Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast and Accurate Species Trees from Weighted Internode Distances

Species tree estimation is a basic step in many biological research projects, but is complicated by the fact that gene trees can differ from the species tree due to processes such as incomplete lineage sorting (ILS), gene duplication and loss (GDL), and horizontal gene transfer (HGT), which can cause different regions within the genome to have different evolutionary histories (i.e., “gene tree heterogeneity”). One approach to estimating species trees in the presence of gene tree heterogeneity resulting from ILS operates by computing trees on each genomic region (i.e., computing “gene trees”) and then using these gene trees to define a matrix of average internode distances, where the internode distance in a tree T between two species x and y is the number of nodes in T between the leaves corresponding to x and y. Given such a matrix, a tree can then be computed using methods such as neighbor joining. Methods such as ASTRID and NJst (which use this basic approach) are provably statistically consistent, very fast (low degree polynomial time) and have had high accuracy under many conditions that makes them competitive with other popular species tree estimation methods. In this study, inspired by the very recent work of weighted ASTRAL, we present weighted ASTRID, a variant of ASTRID that takes the branch uncertainty on the gene trees into account in the internode distance. Our experimental study evaluating weighted ASTRID shows improvements in accuracy compared to the original (unweighted) ASTRID while remaining fast. Moreover, weighted ASTRID shows competitive accuracy against weighted ASTRAL, the state of the art. Thus, this study provides a new and very fast method for species tree estimation that improves upon ASTRID, has comparable accuracy with the state of the art while remaining much faster. Weighted ASTRID is available at https://github.com/RuneBlaze/internode.
Upper mantle heterogeneities in the Indian Ocean from waveform inversion

A waveform inversion method is applied to 156 Love and Rayleigh wave seismograms to build up a 3‐D model of the shear velocity in the upper mantle beneath the Indian Ocean. The first step of the method consists in finding, for each path, a radially stratified upper mantle model compatible with the Love and Rayleigh wave seismograms relative to that path. The fundamental mode and few higher modes are modelled in the waveform inversion. In a second step, the models related to the different paths are used in a tomographic inversion to map the 3‐D upper mantle structure. The 3‐D velocity model has a lateral resolution of 1000 km. No significant velocity anomalies are found below 300 km, although the resolution is still good. Continental roots are confined to the upper 300 km and low velocities below mid‐oceanic ridges to the upper 250 km, depending on their spreading rates. At shallow depths (<80 km), we find a positive velocity anomaly beneath the West Indian ridge near the Rodriguez triple junction, where gravimetric and bathymetric data indicate a reduced volcanic activity. At greater depth (around 200 km), a low velocity signature is found beneath the hot‐spot of Réunion‐Mauritius islands and the Central Indian ridge. It could reflect a real connection between the two structures.
Effects of product design on assembly lines performances: A concurrent engineering approach

Purpose – The paper's aim is to assess the impact of product related features on the performances of assembly line manufacturing systems, also providing a specific Design for Manufacturing and Assembly rating index to assess the goodness of a product design solution with respect to assembly line performances.Design/methodology/approach – A computer simulation‐based parametric analysis was carried out to assess the impact of four major product‐related parameters. 216 different assembly line balance problem instances were evaluated. Findings allowed to develop a DFMA rating index specific for assembly line manufacturing as well as design guidelines.Findings – Assembly sequence degrees of freedom and the ratio of the average task duration to the maximum duration are the most influencing parameters. While the former should be maximized, only a moderate task duration variability was found beneficial. The influence of other factors resulted less marked and changing on a case‐specific basis.Research limitations/.
An Online System for Real-Time Activity Detection in Untrimmed Surveillance Videos

Activity detection in surveillance videos is a challenging problem due to multiple factors such as large ﬁeld of view, presence of multiple activities, varying scales and view-points, and its untrimmed nature. The requirement of processing the surveillance videos in real-time makes this more challenging. In this work, we propose a real-time online system to perform activity detection on untrimmed surveillance videos. The proposed system consists of three stages: ﬁrst we detect tubelets with activities, then classify them, and ﬁnally merge them to generate spatio-temporal activity detections. We propose a localization network which takes a video clip as input and makes use of feature pyramid, multi-layer loss, and atrous convolutions to address the issue of multiple scales and detect small activities in terms of tubelets. The online processing of videos at a clip level drastically reduces the computation time in detecting activities. The detected tubelets are assigned activity class scores and merged together using our proposed Tubelet-Merge Action-Split (TMAS) algorithm to form action tubes. The TMAS algorithm efﬁciently connects the tubelets in an online fashion to generate spatio-temporal detections which are robust against varying length activities. We perform our experiments on the DIVA (Deep Intermodal Video Analytics) dataset and demonstrate the effectiveness of the proposed approach in terms of speed ( ⇠ 100 fps) and performance with state-of-the-art results. The code and models will be made publicly available
Nonsmooth $H_infty$ Synthesis

