Miracle-dz/UniFAR-FTUs huggingface dataset:
T HE E FFECTS OF P ARTISAN A LIGNMENT ON THE A LLOCATION OF I NTERGOVERNMENTAL T RANSFERS . D IFFERENCES - IN -D IFFERENCES E STIMATES FOR S PAIN

In this paper we test the hypothesis that municipalities aligned with upper-tier grantor governments (i.e., controlled by the same party) will receive more grants than those that are unaligned. We use a rich Spanish database, which provides information on grants received by nearly 900 municipalities during the period 1993-2003 from three different upper-tier governments (i.e., Central , Regional and Upper-local ). Since three elections were held at each tier during this period, we have enough within-municipality variation in partisan alignment to provide differences-in-differences estimates of the effects of alignment on the amount of grants coming from each source. Moreover, the fact that a municipality may simultaneously receive grants from aligned and unaligned grantors allows us to use a triple-differences estimator, which consists of estimating the effects of changing alignment status on the change in grants coming from the aligned grantors relative to the change in grants coming from the unaligned ones. The results suggest that partisan alignment has a sizeable positive effect on the amount of grants received by municipalities. JEL Code: C72, D72.
DeepTIMe: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting

Deep learning has been actively applied to time-series forecasting, leading to a deluge of new autoregressive model architectures. Yet, despite the attractive properties of time-index based models, such as being a continuous signal function over time leading to smooth representations, little attention has been given to them. Indeed, while naive deep time-index based models are far more expressive than the manually predeﬁned function representations of classical time-index based models, they are inadequate for forecasting due to the lack of inductive biases, and the non-stationarity of time-series. In this paper, we propose DeepTIMe, a deep time-index based model trained via a meta-learning formulation which overcomes these limitations, yielding an efﬁcient and accurate forecasting model. Extensive experiments on real world datasets demonstrate that our approach achieves competitive results with state-of-the-art methods, and is highly efﬁcient. Code is available at https://github.com/salesforce/DeepTIMe .
Domain Gap Estimation for Source Free Unsupervised Domain Adaptation with Many Classifiers

In theory, the success of unsupervised domain adaptation (UDA) largely relies on domain gap estimation. However, for source free UDA, the source domain data can not be accessed during adaptation, which poses great challenge of measuring the domain gap. In this paper, we propose to use many classifiers to learn the source domain decision boundaries, which provides a tighter upper bound of the domain gap, even if both of the domain data can not be simultaneously accessed. The source model is trained to push away each pair of classifiers whilst ensuring the correctness of the decision boundaries. In this sense, our many classifiers model separates the source different categories as far as possible which induces the maximum disagreement of many classifiers in the target domain, thus the transferable source domain knowledge is maximized. For adaptation, the source model is adapted to maximize the agreement among pairs of the classifiers. Thus the target features are pushed away from the decision boundaries. Experiments on several datasets of UDA show that our approach achieves state of the art performance among source free UDA approaches and can even compete to source available UDA methods.
Spatially resolved study of the SS 433/W 50 west region with Chandra: X-ray structure and spectral variation of non-thermal emission

The X-ray binary SS 433, embedded in the W 50 nebula (or supernova remnant W 50), shows bipolar jets that are ejected with mildly relativistic velocities and which extend toward the east and west out to scales of tens of parsecs. Previous X-ray observations revealed twin lobes along the jet precession axis that contain compact bright knots dominated by synchrotron radiation, which provide evidence of electron acceleration in this system. Particle acceleration in this system is substantiated by the recently detected gamma rays with energies up to at least 25 TeV. To elucidate the origin of the knots and particle acceleration sites in SS 433/W 50 further, we report here on detailed, spatially resolved X-ray spectroscopy of its western lobe with Chandra. We detect synchrotron emission along the jet precession axis, as well as optically thin thermal emission that is more spatially extended. Between the two previously known knots, w1 and w2, we discover another synchrotron knot, which we call w1.5. We find no significant synchrotron emission between SS 433 and the innermost X-ray knot (w1), suggesting that electrons only begin to be accelerated at w1. The X-ray spectra become gradually steeper from w1 to w2, and then rapidly so immediately outside of w2. Through comparison with a model taking into account electron transport and cooling along the jet, this result indicates that the magnetic field in w2 is substantially enhanced, which also explains its brightness. We discuss possible origins of the enhanced magnetic field of w2 as well as scenarios to explain the other two knots.
Optimistic PAC Reinforcement Learning: the Instance-Dependent View

