Miracle-dz/UniFAR-FTUs huggingface dataset:
Global Aggregation Then Local Distribution for Scene Parsing

Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation. However, convolutional neural networks (CNNs) are inherently limited to model such dependencies due to the naive structure in its building modules (e.g., local convolution kernel). While recent global aggregation methods are beneficial for long-range structure information modelling, they would oversmooth and bring noise to the regions contain fine details (e.g., boundaries and small objects), which are very much cared in the semantic segmentation task. To alleviate this problem, we propose to explore the local context for making the aggregated long-range relationship being distributed more accurately in local regions. In particular, we design a novel local distribution module which models the affinity map between global and local relationship for each pixel adaptively. Integrating existing global aggregation modules, we show that our approach can be modularized as an end-to-end trainable block and easily plugged into existing semantic segmentation networks, giving rise to the GALD networks. Despite its simplicity and versatility, our approach allows us to build new state of the art on major semantic segmentation benchmarks including Cityscapes, ADE20K, Pascal Context, Camvid and COCO-stuff. Code and trained models are released at https://github.com/lxtGH/GALD-DGCNet to foster further research.
Improving Video Instance Segmentation via Temporal Pyramid Routing

Video Instance Segmentation (VIS) is a new and inherently multi-task problem, which aims to detect, segment, and track each instance in a video sequence. Existing approaches are mainly based on single-frame features or single-scale features of multiple frames, where either temporal information or multi-scale information is ignored. To incorporate both temporal and scale information, we propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames. Specifically, TPR contains two novel components, including Dynamic Aligned Cell Routing (DACR) and Cross Pyramid Routing (CPR), where DACR is designed for aligning and gating pyramid features across temporal dimension, while CPR transfers temporally aggregated features across scale dimension. Moreover, our approach is a light-weight and plug-and-play module and can be easily applied to existing instance segmentation methods. Extensive experiments on three datasets including YouTube-VIS (2019, 2021) and Cityscapes-VPS demonstrate the effectiveness and efficiency of the proposed approach on several state-of-the-art instance and panoptic segmentation methods. Codes will be publicly available at https://github.com/lxtGH/TemporalPyramidRouting.
Learning Site-Specific Probing Beams for Fast mmWave Beam Alignment

Beam alignment – the process of finding an optimal directional beam pair – is a challenging procedure crucial to millimeter wave (mmWave) communication systems. We propose a novel beam alignment method that learns a site-specific probing codebook and uses the probing codebook measurements to predict the optimal narrow beam. An end-to-end neural network (NN) architecture is designed to jointly learn the probing codebook and the beam predictor. The learned codebook consists of site-specific probing beams that can capture particular characteristics of the propagation environment. The proposed method relies on beam sweeping of the learned probing codebook, does not require additional context information, and is compatible with the beam sweeping-based beam alignment framework in 5G. Using realistic ray-tracing datasets, we demonstrate that the proposed method can achieve high beam alignment accuracy and signal-to-noise ratio (SNR) while significantly – by roughly a factor of 3 in our setting – reducing the beam sweeping complexity and latency.
Evaluating Efficient Performance Estimators of Neural Architectures

Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS). To reduce the architecture training costs in NAS, one-shot estimators (OSEs) amortize the architecture training costs by sharing the parameters of one"supernet"between all architectures. Recently, zero-shot estimators (ZSEs) that involve no training are proposed to further reduce the architecture evaluation cost. Despite the high efficiency of these estimators, the quality of such estimations has not been thoroughly studied. In this paper, we conduct an extensive and organized assessment of OSEs and ZSEs on five NAS benchmarks: NAS-Bench-101/201/301, and NDS ResNet/ResNeXt-A. Specifically, we employ a set of NAS-oriented criteria to study the behavior of OSEs and ZSEs and reveal that they have certain biases and variances. After analyzing how and why the OSE estimations are unsatisfying, we explore how to mitigate the correlation gap of OSEs from several perspectives. Through our analysis, we give out suggestions for future application and development of efficient architecture performance estimators. Furthermore, the analysis framework proposed in our work could be utilized in future research to give a more comprehensive understanding of newly designed architecture performance estimators. All codes are available at https://github.com/walkerning/aw_nas.
Checking Patch Behaviour against Test Specification

