Miracle-dz/UniFAR-FTUs huggingface dataset:
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights

Anomaly detection is of paramount importance in many real-world domains characterized by evolving behavior, such as monitoring cyber-physical systems, human conditions and network traffic. Current research in anomaly detection leverages offline learning working with static data or online learning focusing on constant adaptation to evolving data. At the same time, lifelong learning represents an emerging trend, answering the need for machine learning models that continuously adapt to new challenges in dynamic environments while retaining past knowledge. Although this aspect could be beneficial to build effective and robust anomaly detection models, lifelong learning research is mainly dedicated to proposing new model update strategies in image classification and reinforcement learning domains. The limited scope addressed by lifelong learning works thus far creates a gap in understanding whether such techniques and capabilities can be fruitfully exploited in anomaly detection contexts, which represents the main motivation of this paper. More specifically, anomaly detection provides unique challenges, such as an evolving normal class and limited availability of anomalies, which significantly differs from the landscape and scenarios of lifelong image classification and reinforcement learning. In this paper, we face this issue by exploring, motivating, and discussing lifelong anomaly detection, as well as providing foundations with regard to scenarios, strategies, and metrics. First, we explain why lifelong anomaly detection is relevant, defining challenges and opportunities to design anomaly detection methods that deal with lifelong learning complexities. Second, we formulate and characterize lifelong learning settings tailored for anomaly detection problems, and design a scenario generation procedure that enables researchers to experiment with lifelong anomaly detection using existing datasets. Third, we perform experiments with popular anomaly detection methods on proposed lifelong scenarios, emphasizing the gap in performance that could be filled with the adoption of lifelong learning. In summary, our efforts are directed at assessing the performance of non-lifelong anomaly detection models in lifelong scenarios and how the adoption of lifelong learning impacts their learning capabilities. Overall, we conclude that the adoption of lifelong anomaly detection is important to design more robust models that provide a comprehensive view of the environment, as well as simultaneous adaptation and knowledge retention.
Entropy based camera control for visual object tracking

In active visual 3D object tracking, one goal is to control the pan and tilt axes of the involved cameras to keep the tracked object in the centers of the fields of view. We present a novel method, based on an information theoretic measure, that manages this task. The main advantage of the proposed approach is that there is no need for an explicit formulation of a camera controller, such as a PID-controller or something similar. For the case of Kalman filter based tracking, we demonstrate the practicability and evaluate the accuracy of the proposed method in simulations as well as in real-time tracking experiments.
Mechanism for Recognition of an Unusual Mycobacterial Glycolipid by the Macrophage Receptor Mincle*

Background: Mincle facilitates establishment of persistent infections of macrophages by Mycobacterium tuberculosis. Results: The mechanism of mincle binding to mycobacterial glycolipids has been defined, and inhibitors have been synthesized. Conclusion: Mincle binds to both the sugar portion of the glycolipid and the hydrocarbon tail. Significance: The results suggest ways to manipulate the response to mycobacteria and to improve adjuvants that stimulate the immune system. Binding of the macrophage lectin mincle to trehalose dimycolate, a key glycolipid virulence factor on the surface of Mycobacterium tuberculosis and Mycobacterium bovis, initiates responses that can lead both to toxicity and to protection of these pathogens from destruction. Crystallographic structural analysis, site-directed mutagenesis, and binding studies with glycolipid mimics have been used to define an extended binding site in the C-type carbohydrate recognition domain (CRD) of bovine mincle that encompasses both the headgroup and a portion of the attached acyl chains. One glucose residue of the trehalose Glcα1–1Glcα headgroup is liganded to a Ca2+ in a manner common to many C-type CRDs, whereas the second glucose residue is accommodated in a novel secondary binding site. The additional contacts in the secondary site lead to a 36-fold higher affinity for trehalose compared with glucose. An adjacent hydrophobic groove, not seen in other C-type CRDs, provides a docking site for one of the acyl chains attached to the trehalose, which can be targeted with small molecule analogs of trehalose dimycolate that bind with 52-fold higher affinity than trehalose. The data demonstrate how mincle bridges between the surfaces of the macrophage and the mycobacterium and suggest the possibility of disrupting this interaction. In addition, the results may provide a basis for design of adjuvants that mimic the ability of mycobacteria to stimulate a response to immunization that can be employed in vaccine development.
Recapitulation of developmental mechanisms to revascularize the ischemic heart

