Miracle-dz/UniFAR-FTUs huggingface dataset:
Global Snowfall Measurement with the CloudSat Space-borne Radar

Contact Information 1hiley@wisc.edu IV. Effect of Attenuation VI. Sensitivity to Z-S relationship 1. For a given snowfall rate S, the ice particle size distribution (PSD) is first derived using the Field et al. (2005) parameterization. 2. PSD + backscatter coefficient data from DDA calculations of various ice particle models (Hong 2007; Kim et al. 2007; Liu 2008b) is used to calculate Z for 25 different ice particle model and 3 different temperatures. See Fig. 1 for example Liu (2008b) ice habits. 3. Best-fit line derived using power law relationship (1-σ fits also derived to define upper and lower bounds). (Fig. 2)
GloDyNE: Global Topology Preserving Dynamic Network Embedding

Learning low-dimensional topological representation of a network in dynamic environments is attracting much attention due to the time-evolving nature of many real-world networks. The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step. The idea of most existing DNE methods is to capture the topological changes at or around the most affected nodes (instead of all nodes) and accordingly update node embeddings. Unfortunately, this kind of approximation, although can improve efficiency, cannot effectively preserve the global topology of a dynamic network at each time step, due to not considering the inactive sub-networks that receive accumulated topological changes propagated via the high-order proximity. To tackle this challenge, we propose a novel node selecting strategy to diversely select the representative nodes over a network, which is coordinated with a new incremental learning paradigm of Skip-Gram based embedding approach. The extensive experiments show GloDyNE, with a small fraction of nodes being selected, can already achieve the superior or comparable performance w.r. t. the state-of-the-art DNE methods in three typical downstream tasks. Particularly, GloDyNE significantly outperforms other methods in the graph reconstruction task, which demonstrates its ability of global topology preservation.
MultiCheXNet: A Multi-Task Learning Deep Network For Pneumonia-like Diseases Diagnosis From X-ray Scans

We present MultiCheXNet, an end-to-end Multi-task learning model, that is able to take advantage of different X-rays data sets of Pneumonia-like diseases in one neural architecture, performing three tasks at the same time; diagnosis, segmentation and localization. The common encoder in our architecture can capture useful common features present in the different tasks. The common encoder has another advantage of efficient computations, which speeds up the inference time compared to separate models. The specialized decoders heads can then capture the task-specific features. We employ teacher forcing to address the issue of negative samples that hurt the segmentation and localization performance. Finally,we employ transfer learning to fine tune the classifier on unseen pneumonia-like diseases. The MTL architecture can be trained on joint or dis-joint labeled data sets. The training of the architecture follows a carefully designed protocol, that pre trains different sub-models on specialized datasets, before being integrated in the joint MTL model. Our experimental setup involves variety of data sets, where the baseline performance of the 3 tasks is compared to the MTL architecture performance. Moreover, we evaluate the transfer learning mode to COVID-19 data set,both from individual classifier model, and from MTL architecture classification head.
A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint

Multi-contrast magnetic resonance (MR) image registration is essential in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless, the efficiency and performance of the existing registration algorithms can still be improved. In this paper, we propose a novel unsupervised learning-based framework to achieve accurate and efficient multi-contrast MR image registrations. Specifically, an end-to-end coarse-to-fine network architecture consisting of affine and deformable transformations is designed to get rid of both the multi-step iteration process and the complex image preprocessing operations. Furthermore, a dual consistency constraint and a new prior knowledge-based loss function are developed to enhance the registration performances. The proposed method has been evaluated on a clinical dataset that consists of 555 cases, with encouraging performances achieved. Compared to the commonly utilized registration methods, including Voxelmorph, SyN, and LDDMM, the proposed method achieves the best registration performance with a Dice score of 0.826 in identifying stroke lesions. More robust performance in low-signal areas is also observed. With regards to the registration speed, our method is about 17 times faster than the most competitive method of SyN when testing on a same CPU.
Quantifying nonequilibrium thermodynamic operations in a multiterminal mesoscopic system

We investigate a multiterminal mesoscopic conductor in the quantum Hall regime, subject to temperature and voltage biases. The device can be considered as a nonequilibrium resource acting on a working substance. We previously showed that cooling and power production can occur in the absence of energy and particle currents from a nonequilibrium resource (calling this an N-demon). Here we allow energy or particle currents from the nonequilibrium resource and find that the device seemingly operates at a better efficiency than a Carnot engine. To overcome this problem, we define free-energy efficiencies which incorporate the fact that a nonequilibrium resource is consumed in addition to heat or power. These efficiencies are well behaved for equilibrium and nonequilibrium resources and have an upper bound imposed by the laws of thermodynamics. We optimize power production and cooling in experimentally relevant parameter regimes.
Algorithmic resolution via weighted blowings up

