Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity of analysis error covariance to the mis‐specification of background error covariance

Most data assimilation and satellite retrieval methods are based on optimal estimation theory, which assumes that the error covariances of the observations and of the a priori (background) information are known. The specification of background error covariance is crucial to the appropriate interpretation of radiance information from satellite sounders within a numerical weather prediction (NWP) data assimilation system. Uncertainties in the specification of error covariances are inevitable, but an improved understanding of the acceptable range of mis‐specification should lead to improved impact of these observations in NWP. In this study, the sensitivity of analysis error covariance to the mis‐specification of background error covariance is investigated. The linear theory for analysis error covariance is presented, both for the optimal case and for the more general suboptimal case. The theory is first applied to a simple scalar case. A ‘danger zone’ is identified, where a mis‐specification of background error variance results in analysis error variances being greater than background error variances. The boundary of the ‘danger zone’ is quantified as a function of the ratio of observation error variance to background error variance. The investigation is then extended to the vector case, and specifically to the vertical eigenmodes of a forecast error covariance matrix and to the linearized error analysis for a one‐dimensional variational (1D‐Var) retrieval analogous to the assimilation of radiances from the Infrared Atmospheric Sounding Interferometer (IASI). In the optimal case, the assimilation of IASI data can improve the analysis of the leading eigenmodes of temperature and humidity. A suboptimal case is then investigated, by assuming one representation of forecast error covariance in the analysis whilst taking another representation as truth. The analysis is shown to cross into the ‘danger zone’ for a small number of eigenmodes. Equivalent degradation of the analysis in profile space is also shown. Copyright © 2012 British Crown copyright, the Met Office. Published by John Wiley & Sons Ltd.
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions

Consider a setting where there are $N$ heterogeneous units and $p$ interventions. Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i.e., $N \times 2^p$ causal parameters. Choosing a combination of interventions is a problem that naturally arises in a variety of applications such as factorial design experiments, recommendation engines, combination therapies in medicine, conjoint analysis, etc. Running $N \times 2^p$ experiments to estimate the various parameters is likely expensive and/or infeasible as $N$ and $p$ grow. Further, with observational data there is likely confounding, i.e., whether or not a unit is seen under a combination is correlated with its potential outcome under that combination. To address these challenges, we propose a novel latent factor model that imposes structure across units (i.e., the matrix of potential outcomes is approximately rank $r$), and combinations of interventions (i.e., the coefficients in the Fourier expansion of the potential outcomes is approximately $s$ sparse). We establish identification for all $N \times 2^p$ parameters despite unobserved confounding. We propose an estimation procedure, Synthetic Combinations, and establish it is finite-sample consistent and asymptotically normal under precise conditions on the observation pattern. Our results imply consistent estimation given $\text{poly}(r) \times \left( N + s^2p\right)$ observations, while previous methods have sample complexity scaling as $\min(N \times s^2p, \ \ \text{poly(r)} \times (N + 2^p))$. We use Synthetic Combinations to propose a data-efficient experimental design. Empirically, Synthetic Combinations outperforms competing approaches on a real-world dataset on movie recommendations. Lastly, we extend our analysis to do causal inference where the intervention is a permutation over $p$ items (e.g., rankings).
Causality Detection for Efficient Multi-Agent Reinforcement Learning

When learning a task as a team, some agents in Multi-Agent Reinforcement Learning (MARL) may fail to understand their true impact in the performance of the team. Such agents end up learning sub-optimal policies, demonstrating undesired lazy behaviours. To investigate this problem, we start by formalising the use of temporal causality applied to MARL problems. We then show how causality can be used to penalise such lazy agents and improve their behaviours. By understanding how their local observations are causally related to the team reward, each agent in the team can adjust their individual credit based on whether they helped to cause the reward or not. We show empirically that using causality estimations in MARL improves not only the holistic performance of the team, but also the individual capabilities of each agent. We observe that the improvements are consistent in a set of different environments.
Revisiting the Thermal AdS partition function

We rewrite the worldsheet torus partition function of the Thermal AdS CFT by isolating the boundary parameters. Using this, we show that the spectrum of the Euclidean BTZ black hole and Lorentzian AdS3 can be extracted -- the latter as a zero temperature limit. A similar procedure recovers the Lorentzian BTZ spectrum proposed in an earlier work. We then use our expression to construct a boundary modular invariant expression as a Poincar\'e series.
How to generate a fault-resilient network at a lower cost

Blockchains facilitate decentralization, security, identity, and data management in cyber-physical systems. However, consensus protocols used in blockchains are prone to high message and computational complexity costs and are not suitable to be used in IoT. One way to reduce message complexity is to randomly assign network nodes into committees or shards. Keeping committee sizes small is then desirable in order to achieve lower message complexity, but this comes with a penalty of reduced reliability as there is a higher probability that a large number of faulty nodes will end up in a committee. In this work, we study the problem of estimating a probability of a failure in randomly sharded networks. We provide new results and improve existing bounds on the failure probability. Thus, our framework also paves the way to reduce committee sizes without reducing reliability.
DiffuScene: Scene Graph Denoising Diffusion Probabilistic Model for Generative Indoor Scene Synthesis