How do we know a generated patch is correct? This is a key challenging question that generate-and-validate automated program repair (APR) systems struggle to address given the incompleteness of available test suites. Our intuition is that we can triage correct patches by checking whether each generated patch strictly implements code changes (i.e., behaviour) that are relevant to the bug it addresses. Such a bug is commonly specified by a failing test case. Towards predicting patch correctness in APR, we propose a simple, but novel hypothesis on how the link between the patch behaviour and failing test specifications can be drawn: similar failing test cases should require similar patches . We then propose BATS, an unsupervised learning-based system to predict patch correctness by checking patch B ehaviour A gainst failing T est S pecification. BATS exploits deep representation learning models for code and patches: for a given failing test case, the yielded embedding is used to compute similarity metrics in the search for historical similar test cases in order to identify the associated applied patches, which are then used as a proxy for assessing generated patch correctness. Experimentally, we first validate our hypothesis by assessing whether ground-truth developer patches cluster together in the same way that their associated failing test cases are clustered. Then, after collecting a large dataset of 1278 plausible patches (written by developers or generated by some 32 APR tools), we use BATS to predict correctness: BATS achieves an AUC between 0.557 to 0.718 and a recall between 0.562 and 0.854 in identifying correct patches. Compared against previous work, we demonstrate that our approach outperforms state-of-the-art performance in patch correctness prediction, without the need for large labeled patch datasets in contrast with prior machine learning-based approaches. While BATS is constrained by the availability of similar test cases, we show that it can still be complementary to existing approaches: used in conjunction with a recent approach implementing supervised learning, BATS improves the overall recall in detecting correct patches. We finally show that BATS can be complementary to the state-of-the-art PATCH-SIM dynamic approach of identifying the correct patches for APR tools.
Social groups in pedestrian crowds: review of their influence on the dynamics and their modelling

Pedestrians are often encountered walking in the company of some social relations, rather than alone. The social groups thus formed, in variable proportions depending on the context, are not randomly organised but exhibit distinct features, such as the well-known tendency of 3-member groups to be arranged in a V-shape. The existence of group structures might thus impact the collective dynamics of the crowd, possibly in a critical way when emergency situations are considered. After turning a blind eye to these group aspects for years, endeavours to model groups in crowd simulation software have thrived in the past decades. This fairly short review opens on a description of their empirical characteristics and their (more controversial) impact on the global flow. Then, it aims to offer a pedagogical discussion of the main strategies to model such groups, within different types of models, in order to provide guidance for prospective modellers.
Spatially-optimized finite-difference schemes for numerical dispersion suppression in seismic applications

Propagation characteristics of a wave are defined by the dispersion relationship, from which the governing partial differential equation (PDE) can be recovered. PDEs are commonly solved numerically using the finite-difference (FD) method, with stencils constructed from truncated Taylor series expansions which, whilst typically providing good approximation of the PDE in the space-time domain, often differ considerably from the original partial differential in the wavenumber-frequency domain where the dispersion relationship is defined. Consequentially, stable, high-order FD schemes may not necessarily result in realistic wave behavior, commonly exhibiting numerical dispersion: lagging high-frequency components as a product of discretization. A method for optimizing FD stencil weightings via constrained minimization to better approximate the partial derivative in the wavenumber domain is proposed, allowing for accurate propagation with coarser grids than would be otherwise possible. This was applied to second derivatives on a standard grid and first derivatives on a staggered grid. To evaluate the efficacy of the method, a pair of numerical simulations were devised to compare spatially-optimized stencils with conventional formulations of equivalent extent. A spatially-optimized formulation of the 1D acoustic wave equation with Dirichlet boundary conditions is presented, evaluating performance at a range of grid spacings, examining the interval between the theoretical maximum grid spacings for the conventional and optimized schemes in finer detail. The optimized scheme was found to offer superior performance for undersampled wavefields and heavily oversampled wavefields. Staggered-grid first derivative stencils were then applied to the P-SV elastic wave formulation, simulating seismic wave propagation for a two-layer, water-over-rock model.
Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers

A common lens to theoretically study neural net architectures is to analyze the functions they can approximate. However, constructions from approximation theory may be unrealistic and therefore less meaningful. For example, a common unrealistic trick is to encode target function values using infinite precision. To address these issues, this work proposes a formal definition of statistically meaningful (SM) approximation which requires the approximating network to exhibit good statistical learnability. We study SM approximation for two function classes: boolean circuits and Turing machines. We show that overparameterized feedforward neural nets can SM approximate boolean circuits with sample complexity depending only polynomially on the circuit size, not the size of the network. In addition, we show that transformers can SM approximate Turing machines with computation time bounded by $T$ with sample complexity polynomial in the alphabet size, state space size, and $\log (T)$. We also introduce new tools for analyzing generalization which provide much tighter sample complexities than the typical VC-dimension or norm-based bounds, which may be of independent interest.
Experimental inference of neutral and impurity transport in Alcator C-Mod using high-resolution x-ray and ultra-violet spectra