We develop nonsmooth optimization techniques to solve $H_infty$ synthesis problems under additional structural constraints on the controller. Our approach avoids the use of Lyapunov variables and therefore leads to moderate size optimization programs even for very large systems. The proposed framework is versatile and can accommodate a number of challenging design problems including static, fixed-order, fixed-structure, decentralized control, design of PID controllers and simultaneous design and stabilization problems. Our algorithmic strategy uses generalized gradients and bundling techniques suited for the $H_infty$ norm and other nonsmooth performance criteria. We compute descent directions by solving quadratic programs and generate steps via line search. Convergence to a critical point from an arbitrary starting point is proved and numerical tests are included to validate our methods. The proposed approach proves to be efficient even for systems with several hundreds of states.
A midterm report on the GEWEX Continental-Scale International Project (GCIP)

The GEWEX Continental-Scale International Project (GCIP) is being carried out in the Mississippi River Basin under the auspices of the World Climate Research Program and its Global Energy and Water Cycle Experiment (GEWEX). This paper provides a brief history of GCIP's development and its mission and objectives. The scientific achievements of GCIP are then reviewed against each of these objectives. The paper concludes with a brief discussion of options for the period beyond 2000 after the current phase of the project is completed.
An empirical Bayes approach to detect anomalies in dynamic multidimensional arrays

We consider the problem of detecting anomalies in data that arise as multidimensional arrays with each dimension corresponding to the levels of a categorical variable. In typical data mining applications, the number of cells in such arrays is usually large. Our primary focus is detecting anomalies by comparing information at the current time to historical data. Naive approaches advocated in the process control literature do not work well in this scenario due to the multiple testing problems - performing multiple statistical tests on the same data produce excessive number of false positives. We use an empirical Bayes method which works by fitting a two component Gaussian mixture to deviations at current time. The approach is scalable to problems that involve monitoring massive number of cells and fast enough to be potentially useful in many streaming scenarios. We show the superiority of the method relative to a naive "per component error rate" procedure through simulation. A novel feature of our technique is the ability to suppress deviations that are merely the consequence of sharp changes in the marginal distributions. This research was motivated by the need to extract critical application information and business intelligence from the daily logs that accompany large-scale spoken dialog systems deployed by AT&T. We illustrate our method on one such system.
Rapid Acquisition of Specular and Diffuse Normal Maps from Polarized Spherical Gradient Illumination

We estimate surface normal maps of an object from either its diffuse or specular reflectance using four spherical gradient illumination patterns. In contrast to traditional photometric stereo, the spherical patterns allow normals to be estimated simultaneously from any number of viewpoints. We present two polarized lighting techniques that allow the diffuse and specular normal maps of an object to be measured independently. For scattering materials, we show that the specular normal maps yield the best record of detailed surface shape while the diffuse normals deviate from the true surface normal due to subsurface scattering, and that this effect is dependent on wavelength. We show several applications of this acquisition technique. First, we capture normal maps of a facial performance simultaneously from several viewing positions using time-multiplexed illumination. Second, we show that highresolution normal maps based on the specular component can be used with structured light 3D scanning to quickly acquire high-resolution facial surface geometry using off-the-shelf digital still cameras. Finally, we present a realtime shading model that uses independently estimated normal maps for the specular and diffuse color channels to reproduce some of the perceptually important effects of subsurface scattering.
Rfold: an exact algorithm for computing local base pairing probabilities

MOTIVATION Base pairing probability matrices have been frequently used for the analyses of structural RNA sequences. Recently, there has been a growing need for computing these probabilities for long DNA sequences by constraining the maximal span of base pairs to a limited value. However, none of the existing programs can exactly compute the base pairing probabilities associated with the energy model of secondary structures under such a constraint. RESULTS We present an algorithm that exactly computes the base pairing probabilities associated with the energy model under the constraint on the maximal span W of base pairs. The complexity of our algorithm is given by O(NW2) in time and O(N+W2) in memory, where N is the sequence length. We show that our algorithm has a higher sensitivity to the true base pairs as compared to that of RNAplfold. We also present an algorithm that predicts a mutually consistent set of local secondary structures by maximizing the expected accuracy function. The comparison of the local secondary structure predictions with those of RNALfold indicates that our algorithm is more accurate. Our algorithms are implemented in the software named 'Rfold. ' AVAILABILITY The C++ source code of the Rfold software and the test dataset used in this study are available at http://www.ncrna. org/software/Rfold/.
STYLER: Style Modeling with Rapidity and Robustness via SpeechDecomposition for Expressive and Controllable Neural Text to Speech

