Miracle-dz/UniFAR-FTUs huggingface dataset:
Nomenclatural notes on some ferns of Costa Rica, Panama, and Colombia

My studies of tropical American ferns have led to the recognition of some new species of ferns that have been (Proc. Biol. Soc. Washington 89: 703-732. 1977) or will be published elsewhere. Others already have epithets, but not in the correct genera as I understand them. For these, the following new combinations are proposed. A few names above the specific level are included. Arachniodes denticulata var. barbensis (C. Chr.) Lellinger, comb. nov. Dryopteris denticulata var. barbensis C. Chr. K. Danske Vidensk. Selsk. Skr. , Natur. Math. Afd. VIII, 6: 115. 1920. LECTOTYPE: In order to fix the application of this name, I choose: Volcan Barba, Pcia. Heredia, Costa Rica, Pittier 1932 (US; isolectotypes B not seen, P not seen). The other syntype is: Volcan Barba, Hoffmann 79 (B not seen). This variety apparently has evolved locally from var. jucunda in the high mountains of southern Mexico, Guatemala, Costa Rica, and Panama. It is larger and more divided than var. jucunda. Arachniodes denticulata var. jucunda (Fee) Lellinger, comb. nov. Aspidiumjucundum Fee, Icon. Esp. Nouv. 41. 1865. LECTOTYPE: Edo. Oaxaca, Mexico, 7500 ft alt, Galeotti 6563 (P or RB not seen; isolectotype BR not seen photo 5181), chosen by Christensen (K. Danske Vidensk. Selsk. Skr., Natur. Math. Afd. VIII, 6: 120. 1920). The other syntype is: Cuba, Linden 2115 (P not seen photo 4675; isosyntypes BM not seen photo 6409, BR not seen Weatherby photo), and is Arachniodes formosa (Fee) Ching.
Newton-Raphson algorithm for 3-phase load flow

One of the problems encountered in power-systems operation is the generation of unbalanced voltages and currents in the presence of long transmission lines with few or no transpositions, including possible unbalances arising in source and load conditions, or indeed any items of plant such as shunt and series reactors. To improve or investigate these unbalance effects in any detail, a 3-phase-load-flow solution that allows representation of all possible unbalances as they exist in the power-systems network without making any assumptions is essential. Consequently, a working Newton-Raphson algorithm is presented, which forms the basis of a computer program developed for the specific purpose of solving the 3-phase-load-flow problem. An indication of program facilities is given, together with sample results for illustration.
A wireless gateway for fieldbus

We study wireless communications within the context of industrial applications [Gerla and Lessard 1994]. The question of interest is whether or not wireless connections can be used for accessing distant sensors and actuators through fieldbus [Pleinevaux and Decotignie 1993]. The question of interest is where the bandwidth of available wireless connections allows access to distant sensors/actuators through a fieldbus protocol. The paper describes a gateway design for the extension of a FIP standard fieldbus through a narrowband radio channel. The performance limit is evaluated and an example given for a 9600 bps channel.
Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps

Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption to help save the environment all while saving money. Non-intrusive load monitoring (NILM) tries to do just that. One of the hardest problems NILM faces is the ability to run unsupervised - discovering appliances without prior knowledge - and to run independent of the differences in appliance mixes and operational characteristics found in various countries and regions. We propose a solution that can do this with the use of an advanced filter pipeline to preprocess the data, a Gaussian appliance model with a probabilistic knapsack algorithm to disaggregate the aggregate smart meter signal, and partition maps to label which appliances were found and how much energy they use no matter the country/region. Experimental results show that relatively complex appliance signals can be tracked accounting for 93.7% of the total aggregate energy consumed.
Observational limits on the X-ray emission from the bubble nebula surrounding Ho IX X-1

Optical and radio observations of shock-ionized bubble nebulae surrounding ultraluminous X-ray sources (ULXs) suggest that they are powered by jets or supercritical outflows presumably launched from the ULX accretion disc. Recent simulations of these systems have shown that the shocked wind can emit thermal X-rays with estimated luminosities ≲1036 erg s−1. In this work, we investigated whether it is possible to detect and spatially resolve the X-ray emission from these systems using archival Chandra observations of the ULX Holmberg IX X-1 (Ho IX X-1). This source is an ideal target to study for two reasons: it is surrounded by an optical bubble nebula with a large spatial extent (∼400 pc) that can easily be resolved with Chandra. Further, it has a hard X-ray continuum that is easily distinguishable from the expected soft thermal emission from the nebula. However, a spectral and photometric analysis on stacked Chandra observations of the source reveals that there is no strong evidence for an X-ray bubble associated with it, to a limiting luminosity of ∼2 × 1036 erg s−1. The detection of such X-ray nebulae may be possible with future X-ray missions such as Advanced Telescope for High ENergy Astrophysics(ATHENA), which would provide useful constraints on the kinematics of the outflow. Finally, our observations also emphasize that the nebular emission does not contribute significantly to the residuals in the X-ray spectrum of the source, which are more likely to be linked to processes localized to the ULX.
Motorway Traffic Flow Prediction using Advanced Deep Learning

Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of traffic flow both in time and space. Recently, deep learning techniques have shown significant prediction improvements over traditional models, however open questions remain around their applicability, accuracy and parameter tuning. This paper proposes an advanced deep learning framework for simultaneously predicting the traffic flow on a large number of monitoring stations along a highly circulated motorway in Sydney, Australia, including exit and entry loop count stations, and over varying training and prediction time horizons. The spatial and temporal features extracted from the 36.34 million data points are used in various deep learning architectures that exploit their spatial structure (convolutional neuronal networks), their temporal dynamics (recurrent neuronal networks), or both through a hybrid spatio-temporal modelling (CNN-LSTM). We show that our deep learning models consistently outperform traditional methods, and we conduct a comparative analysis of the optimal time horizon of historical data required to predict traffic flow at different time points in the future.
Local Distribution Obfuscation via Probability Coupling*

We introduce a general model for the local obfuscation of probability distributions by probabilistic perturbation, e.g., by adding differentially private noise, and investigate its theoretical properties. Specifically, we relax a notion of distribution privacy (DistP) by generalizing it to divergence, and propose local obfuscation mechanisms that provide divergence distribution privacy. To provide f-divergence distribution privacy, we prove that probabilistic perturbation noise should be added proportionally to the Earth mover’s distance between the probability distributions that we want to make indistinguishable. Furthermore, we introduce a local obfuscation mechanism, which we call a coupling mechanism, that provides divergence distribution privacy while optimizing the utility of obfuscated data by using exact/approximate auxiliary information on the input distributions we want to protect.
Reconstructing spontaneous speech

The output of a speech recognition system is often not what is required for subsequent processing, in part because speakers themselves make mistakes (e.g. stuttering, self-correcting, or using filler words). A system would accomplish speech reconstruction of its spontaneous speech input if its output were to represent, in flawless, fluent, and content-preserved English, the message that the speaker intended to convey. These cleaner speech transcripts would allow for more accurate language processing as needed for natural language tasks such as machine translation and conversation summarization, which often assume a grammatical sentence as input. Before attempting to reconstruct speech automatically, we seek to comprehensively understand the problem itself. We quantify the range, complexity, and frequencies of speaker errors common in spontaneous speech given a set of manual reconstruction annotations. This empirical analysis indicates the most frequent and problematic errors and thus suggests areas of focus for future reconstruction research. The surface transformations recorded in our reconstruction annotations reflect underlying influences in the psycholinguistics and speech production models of spontaneous speakers. We review standard theories and seek empirical evidence of both model assumptions and conclusions given the manual reconstructions and corresponding shallow semantic labeling annotation we collect. This investigation of naturally-occurring spontaneous speaker errors with manual semantico-syntactic analysis yields additional insight into the impact of spoken language on semantic structure and how these features can be used in future reconstruction efforts. Finally, given our accumulated knowledge about the types, frequencies, and drivers of speaker-generated errors in spontaneous speech, we build a set of systems to automatically identify and correct a subset of the most frequent errors. Using a conditional random field classification model and lexical, syntactic, and shallow semantic features to train both word-level and utterance-level error classifiers, we show improvement in the correction of these errors over a state-of-the-art system.
FlexAID: Revisiting docking on non native-complex structures

