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As robots increasingly coexist with humans, they must navigate complex, dynamic environments rich in visual information and implicit social dynamics, like when to yield or move through crowds. Addressing these challenges requires significant advances in vision-based sensing and a deeper understanding of socio-dynamic factors, particularly in tasks like navigation. To facilitate this, robotics researchers need advanced simulation platforms offering dynamic, photorealistic environments with realistic actors. Unfortunately, most existing simulators fall short, prioritizing geometric accuracy over visual fidelity, and employing unrealistic agents with fixed trajectories and low-quality visuals. To overcome these limitations, we developed a simulator that incorporates three essential elements: (1) photorealistic neural rendering of environments, (2) neurally animated human entities with behavior management, and (3) an ego-centric robotic agent providing multi-sensor output. By utilizing advanced neural rendering techniques in a dual-NeRF simulator, our system produces high-fidelity, photorealistic renderings of both environments and human entities. Additionally, it integrates a state-of-the-art Social Force Model to model dynamic human-human and human-robot interactions, creating the first photorealistic and accessible human-robot simulation system powered by neural rendering., Comment: 8 pages, 5 figures

Similar to Distributed Acoustic Sensing (DAS), phase transmission fibre optics allows for large bandwidth seismic data measurements using fibre-optic cables. However, while the application range of DAS is limited to tens of kilometres, phase transmission fibre optics has an application range that can go up to thousands of kilometres. This new method has been shown as an effective method to record earthquakes, but its ability to record ambient seismic noise that can be used for seismic imaging and tomography is still up for question, and will be analysed in this work. We provide the theoretical foundation for the interpretation of seismic noise autocorrelations and interferometry from phase transmission fibre optics. Further, we test the model on actual phase transmission data sourced from a phase-stabilised optical frequency network in Switzerland. There, the phase stabilisation scheme measures and compensates noise on the optical phase caused by distortions of the fibre. We analyse the autocorrelation of the measured phase noise correction and explore potential interpretations by comparing it with the autocorrelation of a synthetically computed phase noise correction. This comparison is challenging due to two factors: the intricate cable geometry increases the computational cost of generating synthetic data, and the precise location and geometry of the cable are uncertain. Despite these difficulties, we believe that when applied to a different dataset, this approach could enable seismic tomography with ambient noise interferometry using a long-range fibre-optic sensing device.

Observational genome-wide association studies are now widely used for causal inference in genetic epidemiology. To maintain privacy, such data is often only publicly available as summary statistics, and often studies for the endogenous covariates and the outcome are available separately. This has necessitated methods tailored to two-sample summary statistics. Current state-of-the-art methods modify linear instrumental variable (IV) regression -- with genetic variants as instruments -- to account for unmeasured confounding. However, since the endogenous covariates can be high dimensional, standard IV assumptions are generally insufficient to identify all causal effects simultaneously. We ensure identifiability by assuming the causal effects are sparse and propose a sparse causal effect two-sample IV estimator, spaceTSIV, adapting the spaceIV estimator by Pfister and Peters (2022) for two-sample summary statistics. We provide two methods, based on L0- and L1-penalization, respectively. We prove identifiability of the sparse causal effects in the two-sample setting and consistency of spaceTSIV. The performance of spaceTSIV is compared with existing two-sample IV methods in simulations. Finally, we showcase our methods using real proteomic and gene-expression data for drug-target discovery.

The NIFFTE fission Time Projection Chamber (fissionTPC) has been used to measure the $^{239}$Pu(n,f)/$^{235}$U(n,f) cross-section ratio for neutron-induced fission in the range of 0.1 - 100 MeV, with high precision. A white neutron source was provided by the Los Alamos Neutron Science Center, where the experiment was conducted as a remeasurement to evaluate a roughly 2% discrepancy of the previous fissionTPC results with ENDF/B-VIII.0. A detailed accounting of measurement uncertainties was performed, based on the fissionTPC's novel ability to provide three-dimensional reconstruction of fission-fragment ionization profiles. Current results obtained using a vapor-deposited, highly uniform $^{239}$Pu target, in comparison to the measurement published in 2021, where a $^{239}$Pu electroplated target was used, are presented and discussed. The remeasurement presented here is in agreement with the previous fissionTPC result within measurement uncertainties.

