is tested on a variety of images with ample variations in annotation. Some of the results are presented in this paper to demonstrate its efficiency.
Faster ﬂoating-point square root for integer processors

—This paper presents some work in progress on fast and accurate ﬂoating-point arithmetic software for ST200-based embedded systems. We show how to use some key architectural features to design codes that achieve correct rounding-to-nearest without sacriﬁcing for efﬁciency. This is illustrated with the square root function, whose implementation given here is faster by over 35 % than the previously best one for such systems.
Approximation Algorithms for Rectangle Stabbing and Interval Stabbing Problems

In the weighted rectangle stabbing problem we are given a grid in $\mathbb{R}^2$ consisting of columns and rows each having a positive integral weight, and a set of closed axis-parallel rectangles each having a positive integral demand. The rectangles are placed arbitrarily in the grid with the only assumption being that each rectangle is intersected by at least one column or row. The objective is to find a minimum-weight (multi)set of columns and rows of the grid so that for each rectangle the total multiplicity of selected columns and rows stabbing it is at least its demand. A special case of this problem, called the interval stabbing problem, arises when each rectangle is intersected by exactly one row. We describe an algorithm called STAB, which is shown to be a constant-factor approximation algorithm for different variants of this stabbing problem.
Active Learning via Sequential Design with Applications to Detection of Money Laundering

Money laundering is an act to hide the true origin of funds by sending them through a series of seemingly legitimate transactions. Because it often involves criminal activities, ﬁnancial institutions have the responsibility to detect and inform about them to the appropriate government agencies in a timely manner. However, detecting money laundering is not an easy job because of the huge number of transactions that take place each day. The usual approach adopted by ﬁnancial institutions is to extract some sum-mary statistics from the transaction history and do a thorough and time-consuming investigation on those accounts that appear to be suspicious. In this article, we propose an active learning method using Bayesian sequential designs to identify the suspicious accounts. The method uses a combination of stochastic approximation and D -optimal designs to judiciously select the accounts for investigation. The sequential nature of the method helps to identify the suspicious accounts with minimal time and eﬀort. A case study with real banking data is used to demonstrate the performance of the proposed method. A simulation study shows the eﬃciency and accuracy of the proposed method, as well as its robustness to model assumptions.
A Sentiment Polarity Analyser based on a Lexical-Probabilistic Approach

In this paper, we propose an unsupervised approach to automatically classify the sentiment polarity of texts that can be documents or tweets related to the user’s favorite hashtags. The system is based on a combination of probabilistic and lexicon-based approaches. We first apply the Latent Dirichlet Allocation (LDA) model to discover two vectors of terms relevant for two topics (presumably positive and negative) and then we calculate the polarity of the associated sentiment using the SentiWordnet resource. Experiments have been conducted first on an English dataset and then the system has been associated to an application and tested for Italian. Results show that the system can partition the polarity with a good accuracy.