In this paper we describe a computer implementation of Abramovich, Temkin, and Wlodarczyk's algorithm for resolving singularities in characteristic zero. Their "weighted resolution" algorithm proceeds by repeatedly blowing up along centers that are independent of the history of the past blowing ups, distinguishing weighted resolution from previous resolution algorithms, which all rely on history. We compare our implementation of weighted resolution with that of Villamayor's resolution algorithm, experimentally verifying that weighted resolution is remarkably efficient.
Biconvex Clustering

Abstract Convex clustering has recently garnered increasing interest due to its attractive theoretical and computational properties, but its merits become limited in the face of high-dimensional data. In such settings, pairwise affinity terms that rely on k-nearest neighbors become poorly specified and Euclidean measures of fit provide weaker discriminating power. To surmount these issues, we propose to modify the convex clustering objective so that feature weights are optimized jointly with the centroids. The resulting problem becomes biconvex, and as such remains well-behaved statistically and algorithmically. In particular, we derive a fast algorithm with closed form updates and convergence guarantees, and establish finite-sample bounds on its prediction error. Under interpretable regularity conditions, the error bound analysis implies consistency of the proposed estimator. Biconvex clustering performs feature selection throughout the clustering task: as the learned weights change the effective feature representation, pairwise affinities can be updated adaptively across iterations rather than precomputed within a dubious feature space. We validate the contributions on real and simulated data, showing that our method effectively addresses the challenges of dimensionality while reducing dependence on carefully tuned heuristics typical of existing approaches. Supplementary materials for this article are available online.
On the Sobolev and Lp-Stability of the L2-projection

We show stability of the $L^2$-projection onto Lagrange finite element spaces with respect to (weighted) $L^p$ and $W^{1,p}$-norms for any polynomial degree and for any space dimension under suitable conditions on the mesh grading. This includes $W^{1,2}$-stability in two space dimensions for any polynomial degree and meshes generated by newest vertex bisection. Under realistic assumptions on the mesh grading in three dimensions we show $W^{1,2}$-stability for all polynomial degrees greater than one. We also propose a modified bisection strategy that leads to better $W^{1,p}$-stability. Moreover, we investigate the stability of the $L^2$-projection onto Crouzeix-Raviart elements.
Select, Extract and Generate: Neural Keyphrase Generation with Syntactic Guidance

In recent years, deep neural sequence-to-sequence framework has demonstrated promising results in keyphrase generation. However, processing long documents using such deep neural networks requires high computational resources. To reduce the computational cost, the documents are typically truncated before given as inputs. As a result, the models may miss essential points conveyed in a document. Moreover, most of the existing methods are either extractive (identify important phrases from the document) or generative (generate phrases word by word), and hence they do not benefit from the advantages of both modeling techniques. To address these challenges, we propose \emph{SEG-Net}, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document, and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences. SEG-Net uses a self-attentive architecture, known as, \emph{Transformer} as the building block with a couple of uniqueness. First, SEG-Net incorporates a novel \emph{layer-wise} coverage attention to summarize most of the points discussed in the target document. Second, it uses an \emph{informed} copy attention mechanism to encourage focusing on different segments of the document during keyphrase extraction and generation. Besides, SEG-Net jointly learns keyphrase generation and their part-of-speech tag prediction, where the later provides syntactic supervision to the former. The experimental results on seven keyphrase generation benchmarks from scientific and web documents demonstrate that SEG-Net outperforms the state-of-the-art neural generative methods by a large margin in both domains.
Human β-secretase and Alzheimer’s disease

Alzheimer’s disease (AD) is a huge unmet medical need. Studies of the brain pathology and genetics of familial forms of AD have led to the amyloid cascade hypothesis, stating that Aβ42, a proteolytic breakdown product of the large amyloid precursor protein, plays an early and critical role in AD pathogenesis. Aβ42 generation requires two proteases, β- and γ-secretase, and inhibition of these enzymes is a key focus of AD drug development. Progress in this area has been slow because these enzymes were not identified. Using an expression cloning strategy we have identified a novel membrane bound aspartic protease, BACE1 and demonstrated that it exhibits all known properties of β-secretase. The enzyme has been characterised in detail. The crystal structure, which is critical for rational inhibitor design, has been solved and shown to be very similar to that of other pepsin family members. Our most recent BACE1 knockout studies show that BACE1 is critical for Aβ generation; however the knockout mice show an otherwise normal phenotype, raising the possibility that therapeutic BACE1 inhibition could be accomplished without major mechanism based toxicity.
Learning Long-term Visual Dynamics with Region Proposal Interaction Networks

