Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Magnetic Field Orientations in Dense Cores on Gas Kinematics in Protostellar Envelopes

Theoretically, misalignment between the magnetic field and rotational axis in a dense core is considered to be dynamically important in the star formation process; however, the extent of this influence remains observationally unclear. For a sample of 32 Class 0 and I protostars in the Perseus Molecular Cloud, we analyzed gas motions using C18O data from the SMA MASSES survey and the magnetic field structures using 850 μm polarimetric data from the JCMT BISTRO-1 survey and archive. We do not find any significant correlation between the velocity gradients in the C18O emission in the protostellar envelopes at a 1000 au scale and the misalignment between the outflows and magnetic field orientations in the dense cores at a 4000 au scale, and there is also no correlation between the velocity gradients and the angular dispersions of the magnetic fields. However, a significant dependence on the misalignment angles emerges after we normalize the rotational motion by the infalling motion, where the ratios increase from ≲1 to ≳1 with increasing misalignment angle. This suggests that the misalignment could prompt angular momentum transportation to the envelope scale but is not a dominant factor in determining the envelope rotation, and other parameters, such as mass accretion in protostellar sources, also play an important role. These results remain valid after taking into account projection effects. The comparison between our estimated angular momentum in the protostellar envelopes and the sizes of the known protostellar disks suggests that significant angular momentum is likely lost between radii of ∼1000 and 100 au in protostellar envelopes.
Generic Identiﬁability for REMIS: The Cointegrated Unit Root VAR ∗

The “ RE trieval from MI xed S ampling” (REMIS) approach based on blocking developed in Anderson et al. (2016a) is concerned with retrieving an underlying high frequency model from mixed frequency observations. In this paper we investigate parameter-identiﬁability in the Johansen (1995) vector error correction model for mixed frequency data. We prove that from the second moments of the blocked process after taking diﬀerences at lag N ( N is the slow sampling rate), the parameters of the high frequency system are generically identiﬁed. We treat the stock and the ﬂow case as well as deterministic terms.
The Effect of Aspect Changes on Near-Earth Asteroid Phase Curves

Phase curves of asteroids are typically considered to depend solely on the scattering properties of airless particulate surfaces and the size of the object being studied. In this study, we demonstrate the additional dependence of phase curves on object shape, rotation pole orientation, and viewing geometry over an apparition. Variations in the phase curve of near-Earth asteroid (159402) 1999 AP10 over its apparition from July 2020 - January 2021 are verified to be due to aspect changes over the apparition. This is achieved through shape modelling of the asteroid and simulation of the phase curve over the apparition. We present simulations of asteroid phase curves over a range of geometries to understand the potential magnitude of this aspect effect, and under which circumstances it can begin to dominate in the phase curves. This dependence on aspect may introduce significant additional uncertainty in the properties derived from phase curve data. We provide and demonstrate software code to estimate the aspect-related uncertainty in near-Earth asteroid phase curves through simulation and model fitting of a randomly generated sample of ellipsoidal asteroid models over the observed viewing geometry. We demonstrate how ignoring this effect may lead to misleading interpretations of the data and underestimation of uncertainties in further studies, such as those in the infrared that use phase curve derived parameters when fitting physical properties of an asteroid.
Extreme Mass Ratio Inspirals triggered by Massive Black Hole Binaries: from relativistic dynamics to cosmological rates

Extreme mass ratio inspirals (EMRIs) are compact binary systems characterized by a mass-ratio q = m/M in the range 10−9–10−4 and represent primary gravitational wave (GW) sources for the forthcoming Laser Interferometer Space Antenna (LISA). While their standard formation channel involves relaxation processes deflecting compact objects on very low angular momentum orbits around the central massive black hole, a number of alternative formation channels has been proposed, including binary tidal break-up, migration in accretion disks and secular and chaotic dynamics around a massive black hole binary (MBHB). In this work, we take an extensive closer look at this latter scenario, investigating how EMRIs can be triggered by a MBHBs, formed in the aftermath of galaxy mergers. By employing a suite of relativistic three-body simulations, we evaluate the efficiency of EMRI formation for different parameters of the MBHB, assessing the importance of both secular and chaotic dynamics. By modelling the distribution of compact objects in galaxy nuclei, we estimate the resulting EMRI formation rate, finding that EMRI are produced in a sharp burst, with peak rates that are 10-100 times higher than the standard two-body relaxation channel, lasting for 106–108 years. By coupling our results with an estimate of the cosmic MBHB merger rate, we finally forecast that LISA could observe ${\cal O}(10)$ EMRIs per year formed by this channel.
Error Bounds for Discrete-Continuous Shortest Path Problems with Application to Free Flight Trajectory Optimization