Sign Language Assessment (SLA) tools are useful to aid in language learning and are underdeveloped. Previous work has focused on isolated signs or comparison against a single reference video to assess Sign Languages (SL). This paper introduces a novel SLA tool designed to evaluate the comprehensibility of SL by modelling the natural distribution of human motion. We train our pipeline on data from native signers and evaluate it using SL learners. We compare our results to ratings from a human raters study and find strong correlation between human ratings and our tool. We visually demonstrate our tools ability to detect anomalous results spatio-temporally, providing actionable feedback to aid in SL learning and assessment., Comment: Accepted to Twelfth International Workshop on Assistive Computer Vision and Robotics at ECCV 2024

The STEP 1 randomized trial evaluated the effect of taking semaglutide vs placebo on body weight over a 68 week duration. As with any study evaluating an intervention delivered over a sustained period, non-adherence was observed. This was addressed in the original trial analysis within the Estimand Framework by viewing non-adherence as an intercurrent event. The primary analysis applied a treatment policy strategy which viewed it as an aspect of the treatment regimen, and thus made no adjustment for its presence. A supplementary analysis used a hypothetical strategy, targeting an estimand that would have been realised had all participants adhered, under the assumption that no post-baseline variables confounded adherence and change in body weight. In this paper we propose an alternative Instrumental Variable method to adjust for non-adherence which does not rely on the same `unconfoundedness' assumption and is less vulnerable to positivity violations (e.g., it can give valid results even under conditions where non-adherence is guaranteed). Unlike many previous Instrumental Variable approaches, it makes full use of the repeatedly measured outcome data, and allows for a time-varying effect of treatment adherence on a participant's weight. We show that it provides a natural vehicle for defining two distinct hypothetical estimands: the treatment effect if all participants would have adhered to semaglutide, and the treatment effect if all participants would have adhered to both semaglutide and placebo. When applied to the STEP 1 study, they both suggest a sustained, slowly decaying weight loss effect of semaglutide treatment.

The PROSPECT experiment is designed to perform precise searches for antineutrino disappearance at short distances (7 - 9~m) from compact nuclear reactor cores. This Letter reports results from a new neutrino oscillation analysis performed using the complete data sample from the PROSPECT-I detector operated at the High Flux Isotope Reactor in 2018. The analysis uses a multi-period selection of inverse beta decay neutrino interactions with reduced backgrounds and enhanced statistical power to set limits on electron-flavor disappearance caused by mixing with sterile neutrinos with 0.2 - 20 eV$^2$ mass splittings. Inverse beta decay positron energy spectra from six different reactor-detector distance ranges are found to be statistically consistent with one another, as would be expected in the absence of sterile neutrino oscillations. The data excludes at 95% confidence level the existence of sterile neutrinos in regions above 3~eV$^2$ previously unexplored by terrestrial experiments, including all space below 10~eV$^2$ suggested by the recently strengthened Gallium Anomaly. The best-fit point of the Neutrino-4 reactor experiment's claimed observation of short-baseline oscillation is ruled out at more than five standard deviations., Comment: 6 pages, 4 figures

The PROSPECT-I detector has several features that enable measurement of the direction of a compact neutrino source. In this paper, a detailed report on the directional measurements made on electron antineutrinos emitted from the High Flux Isotope Reactor is presented. With an estimated true neutrino (reactor to detector) direction of $\phi = 40.8\unicode{xB0} \pm 0.7\unicode{xB0}$ and $\theta = 98.6\unicode{xB0} \pm 0.4\unicode{xB0}$, the PROSPECT-I detector is able to reconstruct an average neutrino direction of $\phi = 39.4\unicode{xB0} \pm 2.9\unicode{xB0}$ and $\theta = 97.6\unicode{xB0} \pm 1.6\unicode{xB0}$. This measurement is made with approximately 48000 Inverse Beta Decay signal events and is the most precise directional reconstruction of reactor antineutrinos to date.

A $^6$Li-doped plastic scintillator with pulse-shape discrimination capabilities, commercially identified as EJ-299-50, has been developed and produced at the kilogram-scale. A total of 44 kg-scale bars of dimensions 5.5 cm $\times$ 5.5 cm $\times$ 50 cm of this material have been characterized. Optical properties like light output and effective attenuation length have been found to be comparable to $^6$Li-doped liquid scintillators. The scintillator EJ-299-50 shows good neutron detection capabilities with an effective efficiency for capture on $^6$Li of approximately 85%. Stability tests performed on two formulation variations showed no intrinsic degradation in the material or optical properties during several months of observations.