Optimistic algorithms have been extensively studied for regret minimization in episodic tabular MDPs, both from a minimax and an instance-dependent view. However, for the PAC RL problem, where the goal is to identify a near-optimal policy with high probability, little is known about their instance-dependent sample complexity. A negative result of Wagenmaker et al. (2021) suggests that optimistic sampling rules cannot be used to attain the (still elusive) optimal instance-dependent sample complexity. On the positive side, we provide the first instance-dependent bound for an optimistic algorithm for PAC RL, BPI-UCRL, for which only minimax guarantees were available (Kaufmann et al., 2021). While our bound features some minimal visitation probabilities, it also features a refined notion of sub-optimality gap compared to the value gaps that appear in prior work. Moreover, in MDPs with deterministic transitions, we show that BPI-UCRL is actually near-optimal. On the technical side, our analysis is very simple thanks to a new"target trick"of independent interest. We complement these findings with a novel hardness result explaining why the instance-dependent complexity of PAC RL cannot be easily related to that of regret minimization, unlike in the minimax regime.
6D Camera Relocalization in Visually Ambiguous Extreme Environments

We propose a novel method to reliably estimate the pose of a camera given a sequence of images acquired in extreme environments such as deep seas or extraterrestrial terrains. Data acquired under these challenging conditions are corrupted by textureless surfaces, image degradation, and presence of repetitive and highly ambiguous structures. When naively deployed, the state-of-the-art methods can fail in those scenarios as confirmed by our empirical analysis. In this paper, we attempt to make camera relocalization work in these extreme situations. To this end, we propose: (i) a hierarchical localization system, where we leverage temporal information and (ii) a novel environment-aware image enhancement method to boost the robustness and accuracy. Our extensive experimental results demonstrate superior performance in favor of our method under two extreme settings: localizing an autonomous underwater vehicle and localizing a planetary rover in a Mars-like desert. In addition, our method achieves comparable performance with state-of-the-art methods on the indoor benchmark (7-Scenes dataset) using only 20% training data.
Human Genome Epidemiology (HuGE) Review Meta-Analysis of Vitamin D Receptor Polymorphisms and Type 1 Diabetes: A HuGE Review of Genetic Association Studies

Several polymorphisms in the vitamin D receptor ( VDR ) gene have been reported to be associated with the risk of developing type 1 diabetes, yet published ﬁndings have been conﬂicting. In this study, the authors attempted to evaluate the evidence regarding the association. They searched all relevant reports from original papers published from 1997 to December 2005. Predeﬁned criteria were used to identify 1) case-control association studies exam-ining the Fok I (11 studies), Bsm I (13 studies), Apa I (9 studies), and Taq I (7 studies) polymorphisms and 2) a few family-transmission studies with analysis of these four polymorphisms. In random-effects modeling, the 95% conﬁdence intervals of the summary odds ratios for all four polymorphisms included 1, indicating no effect. Except for Fok I, no heterogeneity was found. The 95% conﬁdence intervals of the transmission proportions all included 0.5, indicating no effect. Thus, the authors found no evidence for an association between VDR gene polymorphisms and type 1 diabetes risk in either case-control studies or family-transmission studies. In fact, a reanalysis of previously published data (McDermott et al., Diabetologia 1997;40:971–5) indicated no evidence of an association as reported.
Time-of-the-Day Dependence of Long-Term Temperature Changes at Urban Meteorological Stations in Japan