Restoring blood flow after myocardial infarction (MI) is essential for survival of existing and newly regenerated tissue. Endogenous vascular repair processes are deployed following injury but are poorly understood. We sought to determine whether developmental mechanisms of coronary vessel formation are intrinsically reactivated in the adult mouse after MI. Using pulse-chase genetic lineage tracing, we establish that de novo vessel formation constitutes a substantial component of the neovascular response, with apparent cellular contributions from the endocardium and coronary sinus. The adult heart reverts to its former hypertrabeculated state and repeats the process of compaction, which may facilitate endocardium-derived neovascularization. The capacity for angiogenic sprouting of the coronary sinus vein, the adult derivative of the sinus venosus, may also reflect its embryonic origin. The quiescent epicardium is reactivated and, while direct cellular contribution to new vessels is minimal, it supports the directional expansion of the neovessel network toward the infarcted myocardium. Thymosin β4, a peptide with roles in vascular development, was required for endocardial compaction, epicardial vessel expansion, and smooth muscle cell recruitment. Insight into pathways that regulate endogenous vascular repair, drawing on comparisons with development, may reveal novel targets for therapeutically enhancing neovascularization.
Journal of Geophysical Research: Space Physics Near-Earth Solar Wind: Plasma Characteristics From ARTEMIS Measurements

The interaction between the solar wind and Earth’s magnetosphere is the main driver of magnetosphere dynamics because many important processes in Earth’s inner and outer magnetosphere can be considered as responses to solar wind parameter variations. Therefore, accurate measurements of these parameters (density, velocity, ion, and electron temperature) in the near-Earth solar wind is critical for understanding and forecasting magnetosphere dynamics. Moreover, the solar wind is a natural laboratory for investigation of plasma turbulence, including the configuration and dynamics of coherent plasma structures (such as large-amplitude waves, discontinuities, and interplanetary shocks). A major solar wind parameter database, the OMNI database, consists of solar wind proton characteristics and magnetic field parameters but does not provide electron characteristics. We consider a supplementary solar wind data set compiled by observations of the two Acceleration, Reconnection, Turbulence and Electrodynamics of the Moon’s Interaction with the Sun (ARTEMIS) probes. We compare characteristics available in both the ARTEMIS and OMNI data sets. We also describe characteristics of solar wind electron components measured by ARTEMIS (density, temperature, and anisotropy). Finally, we discuss opportunities for investigation of the near-Earth solar wind provided by the ARTEMIS data set.
Concept-Driven Generation of Intuitive Explanations of Program Execution for a Visual Tutor

Learning a programming language is hard. Students need to acquire three types of skills: (1) understand the new language concepts, (2) interpret pieces of code that use those concepts, and (3) write pieces of code involving those concepts. In this paper, we present an approach to help with the second type of skill. There are tools that explain/visualize the execution by stepping through the code. However, such tools suffer from two types of problems. First, the granularity of the stepping is coarse, hiding the intermediate steps in evaluating expressions. Second, for a single statement corresponding to a step of the execution, the order of the evaluation of source code constructs is not aligned with the order the constructs appear in the source code. To fix those, we combine compile-time with run-time information to automatically produce intuitive explanations of code. At compile time, we generate the explanations by traversing the AST. The runtime information provides the execution map and runtime values. We applied our idea to the Java version of Online Python Tutor, a web-based program visualization tool. Each explanation is complemented by highlighting the piece of the source code to which it corresponds, being spoken by the system, and a tree structure visualizing the evaluation of the involved expressions. The fact that the generation of explanations is syntax driven makes the result close to what a human tutor would provide.
Blocking effect reduction of JPEG images by signal adaptive filtering

A postprocessing algorithm is proposed to reduce the blocking artifacts of Joint Photographic Experts Group (JPEG) decompressed images. The reconstructed images from JPEG compression produce noticeable image degradation near the block boundaries, in particular for highly compressed images, because each block is transformed and quantized independently. The blocking effects are classified into three types of noises in this paper: grid noise, staircase noise, and corner outlier. The proposed postprocessing algorithm, which consists of three stages, reduces these blocking artifacts efficiently. A comparison study between the proposed algorithm and other postprocessing algorithms is made by computer simulation with several JPEG images.
Consistent Covariance Selection From Data With Missing Values

We study a simple two step procedure for estimating sparse precision matrices from data with missing values, which is tractable in high-dimensions and does not require imputation of the missing values. We provide rates of convergence for this estimator in the spectral norm, Frobenius norm and element-wise `∞ norm. Simulation studies show that this estimator compares favorably with the EM algorithm. Our results have important practical consequences as they show that standard tools for estimating sparse precision matrices can be used when data contains missing values, without resorting to the iterative EM algorithm that can be slow to converge in practice for large problems.
Geographic Information System-Based Suitability Analysis for Potential Shallow Tube-Well Irrigation Development

