Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrafast learning of 4-node hybridization cycles in phylogenetic networks using algebraic invariants

Motivation: The abundance of gene flow in the Tree of Life challenges the notion that evolution can be represented with a fully bifurcating process, as this process cannot capture important biological realities like hybridization, introgression, or horizontal gene transfer. Coalescent-based network methods are increasingly popular, yet not scalable for big data, because they need to perform a heuristic search in the space of networks as well as numerical optimization that can be NP-hard. Results: Here, we introduce a novel method to reconstruct phylogenetic networks based on algebraic invariants. While there is a long tradition of using algebraic invariants in phylogenetics, our work is the first to define phylogenetic invariants on concordance factors (frequencies of 4-taxon splits in the input gene trees) to identify level-1 phylogenetic networks under the multispecies coalescent model. Our novel inference methodology is optimization-free as it only requires the evaluation of polynomial equations, and as such, it bypasses the traversal of network space, yielding a computational speed at least 10 times faster than the fastest-to-date network methods. We illustrate the accuracy and speed of our new method on a variety of simulated scenarios as well as in the estimation of a phylogenetic network for the genus Canis. Availability and Implementation: We implement our novel theory on an open-source publicly available Julia package PhyloDiamond.jl available at https://github.com/solislemuslab/PhyloDiamond.jl with broad applicability within the evolutionary biology community. Contact: solislemus@wisc.edu
Resolving Prime Modules: The Structure of Pseudo-cographs and Galled-Tree Explainable Graphs

The modular decomposition of a graph $G$ is a natural construction to capture key features of $G$ in terms of a labeled tree $(T,t)$ whose vertices are labeled as"series"($1$),"parallel"($0$) or"prime". However, full information of $G$ is provided by its modular decomposition tree $(T,t)$ only, if $G$ is a cograph, i.e., $G$ does not contain prime modules. In this case, $(T,t)$ explains $G$, i.e., $\{x,y\}\in E(G)$ if and only if the lowest common ancestor $\mathrm{lca}_T(x,y)$ of $x$ and $y$ has label"$1$". Pseudo-cographs, or, more general, GaTEx graphs $G$ are graphs that can be explained by labeled galled-trees, i.e., labeled networks $(N,t)$ that are obtained from the modular decomposition tree $(T,t)$ of $G$ by replacing the prime vertices in $T$ by simple labeled cycles. GaTEx graphs can be recognized and labeled galled-trees that explain these graphs can be constructed in linear time. In this contribution, we provide a novel characterization of GaTEx graphs in terms of a set $\mathfrak{F}_{\mathrm{GT}}$ of 25 forbidden induced subgraphs. This characterization, in turn, allows us to show that GaTEx graphs are closely related to many other well-known graph classes such as $P_4$-sparse and $P_4$-reducible graphs, weakly-chordal graphs, perfect graphs with perfect order, comparability and permutation graphs, murky graphs as well as interval graphs, Meyniel graphs or very strongly-perfect and brittle graphs. Moreover, we show that every GaTEx graph as twin-width at most 1 and and provide linear-time algorithms to solve several NP-hard problems (clique, coloring, independent set) on GaTEx graphs by utilizing the structure of the underlying galled-trees they explain.
Long-time asymptotics of a complex cubic Camassa-Holm equation

In this paper, we study the Cauchy problem of the following complex cubic Camassa-Holm (ccCH) equation mt = bux + 1 2 [ m ( |u| − |ux| )] x − 12m (uūx − uxū) , m = u− uxx, where b is an arbitrary real constant. Long-time asymptotics of the equation is obtained through the ∂̄-steepest descent method. Firstly, based on the spectral analysis of the Lax pair and scattering matrix, the solution of the equation is able to be constructed via solving the corresponding RiemannHilbert problem (RHP). Then, we present different long time asymptotic expansions of the solution u(y, t) in different space-time solitonic regions of ξ = y/t. The half-plane (y, t) : −∞ < y <∞, t > 0 is divided into four asymptotic regions: ξ ∈ (−∞,−1), ξ ∈ (−1, 0), ξ ∈ (0, 1 8 ) and ξ ∈ ( 1 8 ,+∞). When ξ falls in (−∞,−1) ∪ ( 1 8 ,+∞), no stationary phase point of the phase function θ(z) exists on the jump profile in the space-time region. In this case, corresponding asymptotic approximations can be characterized with an N(Λ)-solitons with diverse residual error order O(t−1+2ε). There are four stationary phase points and eight stationary phase points on the jump curve as ξ ∈ (−1, 0) and ξ ∈ (0, 18 ), respectively. The corresponding asymptotic form is accompanied by a residual error order O(t− 3 4 ).
Limiting multiple hyperchargeless scalar triplets using electroweak phase transition and other constraints

