Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Learning for Hardware and Software Conﬁguration Co-Optimization

—Both hardware and software systems are increasingly conﬁgurable, which poses a challenge to ﬁnding the highest performance conﬁguration due to the tremendous search space. Prior scheduling and resource management work uses machine learning (ML) to ﬁnd high-performance conﬁgurations for either the hardware or software conﬁgurations alone while assuming the other is ﬁxed. Such separate optimization is problematic because a software conﬁguration that is fast on one hardware architecture can be up to 50% slower on another. The main di ﬃ culty of co-optimization is the massive search space over both hardware and software conﬁgurations because accurate learning models require large amounts of labeled training data, and thus long periods of data collection and conﬁguration search. To achieve co-optimization with signiﬁcantly smaller training sets, we present Paprika, a scheduler that simultaneously selects hardware and software conﬁgurations using a Bayesian learning approach. Paprika augments software conﬁguration parameters with hardware features seamlessly and e ﬃ ciently via one-hot encoding and parameter selection. To reduce the impact of the search space, Paprika actively queries conﬁgurations based on a novel ensemble optimization objective. The intuition of this ensemble is to combine three prior optimizers to implicitly negotiate the exploration–exploitation tradeo ﬀ . We evaluate Paprika with ten Spark workloads on three hardware architectures and ﬁnd that, compared to prior work, Paprika produces runtimes that are 12–38% closer to the optimal.
The Factors’ Contribution in the Start-Up Process and Success of Tunisian Cyberentreprises

-The present study focuses on the relationships between entrepreneurial characteristics (Training, success-oriented trend, control locus), Strategic and managerial factors (E. networking, Business model, Management team), Service-quality related (reliability, responsiveness, Site quality), and the success of e-commerce entrepreneurs. An exploratory study has been conducted through semi-structured interviews. The reached results reveal that the factors’, in their entirety, appear to enhance well the studied net-entrepreneurs and explain the success level of the Tunisian cyberentreprises.
Highly Parallel Texts Enriched with Highly Useful Metadata? A Wikipedia Case-Study Combining Machine Learning and Social Technology

This is an un-refereed preprint version of an article accepted for publication in Digital Scholarship in the Humanities . The refereed, revised and copyedited version of record “Ahmad Aghaebrahimian, Andy Stauder, Michael Ustaszewski: Automatically extracted parallel corpora enriched with highly useful metadata? A
Electrooptically Switchable Columnar Liquid Crystals: Modeling and Conjectures

Elements of a theory of electrooptically switchable columnar liquid crystals are developed. Assuming spontaneously polarized columns of circular cross section, we consider their dipolar interaction, a special van der Waals interaction of the aromatic molecular cores, and a mean-field theory of a ferroelectric phase transition. Available X-ray data are tentatively assigned to column lattices of low dipolar energy. The agreement between theory and experiments seems satisfactory, especially when the columnar dipole moments per unit length are split and placed at a distance from the column center. However, a dependence of molecular side-chain packing energies on dipolar orientations, so far unmanageable, may intervene and determine which of the low-energy states is the ground state.
Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches

Abbreviation expansion is an important problem in clinical natural language processing because abbreviations often occur in text notes in medical records, and expansions of these ab-breviations are critical for downstream applications such as assistive diagnosis and insurance code review. Previous studies have treated abbreviation expansion as a special case of word sense disambiguation; however, abbre-viation expansion is easier because we only need the character level expansion and not necessarily the full sense of the abbreviation. In particular, such character level expansions may naturally occur elsewhere in medical contexts. Accordingly, we consider two categories of methods for abbreviation expansion: (a) non-sense-based meth-ods that use information solely at lexical levels using state-of-the-art language models, and (b) sense-based methods that also incorporate sense information, such as glosses, from knowledge bases, to simultaneously perform the two tasks of expansion and disambiguation of the abbreviation. We propose two language model based approaches, including a novel length-agnostic permutation language model, ﬁnd non-sense methods to be more eﬀective than sense-based methods, and achieve the state-of-the-art on three clinical datasets. 1
Predicting the Depression of the South Korean Elderly using SMOTE and an Imbalanced Binary Dataset

