Miracle-dz/UniFAR-FTUs huggingface dataset:
Fabrication of Double Shell Targets with a Glass Inner Capsule Supported by SiO2 Aerogel for Shots on the Omega Laser in 2006

Abstract Indirectly driven double shell implosions are being investigated as a possible noncryogenic path to ignition on the National Ignition Facility. Lawrence Livermore National Laboratory has made several technological advances that have produced double shell targets that represent a significant improvement to previously fielded targets. The inner capsule is supported inside the ablator shell by SiO2 aerogel with a nominal density of 50 mg/cm3. The aerogel is cast around the inner capsule and then machined concentric to it. The seamless sphere of aerogel containing the embedded capsule is then assembled between the two halves of the ablator shell. The concentricity between the two shells has been improved to less than 1.5 μm. The ablator shell consists of two hemispherical shells that mate at a step joint that incorporates a gap with a nominal thickness of 0.1 μm. Using a new flexure-based tool holder that precisely positions the diamond cutting tool on the diamond turning machine, step discontinuities on the inner surface of the ablator of less than 0.5 μm have been achieved. New methods have been used to comprehensively characterize each of the targets using high-resolution x-ray imaging systems.
SparDL: Distributed Deep Learning Training with Efficient Sparse Communication

Top-k sparsification has recently been widely used to reduce the communication volume in distributed deep learning. However, due to the Sparse Gradient Accumulation (SGA) dilemma, the performance of top-k sparsification still has limitations. Recently, a few methods have been put forward to handle the SGA dilemma. Regrettably, even the state-of-the-art method suffers from several drawbacks, e.g., it relies on an inefficient communication algorithm and requires extra transmission steps. Motivated by the limitations of existing methods, we propose a novel efficient sparse communication framework, called SparDL. Specifically, SparDL uses the Spar-Reduce-Scatter algorithm, which is based on an efficient Reduce-Scatter model, to handle the SGA dilemma without additional communication operations. Besides, to further reduce the latency cost and improve the efficiency of SparDL, we propose the Spar-All-Gather algorithm. Moreover, we propose the global residual collection algorithm to ensure fast convergence of model training. Finally, extensive experiments are conducted to validate the superiority of SparDL.
A Spectral Analysis of Predictability and Error in an Operational Forecast System

Abstract A spectral analysis of error at 24, 48 and 72 hours in an operational forecast system is carded out for a winter and a summer month. The scale-dependent behavior of the error and the several factors governing error growth, namely the nonlinear production term, the nonlinear transfer of energy between scales and the model-related error source term are evaluated. It is found that the largest errors occur at low wavenumbers where the amplitude of the flow is largest, while the largest relative errors occur at high wavenumbers when error is approaching saturation. Early in the forecast the growth of error is governed by the model error source term which supplies error at all scales. At later forecast times the nonlinear production and interaction terms become important. Error doubling times and the flux of error through wavenumber space are also calculated. The predictability of the flow resides in the low wavenumber region. The behavior of the error and its transient and stationary parts markedly wi.
Reliability of Meta-Analysis Research Claims for Gas Stove Cooking−Childhood Respiratory Health Associations

Odds ratios or p-values from individual observational studies can be combined to examine a common cause−effect research question in meta-analysis. However, reliability of individual studies used in meta-analysis should not be taken for granted as claimed cause−effect associations may not reproduce. An evaluation was undertaken on meta-analysis of base papers examining gas stove cooking (including nitrogen dioxide, NO2) and childhood asthma and wheeze associations. Numbers of hypotheses tested in 14 of 27 base papers (52%) used in meta-analysis of asthma and wheeze were counted. Test statistics used in the meta-analysis (40 odds ratios with 95% confidence limits) were converted to p-values and presented in p-value plots. The median (interquartile range) of possible numbers of hypotheses tested in the 14 base papers was 15,360 (6,336−49,152). None of the 14 base papers made mention of correcting for multiple testing, nor was any explanation offered if no multiple testing procedure was used. Given large numbers of hypotheses available, statistics drawn from base papers and used for meta-analysis are likely biased. Even so, p-value plots for gas stove−current asthma and gas stove−current wheeze associations show randomness consistent with unproven gas stove harms. The meta-analysis fails to provide reliable evidence for public health policy making on gas stove harms to children in North America. NO2 is not established as a biologically plausible explanation of a causal link with childhood asthma. Biases – multiple testing and p-hacking – cannot be ruled out as explanation for a gas stove−current asthma association claim. Selective reporting is another bias in published literature of gas stove–childhood respiratory health studies.
Wind Tunnel Measurements of the Response of Hot-Wire Liquid Water Content Instruments to Large Droplets