Small-molecule protein docking is an essential tool in drug design and to understand molecular recognition. In the present work we introduce FlexAID, a small-molecule docking algorithm that accounts for target side-chain flexibility and utilizes a soft scoring function, i.e. one that is not highly dependent on specific geometric criteria, based on surface complementarity. The pairwise energy parameters were derived from a large dataset of true positive poses and negative decoys from the PDBbind dataset through an iterative process using Monte Carlo simulations. The prediction of binding poses is tested using the independent Astex dataset while performance in virtual screening is evaluated using a subset of the DUD dataset. We compare FlexAID to AutoDock Vina, FlexX, and rDock in an extensive number of scenarios to understand the strengths and limitations of the different programs as well as to reported results for Glide, GOLD and DOCK6 where applicable. The most relevant among these scenarios is that of docking on flexible non native-complex structures where as is the case in reality, the target conformation in the bound form is not known a priori. We demonstrate that FlexAID, unlike other programs, is robust against increasing structural variability. FlexAID obtains equivalent sampling success as GOLD and performs better than AutoDock Vina or FlexX in all scenarios against non native-complex structures. FlexAID is better than rDock when there is at least one critical side-chain movement required upon ligand binding. In virtual screening, FlexAID rescored results are comparable to those of AutoDock Vina and rDock. The higher accuracy in flexible targets where critical movements are required, intuitive PyMOL-integrated graphical user interface and free source code as well as pre-compiled executables for Windows, Linux and Mac OS make FlexAID a welcome addition to the arsenal of existing small-molecule protein docking methods. AUTHOR SUMMARY Protein ligand interactions are essential to understand biological processes such as enzymatic reactions, signalling pathways as well as in the development of new medicines. Docking algorithms permit to predict the structure of a ligand protein complex at the atomic level. Several docking algorithms were developed over the years with a tendency towards utilizing very specific and detailed (i.e., hard) descriptions of molecular interactions. In this work we present a new docking algorithm called FlexAID that utilizes a very general and superficial (i.e., soft) description of interactions based on atomic surface areas in contact. We demonstrate that FlexAID can achieve better accuracy in predicting the structure of ligand protein complexes than existing accessible widely used or state-of-the-art methods in real scenarios when using flexible targets harbouring structural differences with respect to the final protein structure present in the ligand protein complex. FlexAID and its PyMOL-integrated graphical user interface are free, easy to use and available for Windows, Linux and Mac OS.
The Poincaré-Boltzmann Machine: from Statistical Physics to Machine Learning and back

This paper presents the computational methods of information cohomology applied to genetic expression in and in the companion paper and proposes its interpretations in terms of statistical physics and machine learning. In order to further underline the Hochschild cohomological nature af information functions and chain rules, following, the computation of the cohomology in low degrees is detailed to show more directly that the $k$ multivariate mutual-informations (I_k) are k-coboundaries. The k-cocycles condition corresponds to I_k=0, generalizing statistical independence. Hence the cohomology quantifies the statistical dependences and the obstruction to factorization. The topological approach allows to investigate information in the multivariate case without the assumptions of independent identically distributed variables and without mean field approximations. We develop the computationally tractable subcase of simplicial information cohomology represented by entropy H_k and information I_k landscapes and their respective paths. The I_1 component defines a self-internal energy U_k, and I_k,k>1 components define the contribution to a free energy G_k (the total correlation) of the k-body interactions. The set of information paths in simplicial structures is in bijection with the symmetric group and random processes, provides a trivial topological expression of the 2nd law of thermodynamic. The local minima of free-energy, related to conditional information negativity, and conditional independence, characterize a minimum free energy complex. This complex formalizes the minimum free-energy principle in topology, provides a definition of a complex system, and characterizes a multiplicity of local minima that quantifies the diversity observed in biology. I give an interpretation of this complex in terms of frustration in glass and of Van Der Walls k-body interactions for data points.
Functional inequalities for the heat flow on time‐dependent metric measure spaces

We prove that synthetic lower Ricci bounds for metric measure spaces — both in the sense of Bakry–Émery and in the sense of Lott–Sturm–Villani — can be characterized by various functional inequalities including local Poincaré inequalities, local logarithmic Sobolev inequalities, dimension independent Harnack inequality, and logarithmic Harnack inequality.
Hybrid Offline-Online Design for UAV-Enabled Data Harvesting in Probabilistic LoS Channels

This paper considers an unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) in urban areas, where a UAV is deployed to collect data from distributed sensor nodes (SNs) within a given duration. To characterize the occasional building blockage between the UAV and SNs, we construct the probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using the combined simulation and data regression method, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. We assume that only the knowledge of SNs’ locations and the probabilistic LoS channel model is known a priori, while the UAV can obtain the instantaneous LoS/Non-LoS channel state information (CSI) with the SNs in real time along its flight. Our objective is to maximize the minimum (average) data collection rate from all the SNs for the UAV. To this end, we formulate a new rate maximization problem by jointly optimizing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Although the optimal solution is intractable due to the lack of complete UAV-SNs CSI, we propose in this paper a novel and general design method, called hybrid offline-online optimization, to obtain a suboptimal solution to it, by leveraging both the statistical and real-time CSI. Essentially, our proposed method decouples the joint design of UAV trajectory and communication scheduling into two phases: namely, an offline phase that determines the UAV path prior to its flight based on the probabilistic LoS channel model, followed by an online phase that adaptively adjusts the UAV flying speeds along the offline optimized path as well as communication scheduling based on the instantaneous UAV-SNs CSI and SNs’ individual amounts of data received accumulatively. Extensive simulation results are provided to show the significant rate performance improvement of our proposed design as compared to various benchmark schemes.
Identification and qualification of 500 nuclear, single-copy, orthologous genes for the Eupulmonata (Gastropoda) using transcriptome sequencing and exon-capture