This study developed a geographic information system (GIS)-based decision support system in the spatial identification of a viable location for shallow tube-well irrigation development in the province of Isabela. Factors such as slope, soil type; land cover and groundwater depth were used as input parameters. Around 98% of the existing shallow tube-wells were within the suitable area. The developed map was validated using existing irrigated areas. Results show that more than 377,000 hectares were found potentially suitable locations for shallow tube-well irrigation development. These areas are characterized with a water table depth of <6.5 m, a slope of<18%, soil textures (sandy loam, clay loam, and sandy clay). Sufficient factors in combination with the GIS tool is a great tool to carry out spatial data in land-use analysis and the use of this as a means in identifying viable locations for shallow tube-well irrigation development in other places is reliable.
The Long Non-Coding HOTAIR Is Modulated by Cyclic Stretch and WNT/β-CATENIN in Human Aortic Valve Cells and Is a Novel Repressor of Calcification Genes

Aortic valve calcification is a significant and serious clinical problem for which there are no effective medical treatments. Individuals born with bicuspid aortic valves, 1–2% of the population, are at the highest risk of developing aortic valve calcification. Aortic valve calcification involves increased expression of calcification and inflammatory genes. Bicuspid aortic valve leaflets experience increased biomechanical strain as compared to normal tricuspid aortic valves. The molecular pathogenesis involved in the calcification of BAVs are not well understood, especially the molecular response to mechanical stretch. HOTAIR is a long non-coding RNA (lncRNA) that has been implicated with cancer but has not been studied in cardiac disease. We have found that HOTAIR levels are decreased in BAVs and in human aortic interstitial cells (AVICs) exposed to cyclic stretch. Reducing HOTAIR levels via siRNA in AVICs results in increased expression of calcification genes. Our data suggest that β-CATENIN is a stretch responsive signaling pathway that represses HOTAIR. This is the first report demonstrating that HOTAIR is mechanoresponsive and repressed by WNT β-CATENIN signaling. These findings provide novel evidence that HOTAIR is involved in aortic valve calcification.
Conceptual versus monolingual scoring: when does it make a difference?

PURPOSE This study evaluates the extent to which bilingual children produce the same or overlapping responses on tasks assessing semantic skills in each of their languages and whether classification analysis based on monolingual or conceptual scoring can accurately classify the semantic development of typically developing (TD) bilingual children. METHOD In Study 1, 55 TD children (ages 4;0 [years;months] to 7;11) from bilingual backgrounds named characteristic properties of familiar items. The extent to which children produced overlapping responses in each of their languages and their errors were examined. In Study 2, 40 TD children (ages 5;0 to 6;1), group matched for age and bilingual language exposure, responded to the Phase 2 version of the Bilingual English Spanish Assessment (BESA; E. D. Peña, V. Gutierrez-Clellen, A. Iglesias, B. A. Goldstein, and L. M. Bedore, in development). Conceptual and monolingual scores were compared to determine the extent to which were comparable for groups of children. RESULTS The results of Study 1 indicated that TD children from bilingual backgrounds are more likely to produce unique than overlapping responses when they respond to test items. Children were more likely to code switch when tested in Spanish than in English, but they were more likely to produce errors in English. In Study 2, monolingual and bilingual children achieved comparable conceptual scores. For Spanish-speaking bilingual children, the conceptual score was more likely to be in the average range of the monolingual children than was their monolingual score. For testing in English, monolingual and conceptual scores were similar. CLINICAL IMPLICATIONS Bilingual children will benefit from conceptual scoring, especially when they are tested in Spanish.
The Formation of Straight and Twisted Filaments from Short Tau Peptides*

We studied fibril formation in a family of peptides based on PHF6 (VQIVYK), a short peptide segment found in the microtubule binding region of tau protein. N-Acetylated peptides AcVYK-amide (AcVYK), AcIVYK-amide (AcPHF4), AcQIVYK-amide (AcPHF5), and AcV-QIVYK-amide (AcPHF6) rapidly formed straight filaments in the presence of 0.15 m NaCl, each composed of two laterally aligned protofilaments ∼5 nm in width. X-ray fiber diffraction showed the omnipresent sharp 4.7-Å reflection indicating that the scattering objects are likely elongated along the hydrogen-bonding direction in a cross-β conformation, and Fourier transform IR suggested the peptide chains were in a parallel (AcVYK, AcPHF6) or antiparallel (AcPHF4, AcPHF5) β-sheet configuration. The dipeptide N-acetyl-YK-amide (AcYK) formed globular structures ∼200 nm to 1 μm in diameter. The polymerization rate, as measured by thioflavin S binding, increased with the length of the peptide going from AcYK → AcPHF6, and peptides that aggregated most rapidly displayed CD spectra consistent with β-sheet structure. There was a 3-fold decrease in rate when Val was substituted for Ile or Gln, nearly a 10-fold decrease when Ala was substituted for Tyr, and an increase in polymerization rate when Glu was substituted for Lys. Twisted filaments, composed of four laterally aligned protofilaments (9–19 nm width, ∼90 nm half-periodicity), were formed by mixing AcPHF6 with AcVYK. Taken together these results suggest that the core of PHF6 is localized at VYK, and the interaction between small amphiphilic segments of tau may initiate nucleation and lead to filaments displaying paired helical filament morphology.
Mutation Prevalence of Cerebral Cavernous Malformation Genes in Spanish Patients