While an additional scalar multiplet over and above the Standard Model can lead to a strong electroweak phase transition, depending on its quantum numbers, it also potentially confronts crucial constraints from theory and experiments. Should there exist more than one copy of such a multiplet, it is possible to predict that number from the requirements of a strong electroweak phase transition and agreement with the latest constraints. We aim to probe this speciﬁc issue in this study in the context of N degenerate scalar triplets governed by a global O( N ) symmetry. We fold in important constraints from h → γγ signal strength, dark matter direct detection and Landau pole behaviour. A combined analysis reveals N (cid:38) 70 for a strong phase transition and consistency with the constraints. We also look into possible gravitational wave signals in the parameter regions of interest.
Differentiable optimization of the Debye-Wolf integral for light shaping and adaptive optics in two-photon microscopy

Control of light through a microscope objective with a high numerical aperture is a common requirement in applications such as optogenetics, adaptive optics, or laser processing. Light propagation, including polarization effects, can be described under these conditions using the Debye-Wolf diffraction integral. Here, we take advantage of differentiable optimization and machine learning for efficiently optimizing the Debye-Wolf integral for such applications. For light shaping we show that this optimization approach is suitable for engineering arbitrary three-dimensional point spread functions in a two-photon microscope. For differentiable model-based adaptive optics (DAO), the developed method can find aberration corrections with intrinsic image features, for example neurons labeled with genetically encoded calcium indicators, without requiring guide stars. Using computational modeling we further discuss the range of spatial frequencies and magnitudes of aberrations which can be corrected with this approach.
Lopsided galaxies in a cosmological context: a new galaxy–halo connection

Disc galaxies commonly show asymmetric features in their morphology, such as warps and lopsidedness. These features can provide key information regarding the recent evolution of a given disc galaxy. In the nearby Universe, up to ∼30 per cent of late-type galaxies display a global non-axisymmetric lopsided mass distribution. However, the origin of this perturbation is not well understood. In this work, we study the origin of lopsided perturbations in simulated disc galaxies extracted from the TNG50 simulation of the IllustrisTNG project. We statistically explore different excitation mechanisms for this perturbation, such as direct satellite tidal interactions and distortions of the underlying dark matter distributions. We also characterize the main physical conditions that lead to lopsided perturbations. 50 per cent of our sample galaxy have lopsided modes m = 1 greater than ∼0.12. We find a strong correlation between internal galaxy properties, such as central stellar surface density and disc radial extension with the strength of lopsided modes. The majority of lopsided galaxies have lower central surface densities and more extended discs than symmetric galaxies. As a result, such lopsided galaxies are less self-gravitationally cohesive, and their outer disc region is more susceptible to different types of external perturbations. However, we do not find strong evidence that tidal interactions with satellite galaxies are the main driving agent of lopsided modes. Lopsided galaxies tend to live in asymmetric dark matter haloes with high spin, indicating strong galaxy–halo connections in late-type lopsided galaxies.
Extreme eigenvalues of Laplacian random matrices with Gaussian entries

A Laplacian matrix is a real symmetric matrix whose row and column sums are zero. We investigate the limiting distribution of the largest eigenvalue of a Laplacian random matrix with Gaussian entries. Unlike many classical matrix ensembles, this random matrix model contains dependent entries. After properly shifting and scaling, we show the largest eigenvalue converges to the Gumbel distribution as the dimension of the matrix tends to infinity. While the largest diagonal entry is also shown to have Gumbel fluctuations, there is a rather surprising difference between its deterministic centering term and the centering term required for the largest eigenvalue.
Resonance enhancement of the electromagnetic interaction between two charged particles in the bound state in the continuum

In bound states in the continuum (BIC) the mass of a composite particle is greater than the total mass of its constituents. The BIC state is investigated in a system of two charged particles [Formula: see text] and [Formula: see text] with different masses ([Formula: see text]), using the ladder Bethe–Salpeter equation. We demonstrate that there are two momentum space regions for the positive binding energy, in which the electromagnetic interaction between the particles is strongly enhanced, and the effective coupling constant turns out to be equal to [Formula: see text], where [Formula: see text] is the fine structure constant. The interaction resonance confines the constituents in the BIC state with the positive binding energy, which is of the order of the mass of the lighter particle from this pair.
COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training