Abstract Wet wind tunnel tests were performed on more than 23 cloud liquid water content (LWC) probes and drop spectrometers at the NASA Icing Research Tunnel, with a main objective to characterize their response to large-droplet conditions. As a part of this study, the LWC and median volume diameter (MVD) reference values of the tunnel were examined, and accuracies were estimated and reported herein. Alternative MVDs were calculated from measurements conducted during the study. MVD accuracy was particularly difficult to estimate due to the lack of accepted standards, and MVD estimates were quite sensitive to the complement of instruments used to generate composite droplet spectra. Four hot-wire LWC probes were tested to characterize the LWC response as a function of cloud MVD. A response reduction was observed with increasing MVD for all three probes with cylindrical hot wires, most significant for the probe with the smallest wire diameter. The response of the Nevzorov total water content (TWC) probe, wi.
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies

Transformations based on domain expertise (expert transformations), such as random-resized-crop and color-jitter, have proven critical to the success of contrastive learning techniques such as SimCLR. Recently, several attempts have been made to replace such domain-specific, human-designed transformations with generated views that are learned. However for imagery data, so far none of these view-generation methods has been able to outperform expert transformations. In this work, we tackle a different question: instead of replacing expert transformations with generated views, can we constructively assimilate generated views with expert transformations? We answer this question in the affirmative and propose a view generation method and a simple, effective assimilation method that together improve the state-of-the-art by up to ~3.6% on three different datasets. Importantly, we conduct a detailed empirical study that systematically analyzes a range of view generation and assimilation methods and provides a holistic picture of the efficacy of learned views in contrastive representation learning.
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning

In goal-reaching reinforcement learning (RL), the optimal value function has a particular geometry, called quasimetric structure. This paper introduces Quasimetric Reinforcement Learning (QRL), a new RL method that utilizes quasimetric models to learn optimal value functions. Distinct from prior approaches, the QRL objective is specifically designed for quasimetrics, and provides strong theoretical recovery guarantees. Empirically, we conduct thorough analyses on a discretized MountainCar environment, identifying properties of QRL and its advantages over alternatives. On offline and online goal-reaching benchmarks, QRL also demonstrates improved sample efficiency and performance, across both state-based and image-based observations.
Positron Emission Tomography-Computed Tomography and Magnetic Resonance Imaging Assessments in a Mouse Model of Implant-Related Bone and Joint Staphylococcus aureus Infection

Our results encourage future investigations on the utility of the model for testing different therapeutic procedures for osteomyelitis. ABSTRACT Osteomyelitis is an infection of the bone, associated with an inflammatory process. Imaging plays an important role in establishing the diagnosis and the most appropriate patient management. However, data are lacking regarding the use of preclinical molecular imaging techniques to assess osteomyelitis progression in experimental models. This study aimed to compare structural and molecular imaging to assess disease progression in a mouse model of implant-related bone and joint infections caused by Staphylococcus aureus. In SWISS mice, the right femur was implanted with a resorbable filament impregnated with S. aureus (infected group, n = 10) or sterile culture medium (uninfected group, n = 6). Eight animals (5 infected, 3 uninfected) were analyzed with magnetic resonance imaging (MRI) at 1, 2, and 3 weeks postintervention, and 8 mice were analyzed with [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET)-computed tomography (CT) at 48 h and at 1, 2, and 3 weeks postintervention. In infected animals, CT showed bone lesion progression, mainly in the distal epiphysis, although some uninfected animals presented evident bone sequestra at 3 weeks. MRI showed a lesion in the articular area that persisted for 3 weeks in infected animals. This lesion was smaller and less evident in the uninfected group. At 48 h postintervention, FDG-PET showed higher joint uptake in the infected group than in the uninfected group (P = 0.025). Over time, the difference between groups increased. These results indicate that FDG-PET imaging was much more sensitive than MRI and CT for differentiating between infection and inflammation at early stages. FDG-PET clearly distinguished between infection and postsurgical bone healing (in uninfected animals) from 48 h to 3 weeks after implantation. IMPORTANCE Our results encourage future investigations on the utility of the model for testing different therapeutic procedures for osteomyelitis.
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers

An extension of Transformers is proposed that enables explicit relational reasoning through a novel module called the Abstractor. At the core of the Abstractor is a variant of attention called relational cross-attention. The approach is motivated by an architectural inductive bias for relational learning that disentangles relational information from extraneous features about individual objects. This enables explicit relational reasoning, supporting abstraction and generalization from limited data. The Abstractor is first evaluated on simple discriminative relational tasks and compared to existing relational architectures. Next, the Abstractor is evaluated on purely relational sequence-to-sequence tasks, where dramatic improvements are seen in sample efficiency compared to standard Transformers. Finally, Abstractors are evaluated on a collection of tasks based on mathematical problem solving, where modest but consistent improvements in performance and sample efficiency are observed.
Hochschild cohomology of a class of quantized Koszul algebras

