Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the Presence of PAHs and Hydrogenated PAHs in the Young Stellar Object Mon R2 and the Taurus Dense Cloud Toward Elias 16

This study assesses the contribution of neutral polycyclic aromatic hydrocarbons (PAHs) and hydrogenated PAHs (H n -PAHs) in H2O-ices to the 3 μm and 5–8 μm regions of the infrared absorption spectrum of the Young Stellar Object, Mon R2 IRS 3, and the Taurus dense cloud in the direction of Elias 16. We compare the astronomical spectra to the previously published laboratory data of matrix-isolated PAHs and H n -PAHs in H2O-ices. For the molecules in this study, the band positions, FWHMs, and integrated band strengths have been measured. For the PAHs considered here, the ratio of the experimental A(C–H) stretch (in H2O ices) to the theoretical A(C–H) stretch (in the gas phase) is consistently about 0.10, meaning that the trove of theoretical data can be reliably scaled to compare to the astronomical spectra. We find that the fractional percentage contribution to the 5–8 μm region for Mon R2 IRS 3 ranges between 2.7 and 3.9 for neutral PAHs and 0.25–1.2 for H n -PAHs. The best match to the observed 3.25 μm profile in the Mon R2 IRS 3 spectrum is accomplished with the laboratory-measured coronene:H2O spectrum. Using this spectrum we estimate that neutral PAHs contribute up to 12.0% of the cosmic carbon budget for Mon R2 IRS 3 and <14.5% for Elias 16. Neutral hydrogenated PAHs contribute mainly to the 3.47 μm absorption and very little to the 5 to 8 μm region. For neutral H n -PAHs, we estimate that they contribute up to 0.6% of the cosmic carbon budget for Mon R2 IRS 3 and <1% for Elias 16.
Elemental Abundances of Major Elements in the Solar Wind as Measured in Genesis Targets and Implications on Solar Wind Fractionation

We present elemental abundance data of C, N, O, Na, Mg, Al, Ca, and Cr in Genesis silicon targets. For Na, Mg, Al, and Ca, data from three different solar wind (SW) regimes are also presented. Data were obtained by backside depth profiling using secondary ion mass spectrometry. The accuracy of these measurements exceeds those obtained by in situ observations; therefore, the Genesis data provide new insights into elemental fractionation between Sun and SW, including differences between SW regimes. We integrate previously published noble gas and hydrogen elemental abundances from Genesis targets, as well as preliminary values for K and Fe. The abundances of the SW elements measured display the well-known fractionation pattern that correlates with each element’s first-ionization potential (FIP). When normalized either to spectroscopic photospheric solar abundances or to those derived from CI-chondritic meteorites, the fractionation factors of low-FIP elements (K, Na, Al, Ca, Cr, Mg, Fe) are essentially identical within uncertainties, but the data are equally consistent with increasing fractionation with decreasing FIP. The elements with higher FIPs between ∼11 and ∼16 eV (C, N, O, H, Ar, Kr, Xe) display a relatively well-defined trend of increasing fractionation with decreasing FIP, if normalized to modern 3D photospheric model abundances. Among the three Genesis regimes, the fast SW displays the least elemental fractionation for almost all elements (including the noble gases) but differences are modest: for low-FIP elements, the precisely measured fast–slow SW variations are less than 3%.
Diurnal Variability and Detectability of Vegetation Red Edge of Earth-like Exoplanets

The vegetation red edge (VRE) is a unique spectral fingerprint of light-harvesting vegetation on Earth and provides a robust remote detectable surface biosignature of exoplanets. To improve the detectability and sensitivity, we have studied the diurnal variability of VRE in the disk-integrated spectra of Earth and also Earth analogs in the case of different observing geometry conditions. Simulation results show that the VRE index varies from <−0.4 to >0.6 at a diurnal timescale for both present and also Late Triassic Earth, and the maximum variation of VRE in 1 day changes by >3 times with different observing geometry conditions. This means that the extraterrestrial light-harvesting vegetation (even if it really exists) will not be efficiently detectable without proper observing geometry conditions and time, especially in the case of the exoplanets covered with thick clouds. The VRE temporal variation curve can also be used to retrieve the cloud cover fraction and continent distribution of exoplanets with relatively high precision. Several observational strategies are proposed to detect the light-harvesting vegetation and retrieve the planetary information from the planet’s VRE variation signals, and a mock observation is also demonstrated.
Robust MMSE Precoding and Power Allocation for Cell-Free Networks