Since the number of healthy people is much more than that of ill people, it is highly likely that the problem of imbalanced data will occur when predicting the depression of the elderly living in the community using big data. When raw data are directly analyzed without using supplementary techniques such as a sample algorithm for datasets, which have imbalanced class ratios, it can decrease the performance of machine learning by causing prediction errors in the analysis process. Therefore, it is necessary to use a data sampling technique for overcoming this imbalanced data issue. As a result, this study tried to identify an effective way for processing imbalanced data to develop ensemble-based machine learning by comparing the performance of sampling methods using the depression data of the elderly living in South Korean communities, which had quite imbalanced class ratios. This study developed a model for predicting the depression of the elderly living in the community using a logistic regression model, gradient boosting machine (GBM), and random forest, and compared the accuracy, sensitivity, and specificity of them to evaluate the prediction performance of them. This study analyzed 4,085 elderly people (≥60 years old) living in the community. The depression data of the elderly in the community used in this study had an unbalance issue: the result of the depression screening test showed that 87.5% of subjects did not have depression, while 12.5% of them had depression. This study used oversampling, undersampling, and SMOTE methods to overcome the unbalance problem of the binary dataset, and the prediction performance (accuracy, sensitivity, and specificity) of each sampling method was compared. The results of this study confirmed that the SMOTE-based random forest algorithm showing the highest accuracy (a sensitivity ≥ 0.6 and a specificity ≥ 0.6) was best prediction performance among random forest, GBM, and logistic regression analysis. Further studies are needed to compare the accuracy of SMOTE, undersampling, and oversampling for imbalanced data with high dimensional y-variables.
Modeling SWCNT Band-gap and Effective Mass Variation using a Monte Carlo Approach

Synthesizing Single-Walled Carbon-Nanotubes (SWCNTs) with accurate structural control has been widely acknowledged as an exceedingly complex task culmina ting in the realization of CNT devices with uncertain electroni c behavior. In this paper we apply a statistical approach in predi cting the SWCNT band-gap and effective mass variation for typ ical uncertainties associated with the geometrical struc ture. This is firstly carried out by proposing a simulation-efficient analytical model that evaluates the band-gap ( Eg) of an isolated SWCNT with a specified diameter (d) and chirality (θ). Similarly, we develop a SWCNT effective mass model, which is applicable to CNTs of any chirality and diameters > 1nm. A Monte Carlo method is later adopted to simulate the band-gap an d effective mass variation for a selection of structural parameter distributions. As a result, we establish analytical expressions that separately specify the band-gap and effective mass variability (Egσ, m* σ) with respect to the CNT mean diameter ( dμ) and standard deviation (dσ). These expressions offer insight from a theoretical perspective on the optimization of diameter-related process parameters with the aim of suppressing band -gap and effective mass variation.
A Critical Compilation of Atomic Transition Probabilities for Singly Ionized Argon

We have critically compiled the atomic transition probabilities of Ar ii lines by combining recent high‐accuracy lifetime data with branching‐ratio emission measurements. We present several comparisons of the various literature data, including theoretical results, and we discuss our assessment procedure in detail. On the basis of this procedure, we present an extension list of critically evaluated transition probabilities with uncertainty estimates.
Preparation of Poly(β-malic acid) by Enzymatic Ring-Opening Polymerization of Benzyl β-Malolactonate

Poly(β-malic acid) was prepared by lipase-catalyzed ring-opening polymerization of benzyl β-malolactonate with subsequent debenzylation. Benzyl β-malolactonate was readily polymerized by porcine pancreatic lipase or Novozyme 435 lipase at 60 °C to yield poly(benzyl β-malate) having a molecular weight of greater than 7000.
: Persistent Tracking in Modern Browsers