We present experimental inferences of cross-field impurity transport coefficients for AlcatorC-Mod plasmas without edge-localized modes, leveraging a novel forward model for the entire Ca K α spectrum, including satellite lines within the spectral range, to compare to high-resolution x-ray imaging crystal spectroscopy (XICS). These measurements are complemented by extreme ultra-violet (EUV) spectroscopy that constrains transport closer to the edge. Using new atomic data sets for both XICS and EUV analysis has enabled consideration of line ratios across both spectral ranges and has increased the accuracy of inferred transport coefficients. Inclusion of charge exchange between edge thermal neutrals and impurities is shown to be extremely important in C-Mod pedestals. Atomic D neutral densities from experimental D Ly α measurements at the midplane are compared to SOLPS-ITER simulations, finding good agreement. Bayesian inferences of impurity transport coefficients are presented for L-, EDA H-, and I-mode discharges, making use of the Aurora package for forward modeling and combining our spectroscopic constraints. Experimentally inferred diffusion profiles are found to match turbulent transport models at midradius within uncertainties, using both quasilinear gyro-fluid TGLF SAT-1 and nonlinear ion-scale gyrokinetic CGYRO simulations. Significant discrepancies in convection are observed in some cases, suggesting difficulties in predictions of flat or hollow impurity profiles.
On the properties of the asymptotic incompatibility measure in multiparameter quantum estimation

We address the use of asymptotic incompatibility (AI) to assess the quantumness of a multiparameter quantum statistical model. AI is a recently introduced measure which quantifies the difference between the Holevo and the symmetric logarithmic derivative (SLD) scalar bounds, and can be evaluated using only the SLD operators of the model. At first, we evaluate analytically the AI of the most general quantum statistical models involving two-level (qubit) and single-mode Gaussian continuous-variable quantum systems, and prove that AI is a simple monotonous function of the state purity. Then, we numerically investigate the same problem for qudits (d-dimensional quantum systems, with 2 < d ⩽ 4), showing that, while in general AI is not in general a function of purity, we have enough numerical evidence to conclude that the maximum amount of AI is attainable only for quantum statistical models characterized by a purity larger than μmin=1/(d−1) . In addition, by parametrizing qudit states as thermal (Gibbs) states, numerical results suggest that, once the spectrum of the Hamiltonian is fixed, the AI measure is in one-to-one correspondence with the fictitious temperature parameter β characterizing the family of density operators. Finally, by studying in detail the definition and properties of the AI measure we find that: (i) given a quantum statistical model, one can readily identify the maximum number of asymptotically compatible parameters; (ii) the AI of a quantum statistical model bounds from above the AI of any sub-model that can be defined by fixing one or more of the original unknown parameters (or functions thereof), leading to possibly useful bounds on the AI of models involving noisy quantum dynamics.
Q-Learning for Conflict Resolution in B5G Network Automation

—Network automation is gaining signiﬁcant attention in the development of B5G networks, primarily for reducing operational complexity, expenditures and improving network efﬁciency. Concurrently operating closed loops aiming for individual optimization targets may cause conﬂicts which, left unresolved, would lead to signiﬁcant degradation in network Key Performance Indicators (KPIs), thereby resulting in sub-optimal network performance. Centralized coordination, albeit optimal, is impractical in large scale networks and for time-critical applications. Decentralized approaches are therefore envisaged in the evolution to B5G and subsequently, 6G networks. This work explores pervasive intelligence for conﬂict resolution in network automation, as an alternative to centralized orchestration. A Q-Learning decentralized approach to network automation is proposed, and an application to network slice auto-scaling is designed and evaluated. Preliminary results highlight the potential of the proposed scheme and justify further research work in this direction
When and how CNNs generalize to out-of-distribution category-viewpoint combinations

Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent works suggest that convolutional neural networks (CNNs) fail to generalize to out-of-distribution (OOD) category-viewpoint combinations, ie. combinations not seen during training. In this paper, we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and 3D viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization. We show that increasing the number of in-distribution combinations (ie. data diversity) substantially improves generalization to OOD combinations, even with the same amount of training data. We compare learning category and viewpoint in separate and shared network architectures, and observe starkly different trends on in-distribution and OOD combinations, ie. while shared networks are helpful in-distribution, separate networks significantly outperform shared ones at OOD combinations. Finally, we demonstrate that such OOD generalization is facilitated by the neural mechanism of specialization, ie. the emergence of two types of neurons -- neurons selective to category and invariant to viewpoint, and vice versa.
Inflationary Implications of the Covariant Entropy Bound and the Swampland de Sitter Conjectures