Two-stage methods addressing continuous shortest path problems start local minimization from discrete shortest paths in a spatial graph. The convergence of such hybrid methods to global minimizers hinges on the discretization error induced by restricting the discrete global optimization to the graph, with corresponding implications on choosing an appropriate graph density. A prime example is flight planning, i.e., the computation of optimal routes in view of flight time and fuel consumption under given weather conditions. Highly efficient discrete shortest path algorithms exist and can be used directly for computing starting points for locally convergent optimal control methods. We derive a priori and localized error bounds for the flight time of discrete paths relative to the optimal continuous trajectory, in terms of the graph density and the given wind field. These bounds allow designing graphs with an optimal local connectivity structure. The properties of the bounds are illustrated on a set of benchmark problems. It turns out that localization improves the error bound by four orders of magnitude, but still leaves ample opportunities for tighter error bounds by a posteriori estimators.
Balls-in-bins models with asymmetric feedback and reflection

Balls-in-bins models describe a random sequential allocation of infinitely many balls into a finite number of bins. In these models a ball is placed into a bin with probability proportional to a given function (feedback function), which depends on the number of existing balls in the bin. Typically, the feedback function is the same for all bins (symmetric feedback), and there are no constraints on the number of balls in the bins. In this paper we study versions of BB models with two bins, in which the above assumptions are violated. In the first model of interest the feedback functions can depend on a bin (BB model with asymmetric feedback). In the case when both feedback functions are power law and superlinear, a single bin receives all but finitely many balls almost surely, and we study the probability that this happens for a given bin. In particular, under certain initial conditions we derive the normal approximation for this probability, which generalizes the result in [5] obtained in the case of the symmetric feedback. The main part of the paper concerns the BB model with asymmetric feedback evolving subject to certain constraints on the numbers of allocated balls. The model can be interpreted as a transient reflecting random walk in a curvilinear wedge, and we obtain a complete classification of its long term behavior.
Compact Model Training by Low-Rank Projection with Energy Transfer

Low-rankness plays an important role in traditional machine learning, but is not so popular in deep learning. Most previous low-rank network compression methods compress the networks by approximating pre-trained models and re-training. However, the optimal solution in the Euclidean space may be quite different from the one in the low-rank manifold. A well-pre-trained model is not a good initialization for the model with low-rank constraints. Thus, the performance of a low-rank compressed network degrades significantly. Compared to other network compression methods such as pruning, low-rank methods attracts less attention in recent years. In this paper, we devise a new training method, low-rank projection with energy transfer (LRPET), that trains low-rank compressed networks from scratch and achieves competitive performance. First, we propose to alternately perform stochastic gradient descent training and projection onto the low-rank manifold. Compared to re-training on the compact model, this enables full utilization of model capacity since solution space is relaxed back to Euclidean space after projection. Second, the matrix energy (the sum of squares of singular values) reduction caused by projection is compensated by energy transfer. We uniformly transfer the energy of the pruned singular values to the remaining ones. We theoretically show that energy transfer eases the trend of gradient vanishing caused by projection. Third, we propose batch normalization (BN) rectification to cut off its effect on the optimal low-rank approximation of the weight matrix, which further improves the performance. Comprehensive experiments on CIFAR-10 and ImageNet have justified that our method is superior to other low-rank compression methods and also outperforms recent state-of-the-art pruning methods. Our code is available at https://github.com/BZQLin/LRPET.
Deep Annotation of Therapeutic Working Alliance in Psychotherapy

The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment. In practice, the working alliance is estimated from a set of scoring ques-tionnaires in an inventory that both the patient and the therapists ﬁll out. In this work, we propose an analytical framework of directly inferring the therapeutic working alliance from the natural language within the psychotherapy sessions in a turn-level resolution with deep embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each psychotherapy session can be transcribed and generated in real-time from the session speech recordings, and these embedded dialogues are compared with the distributed representations of the statements in the working alliance inventory. We demonstrate, in a real-world dataset with over 950 sessions of psychotherapy treatments in anxiety, depression, schizophrenia and suicidal patients, the effectiveness of this method in mapping out trajectories of patient-therapist alignment and the interpretability that can offer insights in clinical psychiatry. We believe such a framework can be provide timely feedback to the therapist re-garding the quality of the conversation in interview sessions.
A Simple Approach to Adversarial Robustness in Few-shot Image Classification

Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable to adversarial examples, posing a question regarding their generalization capabilities. Recent works have tried to combine meta-learning approaches with adversarial training to improve the robustness of few-shot classifiers. We show that a simple transfer-learning based approach can be used to train adversarially robust few-shot classifiers. We also present a method for novel classification task based on calibrating the centroid of the few-shot category towards the base classes. We show that standard adversarial training on base categories along with calibrated centroid-based classifier in the novel categories, outperforms or is on-par with state-of-the-art advanced methods on standard benchmarks for few-shot learning. Our method is simple, easy to scale, and with little effort can lead to robust few-shot classifiers. Code is available here: \url{https://github.com/UCDvision/Simple_few_shot. git}
VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration

Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on a single type of distortion, such as speech denoising or dereverberation. However, speech signals can be degraded by several different distortions simultaneously in the real world. It is thus important to extend speech restoration models to deal with multiple distortions. In this paper, we introduce VoiceFixer, a unified framework for high-fidelity speech restoration. VoiceFixer restores speech from multiple distortions (e.g., noise, reverberation, and clipping) and can expand degraded speech (e.g., noisy speech) with a low bandwidth to 44.1 kHz full-bandwidth high-fidelity speech. We design VoiceFixer based on (1) an analysis stage that predicts intermediate-level features from the degraded speech, and (2) a synthesis stage that generates waveform using a neural vocoder. Both objective and subjective evaluations show that VoiceFixer is effective on severely degraded speech, such as real-world historical speech recordings. Samples of VoiceFixer are available at https://haoheliu.github. io/voicefixer.
Decomposed Meta-Learning for Few-Shot Named Entity Recognition

Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. In this paper, we present a decomposed meta-learning approach which addresses the problem of few-shot NER by sequentially tackling few-shot span detection and few-shot entity typing using meta-learning. In particular, we take the few-shot span detection as a sequence labeling problem and train the span detector by introducing the model-agnostic meta-learning (MAML) algorithm to find a good model parameter initialization that could fast adapt to new entity classes. For few-shot entity typing, we propose MAML-ProtoNet, i.e., MAML-enhanced prototypical networks to find a good embedding space that can better distinguish text span representations from different entity classes. Extensive experiments on various benchmarks show that our approach achieves superior performance over prior methods.
Density Matrix Renormalization Group with Tensor Processing Units

Google's Tensor Processing Units (TPUs) are integrated circuits specifically built to accelerate and scale up machine learning workloads. They can perform fast distributed matrix multiplications and therefore be repurposed for other computationally intensive tasks. In this work we demonstrate the use of TPUs for accelerating and scaling up the density matrix renormalization group (DMRG), a powerful numerical approach to compute the ground state of a local quantum many-body Hamiltonian. The cost of DMRG scales with system size $N$ as $O(ND^3)$, where the so-called bond dimension $D$ regulates how expressive the underlying matrix product state (MPS) variational ansatz is. We consider lattice models in two spatial dimensions, with square lattices of size $10\times 10$ (free fermions) and $20\times 20$ (transverse field Ising model), for which the required MPS bond dimension is known to scale at least as $\exp(\sqrt{N})$. Using half of a TPU v3 pod (namely $1,\! 024$ TPU v3 cores) we reached an unprecedentedly large bond dimension $D = 2^{16} = 65,\! 536$, for which optimizing a single MPS tensor took about 2 minutes.
Codec Compression Efficiency Evaluation of MPEG-5 part 2 (LCEVC) using Objective and Subjective Quality Assessment

With the increasing advancements in video compression efficiency achieved by newer codecs such as HEVC, AV1, and VVC, and intelligent encoding strategies, as well as improved bandwidth availability,there has been a proliferation and acceptance of newer services such as Netflix, Twitch, etc. However, such higher compression efficiencies are achieved at the cost of higher complexity and encoding delay, while many applications are delay sensitive. Hence, there is a requirement for faster, more efficient codecs to achieve higher encoding efficiency without significant trade-off in terms of both complexity and speed. We present in this work an evaluation of the latest MPEG-5 Part 2 Low Complexity Enhancement Video Coding (LCEVC) for live gaming video streaming applications. The results are presented in terms of bitrate savings using both subjective and objective quality measures as well as a comparison of the encoding speeds. Our results indicate that, for the encoding settings used in this work, LCEVC outperforms both x264 and x265 codecs in terms of bitrate savings using VMAF by approximately 42\% and 38\%. Using subjective results, it is found that LCEVC outperforms the respective base codecs, especially for low bitrates. This effect is more evident for x264 than for x265, i.e., for the latter the absolute improvement of quality scores is smaller. The objective and subjective results as well as sample video sequences are made available as part of an open dataset, LCEVC-LiveGaming at https://github.com/NabajeetBarman/LCEVC-LiveGaming.
Gradient corrections to the local-density approximation in the one-dimensional Bose gas