—The privacy threats of online tracking have garnered considerable attention in recent years from researchers and practitioners. This has resulted in users becoming more privacy-cautious and browsers gradually adopting countermeasures to mitigate certain forms of cookie-based and cookie-less tracking. Nonetheless, the complexity and feature-rich nature of modern browsers often lead to the deployment of seemingly innocuous functionality that can be readily abused by adversaries. In this paper we introduce a novel tracking mechanism that misuses a simple yet ubiquitous browser feature: favicons . In more detail, a website can track users across browsing sessions by storing a tracking identiﬁer as a set of entries in the browser’s dedicated favicon cache, where each entry corresponds to a speciﬁc sub-domain. In subsequent user visits the website can reconstruct the identiﬁer
Content-Centric Computational Cognitive Modeling

That human-level cognitive systems will require a lot of knowledge is common knowledge. However, for a number of reasons, issues of content are not directly addressed at scale by the cognitive systems community. This paper makes the case for the centrality of content-related issues for the long-term goals of cognitive system research. Topics addressed include: the nature and scope of content-centric cognitive modeling (CCM); the juxtaposition of CCM with work on cognitive architectures and cognitive systems; the importance of addressing long-term knowledge needs immediately, since they cannot be met by gradual enhancements in application systems; the ways in which a content-oriented perspective affects design preferences for cognitive architectures; how CCM minimizes the need for heuristic search; and the practice of CCM in the OntoAgent environment.
Study on Gas Permeability in Nano Pores of Shale Gas Reservoirs

Producing gas from shale gas reservoirs has played an increasingly important role in the volatile energy industry over recent years in North America for considerable volume of natural gas stored in the reservoirs. Unlike conventional gas reservoirs, the gas flow in shale reservoirs is a complex multi-scale flow process and has special flow mechanisms. Most importantly, the shale gas reservoir contains a large portion of nano pores. The study of flow in nano pores is essential for accurate shale gas numerical simulation. However, there is still not a comprehensive study in understanding how gas flows in nano pores. In this paper, based on the advection-diffusion model, we constructed a new mathematical model to characterize gas flow in nano pores. We derived a new apparent permeability expression based on advection and Knudsen diffusion. Acomprehensive coefficient in characterizing the flow process was proposed. Simulation results were verified against the experimental data for gas flow through nano membranes. By changing the comprehensive coefficient, we found the best candidate for the case of Argon with membrane pore diameter 235 nm. We verified the model using different gases (Oxygen, Argon) and different pore diameters (235 nm, 220 nm). The comparison shows that the new model matches the experimental data very closely. Additionally, we compared our results with experimental data, Knudsen/Hagen-Poiseuille analytical solution, and existing researcher’s work. The results show that this study yielded a more reliable solution. For shale gas simulation where gas flowing in nano pores plays a critical role, the results from this work will made the simulation more accurate and reliable. Introduction With the growing shortage of domestic and foreign energy, producing gas from shale strata has played an increasingly important role in the volatile energy industry over recent years in North America and is gradually becoming a key component in the world’s energy supply (Wang and Krupnick, 2013). A shale gas reservoir is characterized of an organic-rich deposition with extremely low matrix permeability and clusters of mineral-filled “natural” fractures (Fig.1). Through experiment analysis on 152 cores of nine reservoirs in North America, Javadpour (2009) found that the permeability of shale bedrock is mostly 54 nd and about 90% are less than 150 nd (Javadpour et al. 2007). Most of the pores’ diameter are concentrated in the range of 4 ~ 200 nm (10 m)(Curtis et al. 2010).
Algebraic topological entropy