Previous works on expressive text-to-speech (TTS) have a limitation on robustness and speed when training and inferring. Such drawbacks mostly come from autoregressive decoding, which makes the succeeding step vulnerable to preceding error. To overcome this weakness, we propose STYLER, a novel expressive text-to-speech model with parallelized architecture. Expelling autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust even with high style transfer performance. Moreover, our novel noise modeling approach from audio using domain adversarial training and Residual Decodingenabled style transfer without transferring noise. Our experiments prove the naturalness and expressiveness of our model from comparison with other parallel TTS models. Together we investigate our model’s robustness and speed by comparison with the expressive TTS model with autoregressive decoding.
The computer scientist as toolsmith II

A process for producing a photographic film having thereon a magnetic recording layer by applying a dispersion of a magnetic substance to an antihalation layer of a light-sensitive film for cinema, where the dispersion contains a compound or compounds, having at least two or more isocyanato groups or thioisocyanato groups or the anti-halation layer is previously processed with a solution containing the compound. A photographic film where either the magnetic layer contains, or the antihalation layer has been processed with, such a compound or compounds.
CrunchGPT: A chatGPT assisted framework for scientific machine learning

Scientiﬁc Machine Learning (SciML) has advanced recently across many different areas in computational science and engineering. The objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing and analysis are still time-consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate CrunchGPT , which plays the role of a conductor orchestrating the entire workﬂow of SciML based on simple prompts by the user. Speciﬁcally, we present two examples that demonstrate the potential use of CrunchGPT in optimizing airfoils in aerodynamics, and in obtaining ﬂow ﬁelds in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the ﬂow of the CrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a webapp based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend CrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientiﬁc computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus in ﬂuid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology and bioinformatics.
Correcting wind‐induced bias in solid precipitation measurements in case of limited and uncertain data

Automatic precipitation gauges tend to underestimate solid precipitation in the presence of wind. Loss as a function of wind speed is typically evaluated by comparing the gauge with a more accurate measurement made using a double‐fence intercomparison reference gauge (DFIR). For small precipitation events, small errors in the observations can induce large errors in the ‘catch’ ratio, i.e. the ratio of the automatic gauge measurement to the DFIR observation. For this reason, precipitation events of less than 3 mm are typically discarded before performing the regression analysis. This can mean discarding more than 90% of the observations. This paper shows how the method of weighted least squares can be used to perform a regression analysis that can take into account the whole sample to provide a more accurate estimation of the relationship between the catch ratio and the wind speed. This methodology is then used to obtain an adjustment curve for a shielded Geonor T‐200B precipitation gauge in Northern Québec. Copyright © 2008 John Wiley & Sons, Ltd and Her Majesty the Queen in right of Canada.
Reference Model for Collaborative Business Intelligence Virtual Assistant

Collaborative Business Analysis (CBA) is a methodology that involves bringing together different stakeholders, including business users, analysts, and technical specialists, to collaboratively analyze data and gain insights into business operations. The primary objective of CBA is to encourage knowledge sharing and collaboration between the different groups involved in business analysis, as this can lead to a more comprehensive understanding of the data and better decision-making. CBA typically involves a range of activities, including data gathering and analysis, brainstorming, problem-solving, decision-making and knowledge sharing. These activities may take place through various channels, such as in-person meetings, virtual collaboration tools or online forums. This paper deals with virtual collaboration tools as an important part of Business Intelligence (BI) platform. Collaborative Business Intelligence (CBI) tools are becoming more user-friendly, accessible, and flexible, allowing users to customize their experience and adapt to their specific needs. The goal of a virtual assistant is to make data exploration more accessible to a wider range of users and to reduce the time and effort required for data analysis. It describes the unified business intelligence semantic model, coupled with a data warehouse and collaborative unit to employ data mining technology. Moreover, we propose a virtual assistant for CBI and a reference model of virtual tools for CBI, which consists of three components: conversational, data exploration and recommendation agents. We believe that the allocation of these three functional tasks allows you to structure the CBI issue and apply relevant and productive models for human-like dialogue, text-to-command transferring, and recommendations simultaneously. The complex approach based on these three points gives the basis for virtual tool for collaboration. CBI encourages people, processes, and technology to enable everyone sharing and leveraging collective expertise, knowledge and data to gain valuable insights for making better decisions. This allows to respond more quickly and effectively to changes in the market or internal operations and improve the progress.
Isotopic constraints on growth conditions of multiphase calcite–pyrite–barite concretions in Carboniferous mudstones