Modern Deep Learning (DL) models have grown to sizes requiring massive clusters of specialized, high-end nodes to train. Designing such clusters to maximize both performance and utilization--to amortize their steep cost--is a challenging task requiring careful balance of compute, memory, and network resources. Moreover, a plethora of each model's tuning knobs drastically affect the performance, with optimal values often depending on the underlying cluster's characteristics, which necessitates a complex cluster-workload co-design process. To facilitate the design space exploration of such massive DL training clusters, we introduce COMET, a holistic cluster design methodology and workflow to jointly study the impact of parallelization strategies and key cluster resource provisioning on the performance of distributed DL training. We develop a step-by-step process to establish a reusable and flexible methodology, and demonstrate its application with case studies of training large models on cluster configurations of variable compute, memory, and network resources. Our case studies demonstrate COMET's utility in identifying promising architectural optimization directions and guiding system designers in configuring key model and cluster parameters. To illustrate, cluster configuration comparisons identify performance differences of up to 7.7x and highlight performance optimization opportunities of up to 1.4x when employing memory expansion as an optimization technique.
SPARTAN: Sparse Hierarchical Memory for Parameter-Efficient Transformers

Fine-tuning pre-trained language models (PLMs) achieves impressive performance on a range of downstream tasks, and their sizes have consequently been getting bigger. Since a different copy of the model is required for each task, this paradigm is infeasible for storage-constrained edge devices like mobile phones. In this paper, we propose SPARTAN, a parameter efficient (PE) and computationally fast architecture for edge devices that adds hierarchically organized sparse memory after each Transformer layer. SPARTAN freezes the PLM parameters and fine-tunes only its memory, thus significantly reducing storage costs by re-using the PLM backbone for different tasks. SPARTAN contains two levels of memory, with only a sparse subset of parents being chosen in the first level for each input, and children cells corresponding to those parents being used to compute an output representation. This sparsity combined with other architecture optimizations improves SPARTAN's throughput by over 90% during inference on a Raspberry Pi 4 when compared to PE baselines (adapters) while also outperforming the latter by 0.1 points on the GLUE benchmark. Further, it can be trained 34% faster in a few-shot setting, while performing within 0.9 points of adapters. Qualitative analysis shows that different parent cells in SPARTAN specialize in different topics, thus dividing responsibility efficiently.
Interpretability with full complexity by constraining feature information

Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, rendering the effects of those features on a model's output comprehensible but at the expense of model complexity. We approach interpretability from a new angle: constrain the information about the features without restricting the complexity of the model. Borrowing from information theory, we use the Distributed Information Bottleneck to find optimal compressions of each feature that maximally preserve information about the output. The learned information allocation, by feature and by feature value, provides rich opportunities for interpretation, particularly in problems with many features and complex feature interactions. The central object of analysis is not a single trained model, but rather a spectrum of models serving as approximations that leverage variable amounts of information about the inputs. Information is allocated to features by their relevance to the output, thereby solving the problem of feature selection by constructing a learned continuum of feature inclusion-to-exclusion. The optimal compression of each feature -- at every stage of approximation -- allows fine-grained inspection of the distinctions among feature values that are most impactful for prediction. We develop a framework for extracting insight from the spectrum of approximate models and demonstrate its utility on a range of tabular datasets.
Dynamic compression of highly compressible porous media with application to snow compaction

A new experimental and theoretical approach is presented to examine the dynamic lift forces that are generated in the compression of both fresh powder snow and wind-packed snow. At typical skiing velocities of 10 to 30ms$^{-1}$ the duration of contact of a ski or snowboard with the snow will vary from 0.05 to 0.2s depending on the length of the planing surface and its speed. No one, to our knowledge, has previously measured the dynamic behaviour of snow on such a short time scale and, thus, there are no existing measurements of the excess pore pressure that can build-up in snow on this time scale. Using a novel porous cylinder–piston apparatus, we have measured the excess pore pressure that would build-up beneath the piston surface and have also measured its subsequent decay due to the venting of the air from the snow at the porous wall of the cylinder. In further experiments, in which the air is slowly and deliberately drained to avoid a build-up in pore pressure, we have been able to separate out the force exerted by the ice crystal phase as a function of its instantaneous deformation. A theoretical model for the pore pressure relaxation in the porous cylinder is then developed using consolidation theory. Dramatically different dynamic behaviour is observed for two different snow types, one (wind-packed) giving a steady continuous relaxation of the excess pore pressure and the other (fresh powder) leading to a piston rebound with negative pore pressure. A feature of the rebound is the apparent debonding of sintered ice crystals after maximum compression. This behaviour is described well by introducing a debonding coefficient where the debonding force is proportional to the expansion velocity of the medium. The experimental and theoretical approach presented herein and the previous generalized lubrication theory for compressible porous media, have laid the foundation for understanding the detailed dynamic response of soft porous layers to rapid deformation.
Handling and extracting key entities from customer conversations using Speech recognition and Named Entity recognition