The qualification of orthology is a significant challenge when developing large, multiloci phylogenetic datasets from assembled transcripts. Transcriptome assemblies have various attributes, such as fragmentation, frameshifts, and mis-indexing, which pose problems to automated methods of orthology assessment. Here, we identify a set of orthologous single-copy genes from transcriptome assemblies for the land snails and slugs (Eupulmonata) using a thorough approach to orthology determination involving manual alignment curation, gene tree assessment and sequencing from genomic DNA. We qualified the orthology of 500 nuclear, protein coding genes from the transcriptome assemblies of 21 eupulmonate species to produce the most complete gene data matrix for a major molluscan lineage to date, both in terms of taxon and character completeness. Exon-capture targeting 490 of the 500 genes (those with at least one exon > 120 bp) from 22 species of Australian Camaenidae successfully captured sequences of 2,825 exons (representing all targeted genes), with only a 3.7% reduction in the data matrix due to the presence of putative paralogs or pseudogenes. The automated pipeline Agalma retrieved the majority of the manually qualified 500 single-copy gene set and identified a further 375 putative single-copy genes, although it failed to account for fragmented transcripts resulting in lower data matrix completeness. This could potentially explain the minor inconsistencies we observed in the supported topologies for the 21 eupulmonate species between the manually curated and Agalma-equivalent dataset (sharing 458 genes). Overall, our study confirms the utility of the 500 gene set to resolve phylogenetic relationships at a broad range of evolutionary depths, and highlights the importance of addressing fragmentation at the homolog alignment stage for probe design.
Markov-switching state space models for uncovering musical interpretation

For concertgoers, musical interpretation is the most important factor in determining whether or not we enjoy a classical performance. Every performance includes mistakes---intonation issues, a lost note, an unpleasant sound---but these are all easily forgotten (or unnoticed) when a performer engages her audience, imbuing a piece with novel emotional content beyond the vague instructions inscribed on the printed page. While music teachers use imagery or heuristic guidelines to motivate interpretive decisions, combining these vague instructions to create a convincing performance remains the domain of the performer, subject to the whims of the moment, technical fluency, and taste. In this research, we use data from the CHARM Mazurka Project---forty-six professional recordings of Chopin's Mazurka Op. 63 No. 3 by consumate artists---with the goal of elucidating musically interpretable performance decisions. Using information on the inter-onset intervals of the note attacks in the recordings, we apply functional data analysis techniques enriched with prior information gained from music theory to discover relevant features and perform hierarchical clustering. The resulting clusters suggest methods for informing music instruction, discovering listening preferences, and analyzing performances.
Bus-invert coding for low-power I/O - a decomposition approach

This paper proposes a new bus-invert coding scheme for reducing the number of bus transitions. Unlike the previous schemes in which the entire bus lines or one subset of the bus fines are considered for bus-invert coding, in the proposed scheme the bus lines are partitioned and each partitioned group is considered independently for bus-invert coding to maximize the effectiveness of reducing the total number of bus transitions. Experimental results show that the decomposed bus-invert coding scheme reduces the total number of bus transitions by 47.2% and 11.9% on average than those of the conventional and the partial bus-invert coding schemes, respectively.
Challenges of Cheap Resource Creation

We describe the challenges of resource creation for a resource-light system for morphological tagging of fusional languages (Feldman and Hana, 2010). The constraints on resources (time, expertise, and money) introduce challenges that are not present in development of morphological tools and corpora in the usual, resource intensive way.
Joint Language Identification of Code-Switching Speech using Attention-based E2E Network

Language identification (LID) has relevance in many speech processing applications. For the automatic recognition of code-switching speech, the conventional approaches often employ an LID system for detecting the languages present within an utterance. In the existing works, the LID on code-switching speech involves modeling of the underlying languages separately. In this work, we propose a joint modeling based LID system for code-switching speech. To achieve the same, an attentionbased end-to-end (E2E) network has been explored. For the development and evaluation of the proposed approach, a recently created Hindi-English code-switching corpus has been used. For the contrast purpose, an LID system employing the connectionist temporal classification-based E2E network is also developed. On comparing both the systems, the attention based approach is noted to result in better LID accuracy. The effective location of code-switching boundaries within the utterance by the proposed approach has been demonstrated by plotting the attention weights of E2E network.