As a "by-product" of the Connes-Nainhofer-Thirring theory of dynamical entropy for (originally nonAbelian) nuclear C*-algebras, the well-known variational principle for topological entropy is equivalently reformulated in purely algebraically defined terms for (separable) Abelian C*-algebras. This "algebraic variational principle" should not only nicely illustrate the ''feed-back'' of methods developed for quantum dynamical systems to the classical theory, but it could also be proved directly by "algebraic" methods and could thus further simplify the original proof of the variational principle (at least "in principle"). Invited contribution to the International Seminar on Applied Mathematics (ISAM 90) on "Dynamical Systems", Gaussig, GDR.
Transmission Loss of Optical Fibers; Achievements in Half a Century

SUMMARY This paper reviews the evolutionary process that reduced the transmission loss of silica optical fibers from the report of 20 dB/km by Corning in 1970 to the current record-low loss. At an early stage, the main effort was to remove impurities especially hydroxy groups for fibers with GeO 2 -SiO 2 core, resulting in the loss of 0.20 dB/km in 1980. In order to suppress Rayleigh scattering due to composition fluctuation, pure-silica-core fibers were developed, and the loss of 0.154 dB/km was achieved in 1986. As the residual main factor of the loss, Rayleigh scattering due to density fluctuation was actively investigated by utilizing IR and Raman spectroscopy in the 1990s and early 2000s. Now, ultra-low-loss fibers with the loss of 0.150 dB/km are commercially available in trans-oceanic submarine cable systems.
Line search in noisy unconstrained black box optimization

In this paper, a new randomized solver for noisy unconstrained black box optimization, called VRBBON , is discussed. Complexity bounds in the presence of noise for nonconvex, convex, and strongly convex functions are studied. Two eﬀec-tive ingredients of VRBBON are an improved derivative-free line search algorithm with many heuristic enhancements and quadratic models in adaptively determined subspaces. A recommendation is made as to which solvers are robust and eﬃcient based on dimension and noise level. It turns out that VRBBON is more robust and eﬃcient than the state-of-the-art solvers, especially for medium and high dimensions.
Cross-Study Analyses of Gut Microbiomes from Healthy and Obese Individuals

Background: With the advent of metagenomics, many large studies have been conducted with the quest of better understanding gut microbiota changes in relation to varying health conditions. Significant findings have been made for diseases such as cirrhosis, colorectal cancers, inflammatory bowel diseases and others, yet one that stands out is obesity for which conflicting results have been reported in the literature. Methods: Here, we built and analyzed a cross-study dataset of healthy and obese individuals looking for major changes in the the taxonomic and functional composition of their metagenomes. Results: Our results suggest that the overweight and normal subjects have no strong dissimilarity in their metagenomes composition. Significant differences were observed when comparing the obese and the non-obese individuals in their functional and taxonomic profiles. Conclusion: In this study, we report the most significant changes that we observed and discuss their potential implication in the obesity condition.
Ultrafast Electron Transfer Dynamics from Molecular Adsorbates to Semiconductor Nanocrystalline Thin Films

Interfacial electron transfer (ET) between semiconductor nanomaterials and molecular adsorbates is an important fundamental process that is relevant to applications of these materials. Using femtosecond midinfrared spectroscopy, we have simultaneously measured the dynamics of injected electrons and adsorbates by directly monitoring the mid-IR absorption of electrons in the semiconductor and the change in adsorbate vibrational spectrum, respectively. We report on a series of studies designed to understand how the interfacial ET dynamics depends on the properties of the adsorbates, semiconductors, and their interaction. In Ru(dcbpy)2(SCN)2 (dcbpy = 2,2‘-bipyridine-4,4‘-dicarboxylate) sensitized TiO2 thin films, 400 nm excitation of the molecule promotes an electron to the metal-to-ligand charge transfer (MLCT) excited state, from which it is injected into TiO2. The injection process was characterized by a fast component, with a time constant of <100 fs, and a slower component that is sensitive to sample con.
Adaptation and correlated fitness responses over two time scales in Drosophila suzukii populations evolving in different environments