— In this modern era of technology with e-commerce developing at a rapid pace, it is very important to understand customer requirements and details from a business conversation. It is very crucial for customer retention and satisfaction. Extracting key insights from these conversations is very important when it comes to developing their product or solving their issue. Understanding customer feedback, responses, and important details of the product are essential and it would be done using Named entity recognition (NER). For extracting the entities we would be converting the conversations to text using the optimal speech-to-text model. The model would be a two-stage network in which the conversation is converted to text. Then, suitable entities are extracted using robust techniques using a NER BERT transformer model. This will aid in the enrichment of customer experience when there is an issue which is faced by them. If a customer faces a problem he will call and register his complaint. The model will then extract the key features from this conversation which will be necessary to look into the problem. These features would include details like the order number, and the exact problem. All these would be extracted directly from the conversation and this would reduce the effort of going through the conversation again.
Sensing the Position of a Single Scatterer in an Opaque Medium by Mutual Scattering

Tracking the position of a single particle within opaque samples is a major challenge in optics. It is usually done by sending a single beam of waves to a target and studying the scattered waves, as in typical X-ray experiments for weakly interacting samples. The recent development of multiple-beam techniques such as optical wavefront shaping, where light scattering with multiple properly phased incident beams, has opened new potential for research of strongly interacting opaque samples. In this work, we show that mutual scattering is a powerful method to detect the displacement of a single scatterer in an optically dense sample of many (up to $\mathrm{N}=1000)$ similar scatterers. We illustrate that our multiple-beam technique provides speckle patterns with an angular sensitivity at least 10 times higher than the traditional one-beam techniques and can be a powerful tool in imaging and metrology applications.
Hierarchically Clustered PCA and CCA via a Convex Clustering Penalty

We introduce an unsupervised learning approach that combines the truncated singular value decomposition with convex clustering to estimate within-cluster directions of maximum variance/covariance (in the variables) while simultaneously hierarchically clustering (on observations). In contrast to previous work on joint clustering and embedding, our approach has a straightforward formulation, is readily scalable via distributed optimization, and admits a direct interpretation as hierarchically clustered principal component analysis (PCA) or hierarchically clustered canonical correlation analysis (CCA). Through numerical experiments and real-world examples relevant to precision medicine, we show that our approach outperforms traditional and contemporary clustering methods on underdetermined problems ( p (cid:29) N with tens of observations) and scales to large datasets (e.g., N = 100 , 000 ; p = 1 , 000 ) while yielding interpretable dendrograms of hierarchical per-cluster principal components or canonical variates.
An Integrated Ride-Matching Model for Shared Mobility on Demand Services

Shared mobility on demand (MoD) services are receiving increased attention as many high- volume ride-hailing companies are oﬀering shared services (e.g. UberPool, LyftLine) at an increasing rate. Also, the advent of autonomous vehicles (AVs) promises further operational opportunities to beneﬁt from these developments as AVs enable a centrally operated and fully connected ﬂeet. There are two fundamental tasks for a shared MoD service: ride-matching and vehicle rebalancing. Traditionally, these two functions are performed sequentially and independently. In this paper, we propose and formulate an integrated ride-matching problem which aims to integrate ride-matching and rebalancing into a single formulation. The integrated problem beneﬁts from interactions between these two tasks. We also propose a methodology to solve the integrated shared ride-matching problem by using supply level information based on a grid representation of the city network. We demonstrate the eﬀectiveness of the proposed methodology through a comparative case study using a benchmark sequential approach and an open source data set. Our results show that the integrated model is able to serve at least the same amount of passengers with signiﬁcant gains in terms of level of service and sustainability metrics.
Optimizing sparse fermionic Hamiltonians

We consider the problem of approximating the ground state energy of a fermionic Hamiltonian using a Gaussian state. In sharp contrast to the dense case [1, 2], we prove that strictly q-local sparse fermionic Hamiltonians have a constant Gaussian approximation ratio; the result holds for any connectivity and interaction strengths. Sparsity means that each fermion participates in a bounded number of interactions, and strictly q-local means that each term involves exactly q fermionic (Majorana) operators. We extend our proof to give a constant Gaussian approximation ratio for sparse fermionic Hamiltonians with both quartic and quadratic terms. With additional work, we also prove a constant Gaussian approximation ratio for the so-called sparse SYK model with strictly 4-local interactions (sparse SYK-4 model). In each setting we show that the Gaussian state can be efficiently determined. Finally, we prove that the O(n−1/2) Gaussian approximation ratio for the normal (dense) SYK-4 model extends to SYK-q for even q>4, with an approximation ratio of O(n1/2–q/4). Our results identify non-sparseness as the prime reason that the SYK-4 model can fail to have a constant approximation ratio [1, 2].