Long-term changes of urban temperature was examined by using hourly or four-hourly data at 42 stations in Japan. The magnitude of temperature change (T') was defined by the difference between averages for two periods: one from 1891 to 1928 and the other from 1977 to 1994. At many stations, T' values are larger at nighttime than in the daytime, and are nearly constant between a few hours after sunset and sunrise. Moreover, nighttime T' values are nearly equal to the change of daily minimum temperature (T' min ). However, some stations in the northern part of Japan show larger values of T' min than T' at each fixed time, with a difference of 1°C or more at stations in inland of Hokkaido. This indicates a decrease in nocturnal temperature fluctuation, and is likely to be explained by a weakening of the surface inversion due to urban growth.
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks

Motion forecasting for autonomous driving is a challenging task because complex driving scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem how best to represent and fuse information about road geometry, lane connectivity, time-varying traffic light state, and history of a dynamic set of agents and their interactions into an effective encoding. To model this diverse set of input features, many approaches proposed to design an equally complex system with a diverse set of modality specific modules. This results in systems that are difficult to scale, extend, or tune in rigorous ways to trade off quality and efficiency. In this paper, we present Wayformer, a family of simple and homogeneous attention based architectures for motion forecasting. Wayformer offers a compact model description consisting of an attention based scene encoder and a decoder. In the scene encoder we study the choice of early, late and hierarchical fusion of input modalities. For each fusion type we explore strategies to trade off efficiency and quality via factorized attention or latent query attention. We show that early fusion, despite its simplicity, is not only modality agnostic but also achieves state-of-the-art results on both Waymo Open Motion Dataset (WOMD) and Argoverse leaderboards, demonstrating the effectiveness of our design philosophy.
Multi-target Filter and Detector for Unknown-number Speaker Diarization

—A strong representation of a target speaker can aid in extracting important information regarding the speaker and detecting the corresponding temporal regions in a multi-speaker conversation. In this study, we propose a neural architecture that simultaneously extracts speaker representations that are consistent with the speaker diarization objective and detects the presence of each speaker frame by frame, regardless of the number of speakers in the conversation. A speaker representation (known as a z-vector) extractor and frame-speaker contextualizer, which is realized by a residual network and processing data in both the temporal and speaker dimensions, are integrated into a uniﬁed framework. Testing on the CALLHOME corpus reveals that our model outperforms most methods presented to date. An evaluation in a more challenging case of concurrent speakers ranging from two to seven demonstrates that our model also achieves relative diarization error rate reductions of 26.35% and 6.4% over two typical baselines, namely the traditional x-vector clustering model and attention-based model, respectively.
Direct numerical simulation of turbulent pipe ﬂow up to Re τ ≈ 5200

Well-resolved direct numerical simulations (DNSs) have been performed of the ﬂow in a smooth circular pipe of radius R and axial length 10 πR at friction Reynolds numbers up to Re τ = 5200 using the pseudospectral code OPENPIPEFLOW. Various turbulence statistics are documented and compared with other DNS and experimental data in pipes as well as channels. Small but distinct diﬀerences between various datasets are identiﬁed. The friction factor λ overshoots by 2% and undershoots by 0 . 6% of the Prandtl friction law at low and high Re ranges, respectively. In addition, λ in our results is slightly higher than that in Pirozzoli et al. (Pirozzoli et al. , J. Fluid. Mech., 926, A28, 2021), but matches well with the experiments in Furuichi et al. (Furuichi et al. , Phys. Fluids, 27, 095108, 2015). The log-law indicator function, which is nearly indistinguishable between the pipe and channel ﬂows up to y + = 250, has not yet developed a plateau further away from the wall in the pipes even for the Re τ = 5200 cases. The wall shear stress ﬂuctuations and the inner peak of the axial velocity intensity – which grow monotonically with Re τ – are lower in the pipe than in the channel, but the diﬀerence decreases with increasing Re τ . While the wall values are slightly lower in channel than pipe ﬂows at the same Re τ , the inner peaks of the pressure ﬂuctuations show negligible diﬀerences between them. The Reynolds number scaling of all these quantities agrees with both the logarithmic and defect power laws if the coeﬃcients are properly chosen.
Distribution of HNCO 505−404 in massive star-forming regions