The process of local adaptation involves differential changes in fitness over time across different environments. Although experimental evolution studies have extensively tested for patterns of local adaptation at a single time point, there is relatively little research that examines fitness more than once during the time course of adaptation. We allowed replicate populations of the fruit pest Drosophila suzukii to evolve in one of eight different fruit media. After five generations, populations with the highest initial levels of maladaptation had mostly gone extinct, whereas experimental populations evolving on cherry, strawberry and cranberry media had survived. We measured the fitness of each surviving population in each of the three fruit media after five and after 26 generations of evolution. After five generations, adaptation to each medium was associated with increased fitness in the two other media. This was also true after 26 generations, except when populations that evolved on cranberry medium developed on cherry medium. These results suggest that, in the theoretical framework of a fitness landscape, the fitness optima of cherry and cranberry media are the furthest apart. Our results show that studying how fitness changes across several environments and across multiple generations provides insights into the dynamics of local adaptation that would not be evident if fitness were analysed at a single point in time. By allowing a qualitative mapping of an experimental fitness landscape, our approach will improve our understanding of the ecological factors that drive the evolution of local adaptation in D. suzukii.
Secondary Structure Motifs Made Searchable to Facilitate the Functional Peptide Design

To ensure a physicochemically desired sequence motif to adapt a specific type of secondary structures, we compile an α-helix database allowing complicate search patterns to facilitate a data-driven design of therapeutic peptides. Nearly 1.7 million helical peptides in >130 thousand proteins are extracted along with their interacting partners from the protein data bank (PDB). The sequences of the peptides are indexed with patterns and gaps and deposited in our Therapeutic Peptide Design dataBase (TP-DB). We here demonstrate its utility in three medicinal design cases. By our pattern-based search engine but not PHI-BLAST, we can identify a pathogenic protein, Helicobacter pylori neutrophil-activating protein (HP-NAP), a virulence factor of H. pylori, which contains a motif DYKYLE that belongs to the affinity determinant motif DYKXX[DE] of the FLAG-tag and can be recognized by the anti-FLAG M2 antibody. By doing so, the known purification-tag-specific antibody is repurposed into a diagnostic kit for H. pylori. Also by leveraging TP-DB, we discovered a stretch of helical peptide matching the potent membrane-insertion pattern WXXWXXW, elucidated by MD simulations. The newly synthesized peptide has a better minimal inhibitory concentration (MIC) and much lower cytotoxicity against Candida albicans (fungus) than that of previously characterized homologous antimicrobial peptides. In a similar vein, taking the discontinued anchoring residues in the helix-helix interaction interface as the search pattern, TP-DB returns several helical peptides as potential tumor suppressors of hepatocellular carcinoma (HCC) whose helicity and binding affinity were examined by MD simulations. Taken together, we believe that TP-DB and its pattern-based search engine provide a new opportunity for a (secondary-)structure-based design of peptide drugs and diagnostic kits for pathogens without inferring evolutionary homology between sequences sharing the same pattern. TP-DB is made available at http://dyn.life. nthu.edu. tw/design/.
Divergence in a stress regulatory network underlies differential growth control

The phytohormone abscisic acid (ABA) is a central regulator of acclimation during environmental stress. While some plants exhibit tremendous stress resilience, it has been unclear whether differences in ABA response underlie such adaptations. Here we establish a cross-species gene regulatory network (GRN) for ABA to identify broadly conserved, core components of the ABA signaling network and peripheral pathways exhibiting species-specific connectivity. Genes that are broadly conserved in the network share promoter architecture and patterns of gene expression. Networks associated with growth hormones exhibited highly divergent wiring of their ABA network leading to changes in the physiological outcome of signaling. Together our study provides a model for understanding how GRN subcircuits deploy different growth regulatory states across ecologically diverse species.Comparative studies reveal core and peripheral stress-mediated gene networks driving divergent growth control in plants.