Objective To study the molecular genetic and clinical features of cerebral cavernous malformations (CCM) in a cohort of Spanish patients. Methods We analyzed the CCM1, CCM2, and CCM3 genes by MLPA and direct sequencing of exons and intronic boundaries in 94 familial forms and 41 sporadic cases of CCM patients of Spanish extraction. When available, RNA studies were performed seeking for alternative or cryptic splicing. Results A total of 26 pathogenic mutations, 22 of which predict truncated proteins, were identified in 29 familial forms and in three sporadic cases. The repertoire includes six novel non-sense and frameshift mutations in CCM1 and CCM3. We also found four missense mutations, one of them located at the third NPXY motif of CCM1 and another one that leads to cryptic splicing of CCM1 exon 6. We found four genomic deletions with the loss of the whole CCM2 gene in one patient and a partial loss of CCM1and CCM2 genes in three other patients. Four families had mutations in CCM3. The results include a high frequency of intronic variants, although most of them localize out of consensus splicing sequences. The main symptoms associated to clinical debut consisted of cerebral haemorrhage, migraines and epileptic seizures. The rare co-occurrence of CCM with Noonan and Chiari syndromes and delayed menarche is reported. Conclusions Analysis of CCM genes by sequencing and MLPA has detected mutations in almost 35% of a Spanish cohort (36% of familial cases and 10% of sporadic patients). The results include 13 new mutations of CCM genes and the main clinical symptoms that deserves consideration in molecular diagnosis and genetic counselling of cerebral cavernous malformations.
B2B-Swipe: Swipe Gesture for Rectangular Smartwatches from a Bezel to a Bezel

We present B2B-Swipe, a single-finger swipe gesture for a rectangular smartwatch that starts at a bezel and ends at a bezel to enrich input vocabulary. There are 16 possible B2B-Swipes because a rectangular smartwatch has four bezels. Moreover, B2B-Swipe can be implemented with a single-touch screen with no additional hardware. Our study shows that B2B-Swipe can co-exist with Bezel Swipe and Flick, with an error rate of 3.7% under the sighted condition and 8.0% under the eyes-free condition. Furthermore, B2B-Swipe is potentially accurate (i.e., the error rates were 0% and 0.6% under the sighted and eyes-free conditions) if the system uses only B2B-Swipes for touch gestures.
2016 Ieee International Conference on Big Data (big Data) Analysis of Teamwork Dialogue: a Data Mining Approach

—With the advent of the Internet and widespread popularity of online technology-enhanced learning platforms, many pedagogical activities today involve learners in online discussions such as synchronous chat. In this study, we describe a text mining method used for analyzing teamwork from such chat dialogue of students. The steps in the text mining method such as pre-processing and classification are described and the results of our analysis are presented in this paper. I. BACKGROUND & MOTIVATION In today's technology enhanced learning environment, learners experience pedagogical activities involving asynchronous chat conversations in online discussions. Prior studies [1] have shown that online team chat conversations tend to increase learners' participation. Further, it has shown that participation is more evenly distributed in online discussions. Visual-oriented learners of foreign languages particularly welcome such online discourse [2]. In today's global, complex and highly dynamic world, aside from developing the academic competency of learners, it is also important to nurture their skills and competencies in teamwork and collaboration [3]. Hence, on top of evaluating the academic competency, it is important to examine and evaluate the teamwork discourse of learners. By analyzing teamwork dialogue, researchers and educators can attain better understanding of learners' teamwork competency [4, 5]. Prior research in the field examined online discourse from manual coding schemes and ratings, to computational linguistics and machine learning techniques. In particular, training models involving human evaluation (using computational linguistics and text classification) show most promises for efficiency and scalability as these models use computers to code and analyze the data (as opposed to slower and less scalable human effort); it can also be developed to examine different kinds of text and large amounts of text [6, 7]. Automated text analysis of chat texts meets challenges such as many irregularities in structure, short lengths and contextual complexities in online chat conversations. Prior research work in this regard suggests that pre-processing text to organize and identify desired features would be helpful to this text analysis. Pre-processing is a critical first step in analyzing and understanding how learners work together as a team which will be described in this study. Further, we evaluate several supervised machine learning algorithms for classifying the text to teamwork dimensions. This study is from a teamwork competency awareness program designed for 14 year old students in a Singapore secondary school. In one section of the program, student teams of sizes 3 to 4 participated in a collaborative …