Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Ultrasound Imaging of Speed-of-Sound and Attenuation Using an Acoustic Mirror

Speed-of-sound and attenuation of ultrasound waves vary in the tissues. There exist methods in the literature that allow for spatially reconstructing the distribution of group speed-of-sound (SoS) and frequency-dependent ultrasound attenuation (UA) using reflections from an acoustic mirror positioned at a known distance from the transducer. These methods utilize a conventional ultrasound transducer operating in pulse-echo mode and a calibration protocol with measurements in water. In this study, we introduce a novel method for reconstructing local SoS and UA maps as a function of acoustic frequency through Fourier-domain analysis and by fitting linear and power-law dependency models in closed form. Frequency-dependent SoS and UA together characterize the tissue comprehensively in spectral domain within the utilized transducer bandwidth. In simulations, our proposed methods are shown to yield low reconstruction error: 0.01 dB/cm⋅MHz y for attenuation coefficient and 0.05 for the frequency exponent. For tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast was achieved. Attenuation exponents in a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.3 and 0.6 on average. Linear dispersion of SoS in a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.3 and 4.0 m/s⋅MHz on average. These findings were reproducible when the inclusion and substrate materials were exchanged. Bulk loss modulus in the bovine muscle sample was computed to be approximately 4 times the bulk loss modulus in the gelatin-cellulose mixture. Such frequency-dependent characteristics of SoS and UA, and bulk loss modulus may therefore differentiate tissues as potential diagnostic biomarkers.
Atomized Search Length: Beyond User Models

We argue that current IR metrics, modeled on optimizing user experience, measure too narrow a portion of the IR space. If IR systems are weak, these metrics undersample or completely filter out the deeper documents that need improvement. If IR systems are relatively strong, these metrics undersample deeper relevant documents that could underpin even stronger IR systems, ones that could present content from tens or hundreds of relevant documents in a user-digestible hierarchy or text summary. We reanalyze over 70 TREC tracks from the past 28 years, showing that roughly half undersample top ranked documents and nearly all undersample tail documents. We show that in the 2020 Deep Learning tracks, neural systems were actually near-optimal at top-ranked documents, compared to only modest gains over BM25 on tail documents. Our analysis is based on a simple new systems-oriented metric, 'atomized search length', which is capable of accurately and evenly measuring all relevant documents at any depth.
Surface-Aligned Neural Radiance Fields for Controllable 3D Human Synthesis

We propose a new method for reconstructing control-lable implicit 3D human models from sparse multi-view RGB videos. Our method defines the neural scene repre-sentation on the mesh surface points and signed distances from the surface of a human body mesh. We identify an indistinguishability issue that arises when a point in 3D space is mapped to its nearest surface point on a mesh for learning surface-aligned neural scene representation. To address this issue, we propose projecting a point onto a mesh surface using a barycentric interpolation with modi-fied vertex normals. Experiments with the ZJU-MoCap and Human3.6M datasets show that our approach achieves a higher quality in a novel-view and novel-pose synthesis than existing methods. We also demonstrate that our method eas-ily supports the control of body shape and clothes. Project page: https://pfnet-research.github. io/surface-aligned-nerf/
Determining the Twist Angle of Bilayer Graphene by Machine Learning Analysis of its Raman Spectrum

With the increasing interest in twisted bilayer graphene (tBLG) of the past years, fast, reliable, and non-destructive methods to precisely determine the twist angle are required. Raman spectroscopy potentially provides such method, given the large amount of information about the state of the graphene that is encoded in its Raman spectrum. However, changes in the Raman spectra induced by the stacking order can be very subtle, thus making the angle identification tedious. In this work, we propose the use of machine learning (ML) analysis techniques for the automated classification of the Raman spectrum of tBLG into a selected range of twist angles. The ML classification proposed here is low computationally demanding, providing fast and accurate results with ~99 % of agreement with the manual labelling of the spectra. The flexibility and non-invasive nature of the Raman measurements, paired with the predictive accuracy of the ML, is expected to facilitate the exploration of the nascent research of tBLG. Moreover, the present work showcases how the currently available open-source tools facilitate the study and integration of ML-based techniques.
Collisions of Young Disk Galaxies in the Early Universe

In the local universe, disk galaxies are generally well evolved and Toomre stable. Their collisions with satellite galaxies naturally produce ring structures, which have been observed and extensively studied. By contrast, at high redshifts, disk galaxies are still developing and clumpy. These young galaxies interact with each other more frequently. However, the products of their collisions remain elusive. Here, we systematically study the minor collisions between a clumpy galaxy and a satellite on orbits with different initial conditions, and find a new structure that is different from the local collisional ring galaxies. The clumpiness of the target galaxy is fine-tuned by the values of Toomre parameter, Q. Interestingly, a thick and knotty ring structure is formed without any sign of a central nucleus in the target galaxy. Our results provide a promising explanation of the empty ring galaxy recently observed in R5519 at redshift z = 2.19. Moreover, we show that the clumpy state of the collided galaxy exists for a much longer timescale compared to isolated self-evolved clumpy galaxies that have been widely investigated.
What Does Dynamic Optimality Mean in External Memory?