We present DiffuScene for indoor 3D scene synthesis based on a novel scene graph denoising diffusion probabilistic model, which generates 3D instance properties stored in a fully-connected scene graph and then retrieves the most similar object geometry for each graph node i.e . object instance which is characterized as a concatenation of different attributes, including location, size, orientation, semantic, and geometry features. Based on this scene graph, we designed a diffusion model to determine the placements and types of 3D instances. Our method can facilitate many downstream applications, including scene completion, scene arrangement, and text-conditioned scene synthesis. Experiments on the 3D-FRONT dataset show that our method can synthesize more physically plausible and diverse indoor scenes than state-of-the-art methods. Extensive ablation studies verify the effectiveness of our design choice in scene diffusion models.
Avatarm: an Avatar With Manipulation Capabilities for the Physical Metaverse

Metaverse is an immersive shared space that remote users can access through virtual and augmented reality interfaces, enabling their avatars to interact with each other and the surrounding. Although digital objects can be manipulated, physical objects cannot be touched, grasped, or moved within the metaverse due to the lack of a suitable interface. This work proposes a solution to overcome this limitation by introducing the concept of a Physical Metaverse enabled by a new interface named “Avatarm”. The Avatarm consists in an avatar enhanced with a robotic arm that performs physical manipulation tasks while remaining entirely hidden in the metaverse. The users have the illusion that the avatar is directly manipulating objects without the mediation by a robot. The Avatarm is the first step towards a new metaverse, the “Physical Metaverse,” where users can physically interact each other and with the environment.
Planet formation via pebble accretion in externally photoevaporating discs

We demonstrate that planet formation via pebble accretion is sensitive to external photoevaporation of the outer disc. In pebble accretion, planets grow by accreting from a flux of solids (pebbles) that radially drift inwards from the pebble production front. If external photoevaporation truncates the outer disc fast enough, it can shorten the time before the pebble production front reaches the disc outer edge, cutting off the supply of pebble flux for accretion, hence limiting the pebble mass reservoir for planet growth. Conversely, cloud shielding can protect the disc from strong external photoevaporation and preserve the pebble reservoir. Because grain growth and drift can occur quickly, shielding even on a short time-scale (<1 Myr) can have a non-linear impact on the properties of planets growing by pebble accretion. For example a 10−3 M⊕ planetary seed at 25 au stays at 25 au with a lunar mass if the disc is immediately irradiated by a 103 G0 field, but grows and migrates to be approximately Earth-like in both mass and orbital radius if the disc is shielded for just 1 Myr. In NGC 2024, external photoevaporation is thought to happen to discs that are <0.5 Myr old, which coupled with the results here suggests that the exact planetary parameters can be very sensitive to the star forming environment. Universal shielding for time-scales of at least ∼1.5 Myr would be required to completely nullify the environmental impact on planetary architectures.
Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation

Neural machine translation (NMT) has progressed rapidly over the past several years, and modern models are able to achieve relatively high quality using only monolingual text data, an approach dubbed Unsupervised Machine Translation (UNMT). However, these models still struggle in a variety of ways, including aspects of translation that for a human are the easiest - for instance, correctly translating common nouns. This work explores a cheap and abundant resource to combat this problem: bilingual lexica. We test the efficacy of bilingual lexica in a real-world set-up, on 200-language translation models trained on web-crawled text. We present several findings: (1) using lexical data augmentation, we demonstrate sizable performance gains for unsupervised translation; (2) we compare several families of data augmentation, demonstrating that they yield similar improvements, and can be combined for even greater improvements; (3) we demonstrate the importance of carefully curated lexica over larger, noisier ones, especially with larger models; and (4) we compare the efficacy of multilingual lexicon data versus human-translated parallel data. Finally, we open-source GATITOS (available at https://github.com/google-research/url-nlp/tree/main/gatitos), a new multilingual lexicon for 26 low-resource languages, which had the highest performance among lexica in our experiments.
Generalizable Neural Voxels for Fast Human Radiance Fields