We consider the downlink of a cell-free massive multiple-input multiple-output (MIMO) system with \textcolor{red}{single}-antenna access points (APs) and single-antenna users. An iterative robust minimum mean-square error (RMMSE) precoder based on generalized loading is developed to mitigate interference in the presence of imperfect channel state information (CSI). An achievable rate analysis is carried out and optimal and uniform power allocation schemes are developed based on the signal-to-interference-plus-noise ratio. An analysis of the computational costs of the proposed RMMSE and existing schemes is also presented. Numerical results show the improvement provided by the proposed RMMSE precoder against linear minimum mean-square error, zero-forcing and conjugate beamforming precoders in the presence of imperfect CSI.
Mock hyperbolic reflection spaces and Frobenius groups of finite Morley rank

We define the notion of mock hyperbolic reflection spaces and use it to study Frobenius groups, in particular in the context of groups of finite Morley rank including the so-called bad groups. We show that connected Frobenius groups of finite Morley rank and odd type with nilpotent complement split or interpret a bad field of characteristic zero. Furthermore, we show that mock hyperbolic reflection spaces of finite Morley rank satisfy certain rank inequalities, implying in particular that any connected Frobenius group of odd type and Morley rank at most ten either splits or is a simple non-split sharply 2-transitive group of characteristic different from 2 and of Morley rank 8 or 10.
UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction

Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing surfaces from multi-view images and synthesizing novel views. Unfortunately, existing methods such as DVR or IDR require accurate per-pixel object masks as supervision. At the same time, neural radiance fields have revolutionized novel view synthesis. However, NeRF’s estimated volume density does not admit accurate surface reconstruction. Our key insight is that implicit surface models and radiance fields can be formulated in a unified way, enabling both surface and volume rendering using the same model. This unified perspective enables novel, more efficient sampling procedures and the ability to reconstruct accurate surfaces without input masks. We compare our method on the DTU, BlendedMVS, and a synthetic indoor dataset. Our experiments demonstrate that we outperform NeRF in terms of reconstruction quality while performing on par with IDR without requiring masks.
Efficient Retrieval Optimized Multi-task Learning

Recently, there have been significant advances in neural methods for tackling knowledge-intensive tasks such as open domain question answering (QA). These advances are fueled by combining large pre-trained language models with learnable retrieval of documents. Majority of these models use separate encoders for learning query representation, passage representation for the retriever and an additional encoder for the downstream task. Using separate encoders for each stage/task occupies a lot of memory and makes it difficult to scale to a large number of tasks. In this paper, we propose a novel Retrieval Optimized Multi-task (ROM) framework for jointly training self-supervised tasks, knowledge retrieval, and extractive question answering. Our ROM approach presents a unified and generalizable framework that enables scaling efficiently to multiple tasks, varying levels of supervision, and optimization choices such as different learning schedules without changing the model architecture. It also provides the flexibility of changing the encoders without changing the architecture of the system. Using our framework, we achieve comparable or better performance than recent methods on QA, while drastically reducing the number of parameters.
Extracting Temporal Event Relation with Syntactic-Guided Temporal Graph Transformer

Extracting temporal relations (e.g., before, after, concurrent) among events is crucial to nat-ural language understanding. Previous studies mainly rely on neural networks to learn effective features or manual-crafted linguistic features for temporal relation extraction, which usually fail when the context between two events is complex or wide. Inspired by the examination of available temporal relation annotations and human-like cognitive procedures, we propose a new Temporal Graph Transformer network to (1) explicitly ﬁnd the connection between two events from a syntactic graph constructed from one or two continuous sentences, and (2) automatically locate the most indicative temporal cues from the path of the two event mentions as well as their surrounding concepts in the syntactic graph with a new temporal-oriented attention mechanism. Experiments on MATRES and TB-Dense datasets show that our approach significantly outperforms previous state-of-the-art methods on both end-to-end temporal relation extraction and temporal relation classiﬁcation.
Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications

Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patient’s neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-observer errors. Recently, automated statistical imaging analysis techniques have been proposed to detect and segment MS lesions based on MRI voxel intensity. However, their effectiveness is limited by the heterogeneity of both MRI data acquisition techniques and the appearance of MS lesions. By learning complex lesion representations directly from images, deep learning techniques have achieved remarkable breakthroughs in the MS lesion segmentation task. Here, we provide a comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and discuss current and future clinical applications. Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.
Accelerating Surgical Robotics Research: Reviewing 10 Years of Research with the dVRK

Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialties. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to re-purpose decommissioned da Vinci surgical systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this paper, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain this initiative and build upon it.
Selective Harmonic Elimination in a Cascaded H-Bridge Multilevel Inverter fed by High-Frequency Isolated DC-DC Converter

In recent years, the use of multilevel inverter has become popular due to its many advantages. Due to the popularity of multilevel inverters in industries and in applications that require a wide range of voltages, there have been many challenges to achieve high-quality voltage. Many pieces of research have been done to solve the problem of annoying harmonics in multilevel inverters. In this study, pulse width modulation (PWM) has been proposed as a switching method. Using the Selective Harmonic Elimination (SHE) technique, the inverter switching can be carried out in low frequency, and also, this method can reduce the annoying harmonics significantly. However, in the lower modulation index, eliminating such harmonics is challenging, resulting in a considerable increase in output voltage distortion. This study suggests a way to solve the problem. In this research, eliminating selected harmonics in a multilevel inverter with variable DC links is proposed. The DC-link variable method in this study is to use a high-frequency isolated DC-DC converter. The proposed method is verified on a 5-level Cascaded H-Bridge (CHB) inverter using the SHE-PWM method solved by particle swarm optimization (PSO) algorithm.
Variational Relational Point Completion Network

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they mostly learn a deterministic partial-to-complete mapping, but overlook structural relations in man-made objects. To tackle these challenges, this paper proposes a variational framework, Variational Relational point Completion network (VRC-Net) with two appealing properties: 1) Probabilistic Modeling. In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds. One path consumes complete point clouds for reconstruction by learning a point VAE. The other path generates complete shapes for partial point clouds, whose embedded distribution is guided by distribution obtained from the reconstruction path during training. 2) Relational Enhancement. Specifically, we carefully design point self-attention kernel and point selective kernel module to exploit relational point features, which refines local shape de tails conditioned on the coarse completion. In addition, we contribute a multi-view partial point cloud dataset (MVP dataset) containing over 100,000 high-quality scans, which renders partial 3D shapes from 26 uniformly distributed camera poses for each 3D CAD model. Extensive experiments demonstrate that VRCNet outperforms state-of-the-art methods on all standard point cloud completion benchmarks. Notably, VRCNet shows great generalizability and robustness on real-world point cloud scans.
skweak: Weak Supervision Made Easy for NLP

We present skweak, a versatile, Python-based software toolkit enabling NLP developers to apply weak supervision to a wide range of NLP tasks. Weak supervision is an emerging machine learning paradigm based on a simple idea: instead of labelling data points by hand, we use labelling functions derived from domain knowledge to automatically obtain annotations for a given dataset. The resulting labels are then aggregated with a generative model that estimates the accuracy (and possible confusions) of each labelling function. The skweak toolkit makes it easy to implement a large spectrum of labelling functions (such as heuristics, gazetteers, neural models or linguistic constraints) on text data, apply them on a corpus, and aggregate their results in a fully unsupervised fashion. skweak is especially designed to facilitate the use of weak supervision for NLP tasks such as text classification and sequence labelling. We illustrate the use of skweak for NER and sentiment analysis. skweak is released under an open-source license and is available at https://github.com/NorskRegnesentral/skweak
Quantitative Age Estimation of Supernova Remnants and Associated Pulsars

The age of a supernova remnant (SNR) is, though undoubtedly one of the most important properties for study of its evolution, difficult to estimate reliably in most cases. In this study, we compare the dynamical and plasma ages of the SNRs and characteristic ages of their associated pulsars with the corresponding SNRs’ ages that are generally thought to be reliable (t r): historical and light-echo ages of the SNRs, kinematic ages of the ejecta knots and kinematic ages of the associated neutron stars (NS). The kinematic age of ejecta knots or an NS is the time that they have taken to reach the current positions from the explosion center. We use all of the available 24 systems for which t r is already available (historical, light-echo, and ejecta kinematic ages) or measurable (NS kinematic age). We estimate the NS kinematic ages for eight SNR-NS systems by determining quantitatively the geometric centers of the SNR shells. The obtained t r ranges from 33 yr to ≈400 kyr. We find that the two SNR ages, dynamical and plasma ages, are consistent with t r within a factor of four, whereas the characteristic ages of the pulsars differ from t r by more than a factor of four in some systems. Using the t r summarized in this work, we present the initial spin periods of the associated pulsars, which are more strictly constrained than the previous works, as well.
Controlling Pivoting Gait Using Graph Model Predictive Control