A data structure A is said to be dynamically optimal over a class of data structures C if A is constant-competitive with every data structure C ∈ C . Much of the research on binary search trees in the past forty years has focused on studying dynamic optimality over the class of binary search trees that are modiﬁed via rotations (and indeed, the question of whether splay trees are dynamically optimal has gained notoriety as the so-called dynamic-optimality conjecture). Recently, researchers have extended this to consider dynamic optimality over certain classes of external-memory search trees. In particular, Demaine, Iacono, Koumoutsos, and Langerman propose a class of external-memory trees that support a notion of tree rotations, and then give an elegant data structure, called the Belga B-tree, that is within an O (log log N )-factor of being dynamically optimal over this class. In this paper, we revisit the question of how dynamic optimality should be deﬁned in external memory. A deﬁning characteristic of external-memory data structures is that there is a stark asymmetry between queries and inserts/updates/deletes: by making the former slightly asymptotically slower, one can make the latter signiﬁcantly asymptotically faster (even allowing for operations with sub-constant amortized I/Os). This asymmetry makes it so that rotation-based search trees are not optimal (or even close to optimal) in insert/update/delete-heavy external-memory workloads. To study dynamic optimality for such workloads, one must consider a diﬀerent class of data structures. The natural class of data consider what we call Such adapt dynamically to the locality properties of an input sequence in order to optimize the interactions between diﬀerent inserts/updates/deletes and queries. We also present a new form of beyond-worst-case analysis that allows for us to formally study a continuum between static and dynamic optimality. Finally, we give a novel data structure, called the J ε llo Tree, that is statically optimal and that achieves dynamic optimality for a large natural class of inputs deﬁned by our beyond-worst-case analysis.
An Optical Spectrum of the Diffuse Galactic Light from BOSS and IRIS

We present a spectrum of the diffuse Galactic light (DGL) between 3700 and 10,000 Å, obtained by correlating optical sky intensity with far-infrared dust emission. We use nearly 250,000 blank-sky spectra from BOSS/SDSS-III together with IRIS-reprocessed maps from the IRAS satellite. The larger sample size compared to SDSS-II results in a factor-of-2 increase in signal to noise. We combine these data sets with a model for the optical/far-infrared correlation that accounts for self-absorption by dust. The spectral features of the DGL agree remarkably well with the features present in stellar spectra. There is evidence for a difference in the DGL continuum between the regions covered by BOSS in the northern and southern Galactic hemispheres. We interpret the difference at red wavelengths as the result of a difference in stellar populations, with mainly old stars in both regions, but a higher fraction of young stars in the south. There is also a broad excess in the southern DGL spectrum over the prediction of a simple radiative transfer model, without a clear counterpart in the north. We interpret this excess, centered at ∼6500 Å, as evidence for luminescence in the form of extended red emission. The observed strength of the 4000 Å break indicates that at most ∼7% of the dust-correlated light at 4000 Å can be due to blue luminescence. Our DGL spectrum provides constraints on dust scattering and luminescence, independent of measurements of extinction.
Sample Selection with Deadline Control for Efficient Federated Learning on Heterogeneous Clients

Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data. Unlike centralized training that is usually based on carefully-organized data, FL deals with on-device data that are often unfiltered and imbalanced. As a result, conventional FL training protocol that treats all data equally leads to a waste of local computational resources and slows down the global learning process. To this end, we propose FedBalancer , a systematic FL framework that actively selects clients’ training samples. Our sample selection strategy prioritizes more “in-formative” data while respecting privacy and computational capabilities of clients. To better utilize the sample selection to speed up global training, we further introduce an adaptive deadline control scheme that predicts the optimal deadline for each round with varying client train data. Compared with existing FL algorithms with deadline configuration methods, our evaluation on five datasets from three different domains shows that FedBalancer improves the time-to-accuracy performance by 1.22 ∼ 4.62 × while improving the model accuracy by 1.0 ∼ 3.3%. We also show that FedBalancer is readily applicable to other FL approaches by demonstrating that FedBalancer improves the convergence speed and accuracy when operating jointly with three different FL algorithms.
RobustCalibration: Robust Calibration of Computer Models in R