Rendering moving human bodies at free viewpoints only from a monocular video is quite a challenging problem. The information is too sparse to model complicated human body structures and motions from both view and pose dimensions. Neural radiance fields (NeRF) have shown great power in novel view synthesis and have been applied to human body rendering. However, most current NeRF-based methods bear huge costs for both training and rendering, which impedes the wide applications in real-life scenarios. In this paper, we propose a rendering framework that can learn moving human body structures extremely quickly from a monocular video. The framework is built by integrating both neural fields and neural voxels. Especially, a set of generalizable neural voxels are constructed. With pretrained on various human bodies, these general voxels represent a basic skeleton and can provide strong geometric priors. For the fine-tuning process, individual voxels are constructed for learning differential textures, complementary to general voxels. Thus learning a novel body can be further accelerated, taking only a few minutes. Our method shows significantly higher training efficiency compared with previous methods, while maintaining similar rendering quality. The project page is at https://taoranyi.com/gneuvox .
Dissociation dynamics of doubly excited states of molecular hydrogen

The experimental cross sections for the various decay channels of the doubly excited states of H2 after photon absorption have been modelled as a superposition of scaled absorption profiles. Limits of the adiabatic hypothesis for the dissociation of these states were put into evidence, and some non-adiabatic mixings could be quantified. The various dissociation channels have been identified, state by state, and the various dissociation yields determined when the dipole moment was known.
Muon spin relaxation and emergence of disorder-induced unconventional dynamic magnetic fluctuations in Dy$_{2}$Zr$_{2}$O$_{7}$

The disordered pyrochlore oxide Dy$_{2}$Zr$_{2}$O$_{7}$ shows the signatures of field-induced spin freezing with remnant zero-point spin-ice entropy at 5 kOe magnetic field. We have performed zero-field and longitudinal field Muon spin relaxation ($\mu$SR) studies on Dy$_{2}$Zr$_{2}$O$_{7}$. Our zero field studies reveal the absence of both long-range ordering and spin freezing down to 62 mK. The $\mu$SR relaxation rate exhibits a temperature-independent plateau below 4 K, indicating a dynamic ground state of fluctuating spins similar to the well-known spin ice system Dy$_{2}$Ti$_{2}$O$_{7}$. The low-temperature spin fluctuations persist in the longitudinal field of 20 kOe as well and show unusual field dependence of the relaxation rate, which is uncommon for a spin-liquid system. Our results, combined with the previous studies do not show any evidence of spin ice or spin glass ground state, rather point to a disorder-induced dynamic magnetic ground state in the Dy$_{2}$Zr$_{2}$O$_{7}$ material.
Design of a Smart Waste Management System for the City of Johannesburg

Every human being in this world produces waste. South Africa is a developing country with many townships that have limited waste resources. Over-increasing population growth overpowers the volume of most municipal authorities to provide even the most essential services. Waste in townships is produced via littering, dumping of bins, cutting of trees, dumping of waste near rivers, and overrunning of waste bins. Waste increases diseases, air pollution, and environmental pollution, and lastly increases gas emissions that contribute to the release of greenhouse gases. The ungathered waste is dumped widely in the streets and drains contributing to flooding, breeding of insects, rodent vectors, and spreading of diseases. Therefore, the aim of this paper is to design a smart waste management system for the city of Johannesburg. The city of Johannesburg contains waste municipality workers and has provided some areas with waste resources such as waste bins and trucks for collecting waste. But the problem is that the resources only are not enough to solve the problem of waste in the city. The waste municipality uses traditional ways of collecting waste such as going to each street and picking up waste bins. The traditional way has worked for years but as the population is increasing more waste is produced which causes various problems for the waste municipalities and the public at large. The proposed system consists of sensors, user applications, and a real-time monitoring system. This paper adopts the experimental methodology.
A longitudinal study of the top 1% toxic Twitter profiles

Toxicity is endemic to online social networks (OSNs) including Twitter. It follows a Pareto-like distribution where most of the toxicity is generated by a very small number of profiles and as such, analyzing and characterizing these “toxic profiles” is critical. Prior research has largely focused on sporadic, event-centric toxic content (i.e., tweets) to characterize toxicity on the platform. Instead, we approach the problem of characterizing toxic content from a profile-centric point of view. We study 143K Twitter profiles and focus on the behavior of the top 1% producers of toxic content on Twitter, based on toxicity scores of their tweets availed by Perspective API. With a total of 293M tweets, spanning 16 years of activity, the longitudinal data allows us to reconstruct the timelines of all profiles involved. We use these timelines to gauge the behavior of the most toxic Twitter profiles compared to the rest of the Twitter population. We study the pattern of tweet posting from highly toxic accounts, based on the frequency and how prolific they are, the nature of hashtags and URLs, profile metadata, and Botometer scores. We find that the highly toxic profiles post coherent and well-articulated content, their tweets keep to a narrow theme with lower diversity in hashtags, URLs, and domains, they are thematically similar to each other, and have a high likelihood of bot-like behavior, likely to have progenitors with intentions to influence, based on high fake followers score. Our work contributes insight into the top 1% toxic profiles on Twitter and establishes the profile-centric approach to investigate toxicity on Twitter to be beneficial. The identification of the most toxic profiles can aid in the reporting and suspension of such profiles, making Twitter a better place for discussions. Finally, we contribute to the research community with this large-scale and longitudinal dataset1, annotated with six types of toxic scores.
PeakNet: Bragg peak finding in X-ray crystallography experiments with U-Net