In this paper,we mainly compute the k-dimensions of Hochschild cohomology spaces of a class of quantized Koszul algebras Λq(q ∈ k \ {0}) with the application of combinatorics,explicitly describe the cup product of Hochschild cohomology of Λq,and thus determine the structure of the Hochschild cohomology ring HH*(Λq) of Λq modulo the ideal N generated by nilpotence.As a consequence,we show that HH*(Λq)/N is not finitely generated as an algebra when q is a root of unity,and thus provide more counterexamples for SnashallSolberg conjecture(namely,the Hochschild cohomology ring modulo nilpotence of a finite-dimensional k-algebra is always finitely generated as algebra).
The Greggs-Pret Index: a Machine Learning analysis of consumer habits as a metric for the socio-economic North-South divide in England

In England, it is anecdotally remarked that the number of Greggs bakeries to be found in a town is a reliable measure of the area's 'Northern-ness'. Conversely, a commercial competitor to Greggs in the baked goods and sandwiches market, Pret-a-Manger, is reputed to be popular in more 'southern' areas of England. Using a Support Vector Machine and an Artificial Neural Network (ANN) Regression Model, the relative geographical distributions of Greggs and Pret have been utilised for the first time to quantify the North-South divide in England. The calculated dividing lines were each compared to another line, based on Gross Domestic Household Income (GDHI). The lines match remarkably well, and we conclude that this is likely because much of England's wealth is concentrated in London, as are most of England's Pret-a-Manger shops. Further studies were conducted based on the relative geographical distributions of popular supermarkets Morrisons and Waitrose, which are also considered to have a North-South association. This analysis yields different results. For all metrics, the North-South dividing line passes close to the M1 Watford Gap services. As a common British idiom, this location is oft quoted as one point along the English North-South divide, and it is notable that this work agrees. This tongue-in-cheek analysis aims to highlight more serious factors highlighting the North-South divide, such as life expectancy, education, and poverty.
Towards Goal-Oriented Semantic Communications: New Metrics, Open Challenges, and Future Research Directions

Since Shannon's pioneering masterpiece which established the prototype of modern information theory, the goal of communications has long been promising accurate message reconstruction at a refined bit-by-bit level, which deliberately neglects the semantics/effectiveness aspect of information. Nevertheless, the recent development of wireless technologies and the spurt of deep learning (DL) techniques allow us to reclaim the meaning/usefulness aspect in the design of future 6G communication paradigms, where goal-oriented communication is becoming a trend. Age of Information (AoI), a well-known metric that captures the importance of information by recording the time elapsed from the generation time slot, has been extended to various variants, such as Value of Information (VoI), Urgency of Information (UoI), Age of Incorrect Information (AoII), and etc. While each of them proposes novel ways to measure the meaning/usefulness aspect of information, there is not yet an integrated framework encompassing all of them. To this end, we propose a novel tensor-based approach, the Goal-oriented Tensor (GoT), to unify them, which also allows more flexible and fine-grained goal characterizations. Following the proposed GoT, we architect a holistic goal-oriented framework to enable goal-oriented semantic communications, in which information perception, dissemination, and control-plane decisions are implemented in accordance with specific goals. Finally, we outline several open challenges to fulfill the vision of the GoT framework.
Involutions, links, and Floer cohomologies

We develop a version of Seiberg--Witten Floer cohomology/homotopy type for a spin$^c$ 4-manifold with boundary and with an involution which reverses the spin$^c$ structure, as well as a version of Floer cohomology/homotopy type for oriented links with non-zero determinant. This framework generalizes the previous work of the authors regarding Floer homotopy type for spin 3-manifolds with involutions and for knots. Based on this Floer cohomological setting, we prove Fr{\o}yshov-type inequalities which relate topological quantities of 4-manifolds with certain equivariant homology cobordism invariants. The inequalities and homology cobordism invariants have applications to the topology of unoriented surfaces, Nielsen realization problem for non-spin 4-manifolds, and non-smoothable unoriented surfaces in 4-manifolds.
FinderNet: A Data Augmentation Free Canonicalization aided Loop Detection and Closure technique for Point clouds in 6-DOF separation