This paper addresses the problem of diversity-aware sign language production, where we want to give an image (or sequence) of a signer and produce another image with the same pose but different attributes (\textit{e.g.} gender, skin color). To this end, we extend the variational inference paradigm to include information about the pose and the conditioning of the attributes. This formulation improves the quality of the synthesised images. The generator framework is presented as a UNet architecture to ensure spatial preservation of the input pose, and we include the visual features from the variational inference to maintain control over appearance and style. We generate each body part with a separate decoder. This architecture allows the generator to deliver better overall results. Experiments on the SMILE II dataset show that the proposed model performs quantitatively better than state-of-the-art baselines regarding diversity, per-pixel image quality, and pose estimation. Quantitatively, it faithfully reproduces non-manual features for signers.

Sign Language Production (SLP) is a challenging task, given the limited resources available and the inherent diversity within sign data. As a result, previous works have suffered from the problem of regression to the mean, leading to under-articulated and incomprehensible signing. In this paper, we propose using dictionary examples to create expressive sign language sequences. However, simply concatenating the signs would create robotic and unnatural sequences. Therefore, we present a 7-step approach to effectively stitch the signs together. First, by normalising each sign into a canonical pose, cropping and stitching we create a continuous sequence. Then by applying filtering in the frequency domain and resampling each sign we create cohesive natural sequences, that mimic the prosody found in the original data. We leverage the SignGAN model to map the output to a photo-realistic signer and present a complete Text-to-Sign (T2S) SLP pipeline. Our evaluation demonstrates the effectiveness of this approach, showcasing state-of-the-art performance across all datasets., Comment: 18 pages, 3 figures, 4 tables. Accepted to BMVC 2024

Automatic Sign Language Translation requires the integration of both computer vision and natural language processing to effectively bridge the communication gap between sign and spoken languages. However, the deficiency in large-scale training data to support sign language translation means we need to leverage resources from spoken language. We introduce, Sign2GPT, a novel framework for sign language translation that utilizes large-scale pretrained vision and language models via lightweight adapters for gloss-free sign language translation. The lightweight adapters are crucial for sign language translation, due to the constraints imposed by limited dataset sizes and the computational requirements when training with long sign videos. We also propose a novel pretraining strategy that directs our encoder to learn sign representations from automatically extracted pseudo-glosses without requiring gloss order information or annotations. We evaluate our approach on two public benchmark sign language translation datasets, namely RWTH-PHOENIX-Weather 2014T and CSL-Daily, and improve on state-of-the-art gloss-free translation performance with a significant margin., Comment: Accepted at ICLR2024

In response to widening achievement gaps and increased demand for post-secondary education, local and federal governments across the US have enacted policies that have boosted high school graduation rates without an equivalent rise in student achievement, suggesting a decline in academic standards. To the extent that academic standards can shape effort decisions, these trends can have important implications for human capital accumulation. This paper provides both theoretical and empirical evidence of the causal effect of academic standards on student effort and achievement. We develop a theoretical model of endogenous student effort that depends on grading policies, finding that designs that do not account for either the spread of student ability or the magnitude of leniency can increase achievement gaps. Empirically, under a research design that leverages variation from a statewide grading policy and school entry rules, we find that an increase in leniency mechanically increased student GPA without increasing student achievement. At the same time, this policy induced students to increase their school absences. We uncover stark heterogeneity of effects across student ability, with the gains in GPA driven entirely by high ability students and the reductions in attendance driven entirely by low ability students. These differences in responses compound across high school and ultimately widen long-term achievement gaps as measured by ACT scores.

The interaction of atomic orbitals at the interface of perovskite oxide heterostructures has been investigated for its profound impact on the band structures and electronic properties, giving rise to unique electronic states and a variety of tunable functionalities. In this study, we conducted an extensive investigation of the optical and electronic properties of epitaxial NdNiO3 thin films grown on a series of single crystal substrates. Unlike films synthesized on other substrates, NdNiO3 on SrTiO3 (NNO/STO) gives rise to a unique band structure which features an additional unoccupied band situated above the Fermi level. Our comprehensive investigation, which incorporated a wide array of experimental techniques and density functional theory calculations, revealed that the emergence of the interfacial band structure is primarily driven by the orbital hybridization between Ti 3d orbitals of the STO substrate and O 2p orbitals of the NNO thin film. Furthermore, exciton peaks have been detected in the optical spectra of the NNO/STO film, attributable to the pronounced electron-electron (e-e) and electron-hole (e-h) interactions propagating from the STO substrate into the NNO film. These findings underscore the substantial influence of interfacial orbital hybridization on the electronic structure of oxide thin-films, thereby offering key insights into tuning their interfacial properties., Comment: 26 pages,4 figures