—Serial crystallography at X-ray free electron laser (XFEL) sources has experienced tremendous progress in achieving high data rate in recent times. While this development offers potential to enable novel scientiﬁc investigations, such as imaging molecular events at logarithmic timescales, it also poses challenges in regards to real-time data analysis, which involves some degree of data reduction to only save those features or images pertaining to the science on disks. If data reduction is not effective, it could directly result in a substantial increase in facility budgetary requirements, or even hinder the utilization of ultra-high repetition imaging techniques making data analysis unwieldy. Furthermore, an additional challenge involves providing real-time feedback to users derived from real- time data analysis. In the context of serial crystallography, the initial and critical step in real-time data analysis is ﬁnding X-ray Bragg peaks from diffraction images. To tackle this challenge, we present PeakNet, a Bragg peak ﬁnder that utilizes neural networks and runs about four times faster than Psocake peak ﬁnder, while delivering signiﬁcantly better indexing rates and comparable number of indexed events. We formulated the task of peak ﬁnding into a semantic segmentation problem, which is implemented as a classical U-Net architecture. A key advantage of PeakNet is its ability to scale linearly with respect to data volume, making it well-suited for real-time serial crystallography data analysis at high data rates.
Recognizing Rigid Patterns of Unlabeled Point Clouds by Complete and Continuous Isometry Invariants with no False Negatives and no False Positives

Rigid structures such as cars or any other solid objects are often represented by finite clouds of unlabeled points. The most natural equivalence on these point clouds is rigid motion or isometry maintaining all inter-point distances. Rigid patterns of point clouds can be reliably compared only by complete isometry invariants that can also be called equivariant descriptors without false negatives (isometric clouds having different descriptions) and without false positives (non-isometric clouds with the same description). Noise and motion in data motivate a search for invariants that are continuous under perturbations of points in a suitable metric. We propose the first continuous and complete invariant of unlabeled clouds in any Euclidean space. For a fixed dimension, the new metric for this invariant is computable in a polynomial time in the number of points.
Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety

The rapid growth in user generated content on social media has resulted in a significant rise in demand for automated content moderation. Various methods and frameworks have been proposed for the tasks of hate speech detection and toxic comment classification. In this work, we combine common datasets to extend these tasks to brand safety. Brand safety aims to protect commercial branding by identifying contexts where advertisements should not appear and covers not only toxicity, but also other potentially harmful content. As these datasets contain different label sets, we approach the overall problem as a binary classification task. We demonstrate the need for building brand safety specific datasets via the application of common toxicity detection datasets to a subset of brand safety and empirically analyze the effects of weighted sampling strategies in text classification.
Multicarrier Rate-Splitting Multiple Access: Superiority of OFDM-RSMA Over OFDMA and OFDM-NOMA

Rate-splitting multiple access (RSMA) is a multiple access technique generalizing conventional techniques, such as, space-division multiple access (SDMA), non-orthogonal multiple access (NOMA), and physical layer multi-casting, which aims to address multi-user interference (MUI) in multiple-input multipleoutput (MIMO) systems. In this study, we leverage the interference management capabilities of RSMA to tackle the issue of inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) waveform. We formulate a problem to find the optimal subcarrier and power allocation for downlink transmission in a two-user system using RSMA and OFDM and propose a weighted minimum mean-square error (WMMSE)- based algorithm to obtain a solution. The sum-rate performance of the proposed OFDM-RSMA scheme is compared with that of conventional orthogonal frequency division multiple access (OFDMA) and OFDM-NOMA by numerical results. It is shown that the proposed OFDM-RSMA outperforms OFDM-NOMA and OFDMA under ICI in diverse propagation channel conditions owing to its flexible structure and robust interference management capabilities.
PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters

We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters. Our anime-style domain poses unique challenges to single-view reconstruction; compared to natural images of human heads, character portrait illustrations have hair and accessories with more complex and diverse geometry, and are shaded with non-photorealistic contour lines. In addition, there is a lack of both 3D model and portrait illustration data suitable to train and evaluate this ambiguous stylized reconstruction task. Facing these challenges, our proposed PAniC-3D architecture crosses the illustration-to-3D domain gap with a line-filling model, and represents sophisticated geometries with a volumetric radiance field. We train our system with two large new datasets (11.2k Vroid 3D models, 1k Vtuber portrait illustrations), and evaluate on a novel AnimeRecon benchmark of illustration-to-3D pairs. PAniC-3D significantly outper-forms baseline methods, and provides data to establish the task of stylized reconstruction from portrait illustrations.