Two fundamental research tasks in science and engineering are forward predictions and data inversion. This article introduces a recent R package RobustCalibration for Bayesian data inversion and model calibration by experiments and field observations. Mathematical models for forward predictions are often written in computer code, and they can be computationally expensive slow to run. To overcome the computational bottleneck from the simulator, we implemented a statistical emulator from the RobustGaSP package for emulating both scalar-valued or vector-valued computer model outputs. Both posterior sampling and maximum likelihood approach are implemented in the RobustCalibration package for parameter estimation. For imperfect computer models, we implement Gaussian stochastic process and the scaled Gaussian stochastic process for modeling the discrepancy function between the reality and mathematical model. This package is applicable to various types of field observations, such as repeated experiments and multiple sources of measurements. We discuss numerical examples of calibrating mathematical models that have closed-form expressions, and differential equations solved by numerical methods.
Efficient-Dyn: Dynamic Graph Representation Learning via Event-based Temporal Sparse Attention Network

GStatic graph neural networks have been widely used in modeling and representation learning of graph structure data. However, many real-world problems, such as social networks, ﬁnancial transactions, recommendation systems, etc., are dynamic, that is, nodes and edges are added or deleted over time. Therefore, in recent years, dynamic graph neural networks have received more and more attention from researchers. In this work, we propose a novel dynamic graph neural network, E ﬃ cient-Dyn. It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure. Therefore, while avoiding the use of snapshots to cause information loss, it also achieves a ﬁner time granularity, which is close to what continuous networks could provide. In addition, we also designed a lightweight module, Sparse Temporal Transformer, to compute node representations through both structural neighborhoods and temporal dynamics. Since the fully-connected attention conjunction is simpliﬁed, the computation cost is far lower than the current state-of-the-arts. Link prediction experiments are conducted on both continuous and discrete graph datasets. Through comparing with several state-of-the-art graph embedding baselines, the experimental results demonstrate that E ﬃ cient-Dyn has a faster inference speed while having competitive performance.
Multi-Person Passive WiFi Indoor Localization With Intelligent Reflecting Surface

The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices. However, due to the fundamental limited spatial resolution of WiFi signals, it is still very difficult to achieve accurate localization with existing commodity WiFi devices. To tackle this problem, in this paper, we propose to exploit the degree of freedom provided by the Intelligent Reflecting Surface (IRS), which is composed of a large number of controllable reflective elements, to modulate the spatial distribution of WiFi signals and thus break down the spatial resolution limitation of WiFi signals to achieve accurate localization. Specifically, in the single-person scenario, we derive the closed-form solution to optimally control the phase shift of the IRS elements. In the multi-person scenario, we propose a Side-lobe Cancellation Algorithm to eliminate the near-far effect to achieve accurate localization of multiple persons in an iterative manner. Extensive simulation results demonstrate that without any change to the existing WiFi infrastructure, the proposed framework can locate multiple moving persons passively with sub-centimeter accuracy under multipath interference and random noise.
Tunable giant two-dimensional optical beam shift from a tilted linear polarizer

We demonstrate an intriguing giant optical beam shift in a tilted polarizer system analogous to the Imbert-Fedorov shift in partial reflection around the Brewster's angle of incidence. We explain this giant shift using a generalized theoretical treatment for beam shift in a tilted uniaxial anisotropic material incorporating the three-dimensional orientation of its optic axis in the laboratory frame. We further demonstrate regulated control and tunability of both the magnitude and direction of the shifts by wisely tuning the input polarization and the corresponding orientation angles. These findings open up the possibility of precise micron-level beam steering using a simple linear polarizer.
Simulating Jellyfish Galaxies: A Case Study for a Gas-rich Dwarf Galaxy

We investigate the formation of jellyfish galaxies using radiation-hydrodynamic simulations of gas-rich dwarf galaxies with a multiphase interstellar medium (ISM). We find that the ram-pressure-stripped (RPS) ISM is the dominant source of molecular clumps in the near wake within 10 kpc from the galactic plane, while in situ formation is the major channel for dense gas in the distant tail of the gas-rich galaxy. Only 20% of the molecular clumps in the near wake originate from the intracluster medium (ICM); however, the fraction reaches 50% in the clumps located at 80 kpc from the galactic center since the cooling time of the RPS gas tends to be short owing to the ISM–ICM mixing (≲10 Myr). The tail region exhibits a star formation rate of 0.001–0.01 M ⊙ yr−1, and most of the tail stars are born in the stripped wake within 10 kpc from the galactic plane. These stars induce bright Hα blobs in the tail, while Hα tails fainter than 6 × 1038 erg s−1 kpc−2 are mostly formed via collisional radiation and heating due to mixing. We also find that the stripped tails have intermediate X-ray-to-Hα surface brightness ratios (1.5 ≲ F X/F Hα ≲ 20), compared to the ISM (≲1.5) or pure ICM (≫20). Our results suggest that jellyfish features emerge when the ISM from gas-rich galaxies is stripped by strong ram pressure, mixes with the ICM, and enhances the cooling in the tail.
Graviton scattering amplitudes in first quantisation