Carbonate concretions in the Lower Carboniferous Caton Shale Formation contain diagenetic pyrite, calcite and barite in the concretion matrix or in different generations of septarian fissures. Pyrite was formed by sulphate reduction throughout the sediment before concretionary growth, then continued to form mainly in the concretion centres. The septarian calcites show a continuous isotopic trend from δ13C=−28·7‰ PDB and δ18O=−1·6‰ PDB through to δ13C=−6·9‰ PDB and δ18O=−14·6‰ PDB. This trend arises from (1) a carbonate source initially from sulphate reduction, to which was added increasing contributions of methanogenic carbonate; and (2) burial/temperature effects or the addition of isotopically light oxygen from meteoric water. The concretionary matrix carbonates must have at least partially predated the earliest septarian cements, and thus used the same carbonate sources. Consequently, their isotopic composition (δ13C=−12·0 to −10·1‰ PDB and δ18O=−5·7 to −5·6‰ PDB) can only result from mixing a carbonate cement derived from sulphate reduction with cements containing increasing proportions of carbonate from methanogenesis and, directly or indirectly, also from skeletal carbonate. Concretionary growth was therefore pervasive, with cements being added progressively throughout the concretion body during growth. The concretions contain barite in the concretion matrix and in septarian fissures. Barite in the earlier matrix phase has an isotopic composition (δ34S=+24·8‰ CDT and δ18O=+16·4‰ SMOW), indicating formation from near‐surface, sulphate‐depleted porewaters. Barites in the later septarian phase have unusual isotopic compositions (δ34S=+6 to +11‰ CDT and δ18O=+8 to +11‰ SMOW), which require the late addition of isotopically light sulphate to the porewaters, either from anoxic sulphide oxidation (using ferric iron) or from sulphate dissolved in meteoric water. Carbon isotope and biomarker data indicate that oil trapped within septarian fissures was derived from the maturation of kerogen in the enclosing sediments.
Machine Learning Network Tomography with partial topology knowledge and dynamic routing

Networks are always progressing to support the evolving and diverse applications and the needs for improved capacity, latency and security. To this end, monitoring is key to ensuring the uninterrupted network operation and the QoS of the applications. Network Tomography uses a subset of monitoring information (corresponding to partial view of the network state) to estimate wide-sense network performance, including unmonitored parameters. In this paper, we present a novel Machine Learning (ML) formulation for Network Tomography. The proposed formulation accounts for realistic scenarios where: i) the existence of certain links of the network is not known (e.g., due to security reasons), ii) the routing is dynamic (non-deterministic), i.e., for the same origin-destination node pair, a different route may be selected depending on the state of certain links. Our simulations indicate that our proposal has better estimation accuracy compared to traditional algebraic or other ML approaches that cannot or do not take into account these two assumptions.
Effect of soil type and rainfall on dicyandiamide concentrations in drainage from lysimeters

The nitrification inhibitor dicyandiamide (DCD) is mobile in drainage water, which has implications for its effectiveness in reducing nitrate leaching from urine patches. Lysimeters had been used to investigate the effect of soil type (clay, silt loam, or sandy loam) and precipitation (target ~1140 or 2280 mm/year) on the effectiveness of DCD to decrease nitrate leaching. This paper reports the associated effects on DCD in drainage water. DCD was applied in May and July at a rate of 10 kg/ha, and natural rainfall was supplemented with irrigation to ensure that the target precipitation was achieved for each treatment. The experiment was undertaken twice. The pattern of DCD concentrations in drainage water suggested that movement of DCD in the silt loam and sandy loam soils was typical of convective–dispersive flow. Although there was some preferential flow of DCD from the soil surface to depth in the clay soil, DCD concentration profiles suggested that the main transport mechanism was also by convective–dispersive flow. There were significant soil-type and precipitation effects on DCD leaching (P < 0.05). The soil-type effect could be attributed to differences in drainage volume between soils. Combining data from the two experimental years, DCD leaching losses ranged from 12 to 46% of applied, with annual drainage in the range 422–1292 mm. DCD was detected in drainage up to 15 months after application, demonstrating the longevity of the compound. The experiment demonstrates that leaching of DCD on all of the soil types tested can be substantial under high rainfall. This is likely to have implications for the effectiveness of DCD to decrease nitrogen losses from urine patches under such rainfall conditions, as well as being a source of nitrogen itself.