We present a proposal to relate the de Sitter conjecture (dSC) with the time dependence of fluxes via the covariant entropy bound (CEB). By assuming an early phase of accelerated expansion where the CEB is satisfied, we take into account a contribution from time-dependent flux compactification to the four-dimensional entropy which establishes a bound on the usual slow-roll parameters ηH and ϵH. We also show an explicit calculation of entropy from a toroidal flux compactification, from a transition amplitude of time-dependent fluxes which allows us to determine the conditions on which the bounds on the slow-roll parameters are in agreement to the dSC.
Effects of Chemical Structure on the Stability of Smectites in Short-Term Alteration Experiments

Because of their isolating capacity, smectite-rich clays have been proposed as buffer and backfill materials in high-level radioactive waste repositories. These repositories have to guarantee long-term safety for ~1 million years. Thermodynamics and kinetics of possible alteration processes of bentonite determine its long-term performance as a barrier material. Smectites in 25 different clays and bentonites were investigated in order to identify possible differences in their rates of alteration. These samples were saturated for 30 days in 1 M NaCl solution and deionized water, and then overhead rotated at speeds of 20 rpm and 60 rpm. Depending on the octahedral and interlayer composition, each of the smectites studied had specific rate of alteration, a so-called specific dissolution potential of smectite. The bentonites were classed as ‘slow-reacting bentonite’, ‘moderate-reacting bentonite’, or ‘fast-reacting bentonite’ corresponding to a relatively low (ΔP specific dissolution potential — <-5%), moderate (-5% < ΔP < -20%), or high specific dissolution potential (ΔP > -20%), respectively. The larger the amount of octahedral Fe and Mg compared to octahedral Al, the greater the specific dissolution potential. The present study found that the interlayer composition has a discernible impact on the rate of alteration. In experiments with rotation speeds of 60 rpm and a 1 M NaCl solution, Na+ was found to be the stabilizing cation in the interlayers of all the smectites. The Na-stabilizing mechanism was identified in only some of the smectites (type A) in experiments with 20 rpm (1 M NaCl solution). A second stabilization mechanism (by interlayer cations; Ca and Mg) was identified for other smectites (type B). Each bentonite has a specific rate of alteration. ‘Slow-reacting bentonite’ and clay with smectite-illite interstratifications are recommended as potential clay barriers in HLW repositories. The experimental and analytical procedures described here could be applied to potential barrier materials to identify ‘slow-reacting bentonite’.
Post-Operative Sleep Endoscopy with Target-Controlled Infusion After Palatopharyngoplasty for Obstructive Sleep Apnea: Anatomical and Polysomnographic Outcomes

Introduction The findings of drug-induced sleep endoscopy (DISE) are not always correlated with the outcome of upper airway surgery for obstructive sleep apnea (OSA), and whether multilevel surgery is truly required in treating multilevel obstruction identified in preoperative DISE remains an issue. We attempted to compare DISE findings before and after palatopharyngoplasty in patients with OSA because changes in DISE may be beneficial to better understand polysomnographic and anatomical outcomes. Methods This was a prospective cohort study for 34 patients with moderate to severe OSA who underwent palatopharyngoplasty at a tertiary care center from 2016 to 2018. We recorded the patients’ demographic characteristics, procedures, and surgical outcomes and compared the preoperative and postoperative DISE staging patterns. Results The apnea-hypopnea index (AHI) values of 34 adults improved significantly after surgery (40.6 ± 23.3 versus 25.6 ± 20.6, P < 0.001). The majority of patients, 26/34, had preoperative complete concentric collapse at the velum, and for most (20/26, 77%) there was a change of the collapse pattern into anteroposterior collapse postoperatively. Patients with postoperative velar collapse had higher follow-up AHI values than those who without (27.8 ± 21.9 versus 15.2 ± 7.7, P = 0.023). Patients with preoperative complete tongue base collapse had higher follow-up AHI values than did those with no or partial collapse (40.6 ± 21.0 versus 21.0 ± 18.6, P = 0.017). Patients with postoperative complete tongue base collapse also had higher follow-up AHI values than the others (42.7 ± 22.1 versus 18.5 ± 15.4, P = 0.001). Conclusion Palatopharyngoplasty could change the collapse pattern at the velum in most patients. Preoperative and postoperative complete tongue base collapse and postoperative velar collapse identified in TCI-DISE were associated with relatively poor outcomes.