Aims. The goal of this paper is to study the spatial distribution of HNCO in massive star-forming regions, and investigate its spatial association with infrared sources, as well as physical conditions in region of HNCO emission. Methods. We have mapped nine massive star-forming regions in HNCO 505-404 with the Purple Mountain Observatory 13.7m telescope. The C 18 O maps of these sources were obtained simultaneously. Results. The HNCO emission shows compact distribution, with emission peak centred on water masers. Nearly all the HNCO clumps show signs of embedded mid-infrared or far-infrared sources. The FWHM sizes of HNCO clumps are significantly smaller than C 18 O clumps but rather similar to HC3N clumps. We also found good correlation between the integrated intensities, linewidths and LSR velocities of HNCO and HC3N emission, implying similar excitation mechanism of these two species. As such, collisional excitation is likely to be the dominant excitation mechanism for HNCO 505 404 emission in galactic massive star-forming regions.
An extension of Aritn-Verdier duality to non-torsion sheaves.

gives a perfect pairing of finite groups for all i (see [3], [4], [8], [10]). It is the aim of this note to extend this duality theorem to more general sheaves and in particular to non-torsion sheaves. The motivating problem has been the case where F is represented by j//X the Neron model of an abelian variety A/K. This case is particularly interesting since H (X, s/) is closely related to the Tate-Shafarevich group of A. More generally the ease of a separated commutative group scheme G/X of finite type should be included.
Wien Risk assessment after hip fracture Check the “ healthy ” leg !

Background and aim. Functional deficits may increase the risk for further falls after hip fracture. The aim of this study was to elucidate function and range of movement (ROM)-related risk factors. Methods. The consecutive, prospective study included all patients admitted to the rehabilitation unit. The geriatric assessment was performed after admission and before discharge; ROM was measured using the neutral zero method. Falls, defined as unintentionally coming to rest on the floor, were documented. Patients’ functions were analyzed comparing fallers to nonfallers. Results. A total of 1,497 patients with a mean age of 81.1 ± 8.2 years were included. The incidence of falls during rehabilitation was 9.6 per 1,000 patient–days and was related to patients’ age: patients younger than 75 years had an incidence of falls of 5.0, those between 75–84 years had an incidence of 9.5, and those >84 years had an incidence of 12.0, respectively. Fallers showed significantly more functional deficits both in the operated and on the nonoperated leg. Using logistic regression models, especially ROM measured in the frontal plane (hip adduction and abduction) of the nonoperated hip was significantly associated with an increased risk of falling (OR 0.986, 95% CI 0.973–0.999, p = 0.037). Conclusion. Patients are at high risk for falls after hip fracture, with age itself being one of the most dominant risk factors. In addition to previously identified gait and mobility disorders, the physical limitations of arthritis of the “healthy” hip seem to be responsible for the functional decline. Therefore, it would be of interest to include the examination of the hips into a falls risk assessment battery.
Secure Linear MDS Coded Matrix Inversion

A cumbersome operation in many scientific fields, is inverting large full-rank matrices. In this paper, we propose a coded computing approach for recovering matrix inverse approximations. We first present an approximate matrix inversion algorithm which does not require a matrix factorization, but uses a black-box least squares optimization solver as a subroutine, to give an estimate of the inverse of a real full-rank matrix. We then present a distributed framework for which our algorithm can be implemented, and show how we can leverage sparsest-balanced MDS generator matrices to devise matrix inversion coded computing schemes. We focus on balanced Reed-Solomon codes, which are optimal in terms of computational load; and communication from the workers to the master server. We also discuss how our algorithms can be used to distributively compute the pseudoinverse of a full-rank matrix, and how the communication is secured from eavesdroppers.
Appearance-guided Attentive Self-Paced Learning for Unsupervised Salient Object Detection