Learning long-term dynamics models is the key to understanding physical common sense. Most existing approaches on learning dynamics from visual input sidestep long-term predictions by resorting to rapid re-planning with short-term models. This not only requires such models to be super accurate but also limits them only to tasks where an agent can continuously obtain feedback and take action at each step until completion. In this paper, we aim to leverage the ideas from success stories in visual recognition tasks to build object representations that can capture inter-object and object-environment interactions over a long-range. To this end, we propose Region Proposal Interaction Networks (RPIN), which reason about each object's trajectory in a latent region-proposal feature space. Thanks to the simple yet effective object representation, our approach outperforms prior methods by a significant margin both in terms of prediction quality and their ability to plan for downstream tasks, and also generalize well to novel environments. Code, pre-trained models, and more visualization results are available at this https URL.
What to Do When You Can’t Do It All: Temporal Logic Planning with Soft Temporal Logic Constraints

In this paper, we consider a temporal logic planning problem in which the objective is to find an infinite trajectory that satisfies an optimal selection from a set of soft specifications expressed in linear temporal logic (LTL) while nevertheless satisfying a hard specification expressed in LTL. Our previous work considered a similar problem in which linear dynamic logic for finite traces (LDLf), rather than LTL, was used to express the soft constraints. In that work, LDLf was used to impose constraints on finite prefixes of the infinite trajectory. By using LTL, one is able not only to impose constraints on the finite prefixes of the trajectory, but also to set ‘soft’ goals across the entirety of the infinite trajectory. Our algorithm first constructs a product automaton, on which the planning problem is reduced to computing a lasso with minimum cost. Among all such lassos, it is desirable to compute a shortest one. Though we prove that computing such a shortest lasso is computationally hard, we also introduce an efficient greedy approach to synthesize short lassos nonetheless. We present two case studies describing an implementation of this approach, and report results of our experiment comparing our greedy algorithm with an optimal baseline.
Polynomial scaling of QAOA for ground-state preparation of the fully-connected p-spin ferromagnet.

We show that the quantum approximate optimization algorithm (QAOA) can construct with polynomially scaling resources the ground state of the fully-connected p-spin Ising ferromagnet, a problem that notoriously poses severe difficulties to a Quantum Annealing (QA) approach, due to the exponentially small gaps encountered at first-order phase transition for ${\rm p} \ge 3$. For a target ground state at arbitrary transverse field, we find that an appropriate QAOA parameter initialization is necessary to achieve a good performance of the algorithm when the number of variational parameters $2{\rm P}$ is much smaller than the system size ${\rm N}$, because of the large number of sub-optimal local minima. Instead, when ${\rm P}$ exceeds a critical value ${\rm P}^*_{\rm N} \propto {\rm N}$, the structure of the parameter space simplifies, as all minima become degenerate. This allows to achieve the ground state with perfect fidelity with a number of parameters scaling extensively with ${\rm N}$, and with resources scaling polynomially with ${\rm N}$.
A SOFIA Survey of [C ii] in the Galaxy M51. II. [C ii] and CO Kinematics across the Spiral Arms

We present the first complete, velocity-resolved [C ii] 158 μm image of the M51 grand-design spiral galaxy, observed with the upgraded German Receiver for Astronomy at Terahertz frequencies instrument on the Stratospheric Observatory for Infrared Astronomy. [C ii] is an important tracer of various phases of the interstellar medium (ISM), including ionized gas, neutral atomic, and diffuse molecular regions. We combine the [C ii] data with H i, CO, 24 μm dust continuum, FUV, and NIR K-band observations to study the evolution of the ISM across M51's spiral arms in both position–position and position–velocity space. Our data show strong velocity gradients in H i, 12CO, and [C ii] at the locations of the stellar arms (traced by K-band data) with a clear offset in position–velocity space between upstream molecular gas (traced by 12CO) and downstream star formation (traced by [C ii]). We compare the observed position–velocity maps across the spiral arms with synthetic observations from numerical simulations of galaxies with both dynamical and quasi-stationary steady spiral arms that predict both tangential and radial velocities at the location of the spiral arms. We find that our observations, based on the observed velocity gradients and associated offset between CO and [C ii], are consistent with the presence of shocks in the spiral arms in the inner parts of M51 and in the arm connecting the companion galaxy, M51b, in the outer parts of M51.
Data Cleansing with Contrastive Learning for Vocal Note Event Annotations