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%., Comment: To appear in CVPRW2024

Sign languages, often categorised as low-resource languages, face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets. This paper introduces Select and Reorder (S&R), a novel approach that addresses data scarcity by breaking down the translation process into two distinct steps: Gloss Selection (GS) and Gloss Reordering (GR). Our method leverages large spoken language models and the substantial lexical overlap between source spoken languages and target sign languages to establish an initial alignment. Both steps make use of Non-AutoRegressive (NAR) decoding for reduced computation and faster inference speeds. Through this disentanglement of tasks, we achieve state-of-the-art BLEU and Rouge scores on the Meine DGS Annotated (mDGS) dataset, demonstrating a substantial BLUE-1 improvement of 37.88% in Text to Gloss (T2G) Translation. This innovative approach paves the way for more effective translation models for sign languages, even in resource-constrained settings., Comment: 8 Pages, 5 Figures, 7 Tables, LREC-COLING 2024

Phonetic representations are used when recording spoken languages, but no equivalent exists for recording signed languages. As a result, linguists have proposed several annotation systems that operate on the gloss or sub-unit level; however, these resources are notably irregular and scarce. Sign Language Production (SLP) aims to automatically translate spoken language sentences into continuous sequences of sign language. However, current state-of-the-art approaches rely on scarce linguistic resources to work. This has limited progress in the field. This paper introduces an innovative solution by transforming the continuous pose generation problem into a discrete sequence generation problem. Thus, overcoming the need for costly annotation. Although, if available, we leverage the additional information to enhance our approach. By applying Vector Quantisation (VQ) to sign language data, we first learn a codebook of short motions that can be combined to create a natural sequence of sign. Where each token in the codebook can be thought of as the lexicon of our representation. Then using a transformer we perform a translation from spoken language text to a sequence of codebook tokens. Each token can be directly mapped to a sequence of poses allowing the translation to be performed by a single network. Furthermore, we present a sign stitching method to effectively join tokens together. We evaluate on the RWTH-PHOENIX-Weather-2014T (PHOENIX14T) and the more challenging Meine DGS Annotated (mDGS) datasets. An extensive evaluation shows our approach outperforms previous methods, increasing the BLEU-1 back translation score by up to 72%., Comment: 8 Pages, 3 Figures, 7 Tables, 18th IEEE International Conference on Automatic Face and Gesture Recognition 2024

3D hand pose estimation from images has seen considerable interest from the literature, with new methods improving overall 3D accuracy. One current challenge is to address hand-to-hand interaction where self-occlusions and finger articulation pose a significant problem to estimation. Little work has applied physical constraints that minimize the hand intersections that occur as a result of noisy estimation. This work addresses the intersection of hands by exploiting an occupancy network that represents the hand's volume as a continuous manifold. This allows us to model the probability distribution of points being inside a hand. We designed an intersection loss function to minimize the likelihood of hand-to-point intersections. Moreover, we propose a new hand mesh parameterization that is superior to the commonly used MANO model in many respects including lower mesh complexity, underlying 3D skeleton extraction, watertightness, etc. On the benchmark InterHand2.6M dataset, the models trained using our intersection loss achieve better results than the state-of-the-art by significantly decreasing the number of hand intersections while lowering the mean per-joint positional error. Additionally, we demonstrate superior performance for 3D hand uplift on Re:InterHand and SMILE datasets and show reduced hand-to-hand intersections for complex domains such as sign-language pose estimation.