—Existing Deep-Learning-based (DL-based) Unsuper-vised Salient Object Detection (USOD) methods learn saliency information in images based on the prior knowledge of traditional saliency methods and pretrained deep networks. However, these methods employ a simple learning strategy to train deep networks and therefore cannot properly incorporate the “hidden” information of the training samples into the learning process. Moreover, appearance information, which is crucial for segmenting objects, is only used as post-process after the network training process. To address these two issues, we propose a novel appearance-guided attentive self-paced learning framework for unsupervised salient object detection. The proposed framework integrates both self-paced learning (SPL) and appearance guidance into a uniﬁed learning framework. Speciﬁcally, for the ﬁrst issue, we propose an Attentive Self-Paced Learning (ASPL) paradigm that organizes the training samples in a meaningful order to excavate gradually more detailed saliency information. Our ASPL facilitates our framework capable of automatically producing soft attention weights that measure the learning difﬁculty of training samples in a purely self-learning way. For the second issue, we propose an Appearance Guidance Module (AGM), which formulates the local appearance contrast of each pixel as the probability of saliency boundary and ﬁnds the potential boundary of the target objects by maximizing the probability. Furthermore, we further extend our framework to other multi-modality SOD tasks by aggregating the appearance vectors of other modality data, such as depth map, thermal image or optical ﬂow. Extensive experiments on RGB, RGB-D, RGB-T and video SOD benchmarks prove that our framework achieves state-of-the-art performance against existing USOD methods and is comparable to the latest supervised SOD methods. Code is available at www.github. com/moothes/A2S-v2.
Visual Context-driven Audio Feature Enhancement for Robust End-to-End Audio-Visual Speech Recognition

This paper focuses on designing a noise-robust end-to-end Audio-Visual Speech Recognition (AVSR) system. To this end, we propose Visual Context-driven Audio Feature Enhancement module (V-CAFE) to enhance the input noisy audio speech with a help of audio-visual correspondence. The proposed V-CAFE is designed to capture the transition of lip movements, namely visual context and to generate a noise reduction mask by considering the obtained visual context. Through context-dependent modeling, the ambiguity in viseme-to-phoneme mapping can be refined for mask generation. The noisy representations are masked out with the noise reduction mask resulting in enhanced audio features. The enhanced audio features are fused with the visual features and taken to an encoder-decoder model composed of Conformer and Transformer for speech recognition. We show the proposed end-to-end AVSR with the V-CAFE can further improve the noise-robustness of AVSR. The effectiveness of the proposed method is evaluated in noisy speech recognition and overlapped speech recognition experiments using the two largest audio-visual datasets, LRS2 and LRS3.
NBER WORKING PAPER SERIES COMMODITY-PRICE COMOVEMENT AND GLOBAL ECONOMIC ACTIVITY

Guided by a macroeconomic model in which non-energy commodity prices are endogenously determined, we apply a new factor-based identification strategy to decompose the historical sources of changes in commodity prices and global economic activity. The model yields a factor structure for commodity prices and identification conditions through which factors have economic interpretations: one factor captures the combined contribution of shocks that affect commodity markets only through general equilibrium forces. Applied to a cross-section of commodity prices since 1968, the theoretical restrictions are consistent with the data and yield structural interpretations of the common factors in commodity prices. Commodity-related shocks have contributed modestly to global economic fluctuations. Ron Alquist Bank of Canada 234 Wellington Street Ottawa, ON K1A 0G9 Canada ralquist@bank-banque-canada.ca Olivier Coibion 7600 Pitter Pat Austin, TX 78736 and NBER ocoibion@gmail.com