The local density approximation (LDA) is the central technical tool in the modeling of quantum gases in trapping potentials. It consists in treating the gas as an assembly of independent mesoscopic ﬂuid cells at equilibrium with a local chemical potential, and it is justiﬁed when the correlation length is larger than the size of the cells. The LDA is often regarded as a crude approximation, particularly in the ground state of the one-dimensional (1D) Bose gas, where the correlation length is "therefore said to be" inﬁnite (in the sense that correlation functions decay as a power law). Here we take another look at the LDA. The local density ρ ( x ) is viewed as a functional of the trapping potential V ( x ) , to which one applies a gradient expansion. The zeroth order in that expansion is the LDA. The ﬁrst-order correction in the gradient expansion vanishes due to reﬂection symmetry. At second order, there are two corrections proportional to d 2 V/dx 2 and ( dV/dx ) 2 , and we propose a method to determine the corresponding coeﬃcients by a perturbative calculation in the Lieb-Liniger model. This leads to an expression for the coeﬃcients in terms of matrix elements of the density operator, which can in principle be evaluated numerically for an arbitrary coupling constant; here we show how to eﬃciently evaluate the coeﬃcient associated to the curvature of the potential d 2 V/dx 2 , which dominates the deviation to LDA near local minima or maxima of the trapping potential. Both coeﬃcients are evaluated analytically in the limits of inﬁnite repulsion (hard-core bosons) and small repulsion (quasi-condensate). The corrected LDA density proﬁles are compared to DMRG calculations, with signiﬁcant improvement compared to zeroth-order LDA.
Brittle fracture studied by ultra-high-speed synchrotron X-ray diffraction imaging

Crack propagation in a silicon single-crystal wafer is tracked in situ using synchrotron-based ultra-high speed X-ray diffraction imaging. The high spatio-temporal resolution reached in diffraction imaging mode allows for assessing different parameters such as crack velocity or post crack movements of the separated wafers.
Portfolio Optimization Using a Consistent Vector-Based MSE Estimation Approach

This paper is concerned with optimizing the weights of the global minimum-variance portfolio (GMVP) in high-dimensional settings where both observation and population dimensions grow at a bounded ratio. Optimizing the GMVP weights is highly influenced by the data covariance matrix estimation. In a high-dimensional setting, it is well known that the sample covariance matrix is not a proper estimator of the true covariance matrix since it is not invertible when we have fewer observations than the data dimension. Even with more observations, the sample covariance matrix may not be well-conditioned. This paper determines the GMVP weights based on a regularized covariance matrix estimator to overcome the abovementioned difficulties. Unlike other methods, the proper selection of the regularization parameter is achieved by minimizing the mean squared error of an estimate of the noise vector that accounts for the uncertainty in the data mean estimation. Using random-matrix-theory tools, we derive a consistent estimator of the achievable mean squared error that allows us to find the optimal regularization parameter using a simple line search. Simulation results demonstrate the effectiveness of the proposed method when the data dimension is larger than, or of the same order of, the number of data samples.
Coarse Personalization

Advances in estimating heterogeneous treatment effects enable firms to personalize marketing mix elements and target individuals at an unmatched level of granularity, but feasibility constraints limit such personalization. In practice, firms choose which unique treatments to offer and which individuals to offer these treatments with the goal of maximizing profits: we call this the coarse personalization problem. We propose a two-step solution that makes segmentation and targeting decisions in concert. First, the firm personalizes by estimating conditional average treatment effects. Second, the firm discretizes by utilizing treatment effects to choose which unique treatments to offer and who to assign to these treatments. We show that a combination of available machine learning tools for estimating heterogeneous treatment effects and a novel application of optimal transport methods provides a viable and efficient solution. With data from a large-scale field experiment for promotions management, we find that our methodology outperforms extant approaches that segment on consumer characteristics or preferences and those that only search over a prespecified grid. Using our procedure, the firm recoups over 99.5% of its expected incremental profits under fully granular personalization while offering only five unique treatments. We conclude by discussing how coarse personalization arises in other domains.