About 40 transneptunian binaries (TNBs) have fully determined orbits with about 10 others being solved except for breaking the mirror ambiguity. Despite decades of study almost all TNBs have only ever been analyzed with a model that assumes perfect Keplerian motion (e.g., two point masses). In reality, all TNB systems are non-Keplerian due to non-spherical shapes, possible presence of undetected system components, and/or solar perturbations. In this work, we focus on identifying candidates for detectable non-Keplerian motion based on sample of 45 well-characterized binaries. We use MultiMoon, a non-Keplerian Bayesian inference tool, to analyze published relative astrometry allowing for non-spherical shapes of each TNB system's primary. We first reproduce the results of previous Keplerian fitting efforts with MultiMoon, which serves as a comparison for the non-Keplerian fits and confirms that these fits are not biased by the assumption of a Keplerian orbit. We unambiguously detect non-Keplerian motion in 8 TNB systems across a range of primary radii, mutual orbit separations, and system masses. As a proof of concept for non-Keplerian fitting, we perform detailed fits for (66652) Borasisi-Pabu, possibly revealing a $J_2 \approx 0.44$, implying Borasisi (and/or Pabu) may be a contact binary or an unresolved compact binary. However, full confirmation of this result will require new observations. This work begins the next generation of TNB analyses that go beyond the point mass assumption to provide unique and valuable information on the physical properties of TNBs with implications for their formation and evolution., Comment: Accepted to AJ

Recent years have seen significant progress in human image generation, particularly with the advancements in diffusion models. However, existing diffusion methods encounter challenges when producing consistent hand anatomy and the generated images often lack precise control over the hand pose. To address this limitation, we introduce a novel approach to pose-conditioned human image generation, dividing the process into two stages: hand generation and subsequent body outpainting around the hands. We propose training the hand generator in a multi-task setting to produce both hand images and their corresponding segmentation masks, and employ the trained model in the first stage of generation. An adapted ControlNet model is then used in the second stage to outpaint the body around the generated hands, producing the final result. A novel blending technique is introduced to preserve the hand details during the second stage that combines the results of both stages in a coherent way. This involves sequential expansion of the outpainted region while fusing the latent representations, to ensure a seamless and cohesive synthesis of the final image. Experimental evaluations demonstrate the superiority of our proposed method over state-of-the-art techniques, in both pose accuracy and image quality, as validated on the HaGRID dataset. Our approach not only enhances the quality of the generated hands but also offers improved control over hand pose, advancing the capabilities of pose-conditioned human image generation. The source code of the proposed approach is available at https://github.com/apelykh/hand-to-diffusion.

Sign Language Translation (SLT) is a challenging task that aims to generate spoken language sentences from sign language videos. In this paper, we introduce a hybrid SLT approach, Spotter+GPT, that utilizes a sign spotter and a powerful Large Language Model (LLM) to improve SLT performance. Spotter+GPT breaks down the SLT task into two stages. The videos are first processed by the Spotter, which is trained on a linguistic sign language dataset, to identify individual signs. These spotted signs are then passed to an LLM, which transforms them into coherent and contextually appropriate spoken language sentences. The source code of the Spotter is available at https://gitlab.surrey.ac.uk/cogvispublic/sign-spotter.

Self-supervised learning is the key to unlocking generic computer vision systems. By eliminating the reliance on ground-truth annotations, it allows scaling to much larger data quantities. Unfortunately, self-supervised monocular depth estimation (SS-MDE) has been limited by the absence of diverse training data. Existing datasets have focused exclusively on urban driving in densely populated cities, resulting in models that fail to generalize beyond this domain. To address these limitations, this paper proposes two novel datasets: SlowTV and CribsTV. These are large-scale datasets curated from publicly available YouTube videos, containing a total of 2M training frames. They offer an incredibly diverse set of environments, ranging from snowy forests to coastal roads, luxury mansions and even underwater coral reefs. We leverage these datasets to tackle the challenging task of zero-shot generalization, outperforming every existing SS-MDE approach and even some state-of-the-art supervised methods. The generalization capabilities of our models are further enhanced by a range of components and contributions: 1) learning the camera intrinsics, 2) a stronger augmentation regime targeting aspect ratio changes, 3) support frame randomization, 4) flexible motion estimation, 5) a modern transformer-based architecture. We demonstrate the effectiveness of each component in extensive ablation experiments. To facilitate the development of future research, we make the datasets, code and pretrained models available to the public at https://github.com/jspenmar/slowtv_monodepth.

The fine curve graph was introduced as a geometric tool to study homeomorphisms of surfaces. In this paper we study the Gromov boundary of this space and the local topology near points associated with certain foliations and laminations. We then give several applications including finding dynamically explicit elements with positive stable commutator length, and proving a Tits alternative for subgroups of $\textrm{Homeo}(S)$ containing a pseudo-Anosov map, generalizing a result of Hurtado-Xue., Comment: 29 pages, 2 figures