We focus on the problem of LiDAR point cloud based loop detection (or Finding) and closure (LDC) in a multi-agent setting. State-of-the-art (SOTA) techniques directly generate learned embeddings of a given point cloud, require large data transfers, and are not robust to wide variations in 6 Degrees-of-Freedom (DOF) viewpoint. Moreover, absence of strong priors in an unstructured point cloud leads to highly inaccurate LDC. In this original approach, we propose independent roll and pitch canonicalization of the point clouds using a common dominant ground plane. Discretization of the canonicalized point cloud along the axis perpendicular to the ground plane leads to an image similar to Digital Elevation Maps (DEMs), which exposes strong spatial priors in the scene. Our experiments show that LDC based on learnt embeddings of such DEMs is not only data efficient but also significantly more robust, and generalizable than the current SOTA. We report significant performance gain in terms of Average Precision for loop detection and absolute translation/rotation error for relative pose estimation (or loop closure) on Kitti, GPR and Oxford Robot Car over multiple SOTA LDC methods. Our encoder technique allows to compress the original point cloud by over 830 times. To further test the robustness of our technique we create and opensource a custom dataset called Lidar-UrbanFly Dataset (LUF) which consists of point clouds obtained from a LiDAR mounted on a quadrotor.
The Effect of Superpositions on the Planetary Nebula Luminosity Function

Planetary nebula (PN) surveys in systems beyond ∼10 Mpc often find high-excitation, point-like sources with [O iii] λ5007 fluxes greater than the apparent bright-end cutoff of the planetary nebula luminosity function (PNLF). Here we identify PN superpositions as one likely cause for the phenomenon and describe the proper procedures for deriving PNLF distances when object blends are a possibility. We apply our technique to two objects: a model Virgo-distance elliptical galaxy observed through a narrowband interference filter, and the Fornax lenticular galaxy NGC 1380 surveyed with the MUSE integral-field unit spectrograph. Our analyses show that even when the most likely distance to a galaxy is unaffected by the possible presence of PN superpositions, the resultant value will still be biased toward too small a distance due to the asymmetrical nature of the error bars. We discuss the future of the PNLF in an era where current ground-based instrumentation can push the technique to distances beyond ∼35 Mpc.
VTAE: Variational Transformer Autoencoder With Manifolds Learning

Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables and these models use a non-linear function (generator) to map latent samples into the data space. On the other hand, the non-linearity of the generator implies that the latent space shows an unsatisfactory projection of the data space, which results in poor representation learning. This weak projection, however, can be addressed by a Riemannian metric, and we show that geodesics computation and accurate interpolations between data samples on the Riemannian manifold can substantially improve the performance of deep generative models. In this paper, a Variational spatial-Transformer AutoEncoder (VTAE) is proposed to minimize geodesics on a Riemannian manifold and improve representation learning. In particular, we carefully design the variational autoencoder with an encoded spatial-Transformer to explicitly expand the latent variable model to data on a Riemannian manifold, and obtain global context modelling. Moreover, to have smooth and plausible interpolations while traversing between two different objects’ latent representations, we propose a geodesic interpolation network different from the existing models that use linear interpolation with inferior performance. Experiments on benchmarks show that our proposed model can improve predictive accuracy and versatility over a range of computer vision tasks, including image interpolations, and reconstructions.
On the Price of Transparency: A Comparison Between Overt Persuasion and Covert Signaling

Transparency of information disclosure has always been considered an instrumental component of effective governance, accountability, and ethical behavior in any organization or system. However, a natural question follows: what is the cost or benefit of being transparent, as one may suspect that transparency imposes additional constraints on the information structure, decreasing the maneuverability of the information provider. This work proposes and quantitatively investigates the price of transparency (PoT) in strategic information disclosure by comparing the perfect Bayesian equilibrium payoffs under two representative information structures: overt persuasion and covert signaling models. PoT is defined as the ratio between the payoff outcomes in covert and overt interactions. As the main contribution, this work develops a two-stage-bilinear (TSB) programming approach to solve for non-degenerate perfect Bayesian equilibria of dynamic incomplete information games with finite states and actions. Using TSB, we show that it is always in the information provider's interest to choose the transparent information structure, as $0\ \leq$ PoT $\leq\ 1$. The upper bound is attainable for any strictly Bayesian-posterior competitive games, of which zero-sum games are a particular case. For continuous games, the PoT, still upper-bounded by 1, can be arbitrarily close to 0, indicating the tightness of the lower bound. This tight lower bound suggests that the lack of transparency can result in significant loss for the provider.
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting

Continual learning research has shown that neural networks suffer from catastrophic forgetting"at the output level", but it is debated whether this is also the case at the level of learned representations. Multiple recent studies ascribe representations a certain level of innate robustness against forgetting - that they only forget minimally and no critical information. We revisit and expand upon the experiments that revealed this difference in forgetting and illustrate the coexistence of two phenomena that affect the quality of continually learned representations: knowledge accumulation and feature forgetting. Carefully taking both aspects into account, we show that, even though it is true that feature forgetting can be small in absolute terms, newly learned information tends to be forgotten just as catastrophically at the level of the representation as it is at the output level. Next we show that this feature forgetting is problematic as it substantially slows down knowledge accumulation. Finally, we study how feature forgetting and knowledge accumulation are affected by different types of continual learning methods.