Data cleansing is a well studied strategy for cleaning erroneous labels in datasets, which has not yet been widely adopted in Music Information Retrieval. Previously proposed data cleansing models do not consider structured (e.g. time varying) labels, such as those common to music data. We propose a novel data cleansing model for time-varying, structured labels which exploits the local structure of the labels, and demonstrate its usefulness for vocal note event annotations in music. %Our model is trained in a contrastive learning manner by automatically creating local deformations of likely correct labels. Our model is trained in a contrastive learning manner by automatically contrasting likely correct labels pairs against local deformations of them. We demonstrate that the accuracy of a transcription model improves greatly when trained using our proposed strategy compared with the accuracy when trained using the original dataset. Additionally we use our model to estimate the annotation error rates in the DALI dataset, and highlight other potential uses for this type of model.
Magnetohydrodynamic waves excited by a coupling between gravitational waves and a strongly magnetized plasma in binaries of neutron stars

Coalescence of binary neutron stars (BNSs) is one of the sources of gravitational waves (GWs) able to be detected by ground-based interferometric detectors. The event GW170817 was the first observed in the gravitational and electromagnetic spectra, showing through this joint analysis a certain compatibility with the models of short gamma-ray bursts (sGRBs) to explain the signature of this system. Due to the intense magnetic fields of the neutron stars, the plasma magnetosphere stays strongly magnetized and the propagation of the GW through plasma can excite magnetohydrodynamic (MHD) modes such as Alfven and magnetosonic waves. The MHD modes carry energy and momentum through the plasma, suggesting a mechanism to accelerate the matter during the coalescence of the binaries, explaining some characteristics of the fireball model of the sGRBs. We present a semianalytical formalism to determine the energy transferred by the GW-MHD interaction during the inspiral phase of the stars. Using the inferred physical parameters for GW170817, we show that the energy in the plasma can reach maximum value $\sim 10^{35}\,{\rm J}$ ($\sim 10^{32}\,{\rm J}$) for the Alfven mode (magnetosonic mode) if the angle formed between the background magnetic field and the GW propagation direction is $\theta = \pi / 4$. Particularly, for $\theta = \pi / 2$ only the magnetosonic mode is in coherence with the GWs. In this case, the excited energy in the plasma reaches maximum value $\sim 10^{36} {\rm J}$. If the magnetic field on the surface of the progenitors of the event GW170817 was $\sim 2\times 10^{9}\,{\rm T}$ then energies comparable to those inferred for the GRB 170817A could be obtained. In particular, our semianalytical formalism show consistence with the results obtained by other authors through full general relativistic magnetohydrodynamics (GRMHD) simulations. [ABRIDGED]
Follow the Object: Curriculum Learning for Manipulation Tasks with Imagined Goals

Learning robot manipulation through deep reinforcement learning in environments with sparse rewards is a challenging task. In this paper we address this problem by introducing a notion of imaginary object goals. For a given manipulation task, the object of interest is first trained to reach a desired target position on its own, without being manipulated, through physically realistic simulations. The object policy is then leveraged to build a predictive model of plausible object trajectories providing the robot with a curriculum of incrementally more difficult object goals to reach during training. The proposed algorithm, Follow the Object (FO), has been evaluated on 7 MuJoCo environments requiring increasing degree of exploration, and has achieved higher success rates compared to alternative algorithms. In particularly challenging learning scenarios, e.g. where the object's initial and target positions are far apart, our approach can still learn a policy whereas competing methods currently fail.
Fixed Point Strategies in Data Science

The goal of this article is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solve a great variety of problems. They are seen to constitute a natural environment to explain the behavior of advanced convex optimization methods as well as of recent nonlinear methods in data science which are formulated in terms of paradigms that go beyond minimization concepts and involve constructs such as Nash equilibria or monotone inclusions. We review the pertinent tools of fixed point theory and describe the main state-of-the-art algorithms for provenly convergent fixed point construction. We also incorporate additional ingredients such as stochasticity, block-implementations, and non-Euclidean metrics, which provide further enhancements. Applications to signal and image processing, machine learning, statistics, neural networks, and inverse problems are discussed.
Speaker dependent acoustic-to-articulatory inversion using real-time MRI of the vocal tract

Acoustic-to-articulatory inversion (AAI) methods estimate articulatory movements from the acoustic speech signal, which can be useful in several tasks such as speech recognition, synthesis, talking heads and language tutoring. Most earlier inversion studies are based on point-tracking articulatory techniques (e.g. EMA or XRMB). The advantage of rtMRI is that it provides dynamic information about the full midsagittal plane of the upper airway, with a high 'relative' spatial resolution. In this work, we estimated midsagittal rtMRI images of the vocal tract for speaker dependent AAI, using MGC-LSP spectral features as input. We applied FC-DNNs, CNNs and recurrent neural networks, and have shown that LSTMs are the most suitable for this task. As objective evaluation we measured normalized MSE, Structural Similarity Index (SSIM) and its complex wavelet version (CW-SSIM). The results indicate that the combination of FC-DNNs and LSTMs can achieve smooth generated MR images of the vocal tract, which are similar to the original MRI recordings (average CW-SSIM: 0.94).