Pivoting gait, in which a robot iteratively tilts an object, rotates it around a vertex, and then places it down on the floor, is efficient for manipulating a large and heavy object with a relatively small manipulating force. However, pivoting gait can easily fail, even with a small external disturbance, due to its instability. To address this problem, we propose a controller to robustly control the object’s motion during pivoting gait by introducing two gait modes, i.e., double-support mode, which can manipulate a relatively light object with higher speed, and quadruple-support mode, which can manipulate a relatively heavy object with slower speed. To control the pivoting gait, a graph model predictive control is applied by considering these two gait modes. The experiments show that by adaptively switching the gait mode according to the applied external disturbance, a robot can stably perform the pivoting gait even when an external disturbance is applied to the object. The experimental results lead us to automate the manipulation of a large and heavy object.
Cross-population coupling of neural activity based on Gaussian process current source densities

Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As we show here, however, once disparate source signals are decoupled, their trial-to-trial fluctuations become more accessible, and cross-population correlations become more apparent. To decouple sources we introduce a general framework for estimation of current source densities (CSDs). In this framework, the set of LFPs result from noise being added to the transform of the CSD by a biophysical forward model, while the CSD is considered to be the sum of a zero-mean, stationary, spatiotemporal Gaussian process, having fast and slow components, and a mean function, which is the sum of multiple time-varying functions distributed across space, each varying across trials. We derived biophysical forward models relevant to the data we analyzed. In simulation studies this approach improved identification of source signals compared to existing CSD estimation methods. Using data recorded from primate auditory cortex, we analyzed trial-to-trial fluctuations in both steady-state and task-evoked signals. We found cortical layer-specific phase coupling between two probes and showed that the same analysis applied directly to LFPs did not recover these patterns. We also found task-evoked CSDs to be correlated across probes, at specific cortical depths. Using data from Neuropixels probes in mouse visual areas, we again found evidence for depth-specific phase coupling of primary visual cortex and lateromedial area based on the CSDs.
Cyclic cocycles in the spectral action

We show that the spectral action, when perturbed by a gauge potential, can be written as a series of Chern–Simons actions and Yang–Mills actions of all orders. In the odd orders, generalized Chern–Simons forms are integrated against an odd ( b , B ) -cocycle, whereas, in the even orders, powers of the curvature are integrated against ( b , B ) -cocycles that are Hochschild cocycles as well. In both cases, the Hochschild cochains are derived from the Taylor series expansion of the spectral action Tr ( f ( D + V )) in powers of V = π D ( A ) , but unlike the Taylor expansion we expand in increasing order of the forms in A . This extends [11], which computes only the scale-invariant part of the spectral action, works in dimension at most 4, and assumes the vanishing tadpole hypothesis. In our situation, we obtain a truly inﬁnite odd ( b , B ) -cocycle. The analysis involved draws from recent results in multiple operator integration, which also allows us to give conditions under which this cocycle is entire, and under which our expansion is absolutely convergent. As a consequence of our expansion and of the gauge invariance of the spectral action, we show that the odd ( b , B ) -cocycle pairs trivially with K 1 .
Divisors of Fourier coefficients of two newforms

For a pair of distinct non-CM newforms of weights at least 2, having rational integral Fourier coefficients $a_{1}(n)$ and $a_{2}(n)$, under GRH, we obtain an estimate for the set of primes $p$ such that $$ \omega(a_1(p)-a_2(p)) \le [ 7k+{1}/{2}+k^{1/5}],$$ where $\omega(n)$ denotes the number of distinct prime divisors of an integer $n$ and $k$ is the maximum of their weights. As an application, under GRH, we show that the number of primes giving congruences between two such newforms is bounded by $[7k+{1}/{2}+k^{1/5} ]$. We also obtain a multiplicity one result for newforms via congruences.
Comparing Representations in Tracking for Event Camera-based SLAM

This paper investigates two typical image-type representations for event camera-based tracking: time surface (TS) and event map (EM). Based on the original TS-based tracker, we make use of these two representations’ complementary strengths to develop an enhanced version. The pro-posed tracker consists of a general strategy to evaluate the optimization problem’s degeneracy online and then switch proper representations. Both TS and EM are motion- and scene-dependent, and thus it is important to figure out their limitations in tracking. We develop six tracker variations and conduct a thorough comparison of them on sequences covering various scenarios and motion complexities. We release our implementations and detailed results to benefit the research community on event cameras: https://github.com/gogojjh/ESVO_extension.