We give a pedagogical review to alternative, first quantised approaches to calculating graviton scattering amplitudes, giving an introduction to string inspired approaches and presenting more recent work based on the worldline formalism of quantum field theory that is motivated by these historic results. We describe how these first quantised techniques can greatly simplify the determination of such amplitudes, in particular reducing the number of Feynman-like diagrams that enter the computation and leading to compact results.
Cortical and STN spectral changes during limb movements in PD patients with and without dystonia

Background: Dystonia is a prevalent yet under-studied motor feature of Parkinson disease (PD). Although considerable efforts have focused on brain oscillations related to the cardinal symptoms of PD, whether dystonia is associated with specific electrophysiological features is unclear. Objectives: To investigate subcortical and cortical field potentials at rest and during contralateral hand and foot movements in PD patients with versus without dystonia. Methods: We examined the prevalence and somatotopy of dystonia in PD patients undergoing deep brain stimulation (DBS) surgery. We recorded intracranial electrophysiology from sensorimotor cortex and directional DBS electrodes in subthalamic nucleus (STN), during both rest and voluntary contralateral limb movements. We used wavelet transforms and linear mixed models to characterize spectral content in patients with and without dystonia (n=25). Results: Dystonia was highly prevalent at enrollment (61%) and most common in the foot (78%). PD patients with dystonia display greater subthalamic theta and alpha power during movement (p < 0.05) but not at rest. Regardless of dystonia status, cortical recordings display prominent beta desynchronization (13-30 Hz) during movement, whereas STN signals show increases in spectral power at lower frequencies (4-20 Hz), with peaks at 6.0 +/- 3.3 and 4.2 +/- 2.9 Hz during hand and foot movements, respectively (p < 0.03). Conclusions: Whereas cortex was characterized by beta desynchronization during hand and foot movements similarly, STN showed limb-specific low frequency activity which was increased in PD patients with dystonia. These findings may help elucidate why PD-related dystonia is most common in the foot and help guide future closed-loop DBS devices.
Exploring the impact of shielding advice on the health and wellbeing of individuals identified as extremely vulnerable and advised to shield in Southwest England amid the COVID-19 pandemic: A mixed-methods evaluation

Objective: Explore the impact and responses to public health advice on the health and wellbeing of individuals identified as clinically extremely vulnerable (CEV) and advised to shield (not leave home for 12 weeks at start of the pandemic) in Southwest England during the first COVID-19 lockdown. Design: Mixed-methods study; structured survey and follow-up semi-structured interviews. Setting: Communities served by Bristol, North Somerset & South Gloucestershire Clinical Commissioning Group. Participants: 204 people (57% female, 54% >69 years, 94% White British, 64% retired) in Southwest England identified as CEV and were advised to shield completed the survey. Thirteen survey respondents participated in follow-up interviews (53% female, 40% >69years, 100% White British, 61% retired). Results: Receipt of official communication from NHS England or General Practitioner (GP) was considered by participants as the legitimate start of shielding. 80% of survey responders felt they received all relevant advice needed to shield, yet interviewees criticised the timing of advice and often sought supplementary information. Shielding behaviours were nuanced, adapted to suit personal circumstances, and waned over time. Few interviewees received community support, although food boxes and informal social support were obtained by some. Worrying about COVID-19 was common for survey responders (90%). Since shielding had begun, physical and mental health reportedly worsened for 35% and 42% of survey responders respectively. 21% of survey responders scored 10 or more on the PHQ-9 questionnaire indicating possible depression and 15% scored 10 or more on the GAD-7 questionnaire indicating possible anxiety. Conclusions: This research highlights the difficulties in providing generic messaging that is applicable and appropriate given the diversity of individuals identified as CEV and the importance of sharing tailored and timely advice to inform shielding decisions. Providing messages that reinforce self-determined action and assistance from support services could reduce the negative impact of shielding on mental health and feelings of social isolation.