Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Modeling in Structural-Hankel Domain for Color Image Inpainting

In recent years, some researchers focused on using a single image to obtain a large number of samples through multi-scale features. This study intends to a brand-new idea that requires only ten or even fewer samples to construct the low-rank structural-Hankel matrices-assisted score-based generative model (SHGM) for color image inpainting task. During the prior learning process, a certain amount of internal-middle patches are firstly extracted from several images and then the structural-Hankel matrices are constructed from these patches. To better apply the score-based generative model to learn the internal statistical distribution within patches, the large-scale Hankel matrices are finally folded into the higher dimensional tensors for prior learning. During the iterative inpainting process, SHGM views the inpainting problem as a conditional generation procedure in low-rank environment. As a result, the intermediate restored image is acquired by alternatively performing the stochastic differential equation solver, alternating direction method of multipliers, and data consistency steps. Experimental results demonstrated the remarkable performance and diversity of SHGM.
HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising

The paper presents a novel approach for vector-floorplan generation via a diffusion model, which denoises 2D coordinates of room/door corners with two inference objectives: 1) a single-step noise as the continuous quantity to precisely invert the continuous forward process; and 2) the final 2D coordinate as the discrete quantity to establish geometric incident relationships such as parallelism, orthogonality, and corner-sharing. Our task is graph-conditioned floorplan generation, a common workflow in floorplan design. We represent a floorplan as 1D polygonal loops, each of which corresponds to a room or a door. Our diffusion model employs a Transformer architecture at the core, which controls the attention masks based on the input graph-constraint and directly generates vector-graphics floorplans via a discrete and continuous denoising process. We have evaluated our approach on RPLAN dataset. The proposed approach makes significant improvements in all the metrics against the state-of-the-art with significant margins, while being capable of generating non-Manhattan structures and controlling the exact number of corners per room. A project website with supplementary video and document is here https://aminshabani.github. io/housediffusion.
Emergence of prethermal states in a driven dissipative system through cross-correlated dissipation

Periodically driven closed quantum many-body systems are known to exhibit prethermal or quasi-steady-state dynamics. In this work, we theoretically show that such prethermal phases can appear in the dynamics of a dipolar two-spin-1/2 system coupled to a heat bath if the cross terms between the drive and dipolar interactions are taken into consideration. To this end, we use our recently reported fluctuation-regulated quantum master equation (Chakrabarti A. and Bhattacharyya R., Phys. Rev. A 97 (2018) 063837), to show that the predicted dynamics can successfully explain the experimentally observed features of the transient and prethermal regime.
A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation

The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control. In this work, we explore a method for learning a single policy that manipulates various forms of agents to solve various tasks by distilling a large amount of proficient behavioral data. In order to align input-output (IO) interface among multiple tasks and diverse agent morphologies while preserving essential 3D geometric relations, we introduce morphology-task graph, which treats observations, actions and goals/task in a unified graph representation. We also develop MxT-Bench for fast large-scale behavior generation, which supports procedural generation of diverse morphology-task combinations with a minimal blueprint and hardware-accelerated simulator. Through efficient representation and architecture selection on MxT-Bench, we find out that a morphology-task graph representation coupled with Transformer architecture improves the multi-task performances compared to other baselines including recent discrete tokenization, and provides better prior knowledge for zero-shot transfer or sample efficiency in downstream multi-task imitation learning. Our work suggests large diverse offline datasets, unified IO representation, and policy representation and architecture selection through supervised learning form a promising approach for studying and advancing morphology-task generalization.
PUFFIN: Pitch-Synchronous Neural Waveform Generation for Fullband Speech on Modest Devices

We present a neural vocoder designed with low-powered Alternative and Augmentative Communication devices in mind. By combining elements of successful modern vocoders with established ideas from an older generation of technology, our system is able to produce high quality synthetic speech at 48kHz on devices where neural vocoders are otherwise prohibitively complex. The system is trained adversarially using differentiable pitch synchronous overlap add, and reduces complexity by relying on pitch synchronous Inverse Short-Time Fourier Transform (ISTFT) to generate speech samples. Our system achieves comparable quality with a strong baseline (HiFi-GAN) while using only a fraction of the compute. We present results of a perceptual evaluation as well as an analysis of system complexity.
Long-term climate simulation in NorESM: burst-coupling the sediment in the BLOM/iHAMOCC ocean module

In this report we set forth a simulation method for long-term simulations of NorESM, the Norwegian Earth System Model. In this the sediment is repeatedly decoupled and coupled to the ocean model (BLOM/iHAMOCC), a process called burst coupling . Through this, the ocean (seawater and sediment) is brought into an approximate steady state. We show that just the model has to run at least 50 000 yr to get in an approximate steady state. With burst coupling this can be done in a computationally reasonable time (wall time in the order of one week). The method can be used to gener-ate the sediment over hundreds of thousands of years, so it is useful not only for present-day simulations but also for paleo-climatological studies.
Towards Interpretable Anomaly Detection via Invariant Rule Mining

In the research area of anomaly detection, novel and promising methods are frequently developed. However, most existing studies, especially those leveraging deep neural networks, exclusively focus on the detection task only and ignore the interpretability of the underlying models as well as their detection results. However, anomaly interpretation, which aims to provide explanation of why speciﬁc data instances are identiﬁed as anomalies, is an equally (if not more) important task in many real-world applications. In this work, we pursue highly interpretable anomaly detection via invariant rule mining. Speciﬁcally, we leverage decision tree learning and association rule mining to automatically generate invariant rules that are consistently satisﬁed by the underlying data generation process. The generated invariant rules can provide explicit explanation of anomaly detection results and thus are extremely useful for subsequent decision-making. Furthermore, our empirical evaluation shows that the proposed method can also achieve comparable performance in terms of AUC and partial AUC with popular anomaly detection models in various benchmark datasets.
Bifacial Thermophotovoltaic Energy Conversion

Thermophotovoltaic (TPV) energy conversion efficiency has recently surpassed 30%. The key behind such high efficiency is the inclusion of a highly efficient mirror in the rear of the TPV cell that turns back to the thermal emitter the outband energy photons. Efficiencies over 50% could be theoretically attainable by approaching a mirror reflectance of 100%. However, the very few percent of outband absorption significantly deteriorate the conversion efficiency. Thus, current research focuses on developing advance mirror designs able to reach an extreme high outband reflectance over 95%. In this article I propose a bifacial TPV cell that enables very efficient photon recycling without using mirrors and that is less sensitive to outband optical losses. The key to this design is that the cell is introduced in a thermal emitter enclosure where it is irradiated from both sides. Then, outband photons transmit through the cell and are re-absorbed in the emitter. Therefore, the optical losses linked to the mirror/cell interface are eliminated, potentially enabling higher photon recycling efficiencies. This article presents a detailed balance simulation of an edge-cooled bifacial TPV cell to demonstrate that bifacial configuration enables higher conversion efficiencies and twice much as power density than monofacial designs, the latter being a remarkable advantage for moderate temperature and low-cost TPV power generation. Therefore, bifacial TPV cells are appealing for developing practical high-efficient and low-cost TPV devices for power generation in an extended range of heat source temperatures.
Likelihood ratio tests in curved exponential families with nuisance parameters present only under the alternative

For submodels of an exponential family, we consider likelihood ratio tests for hypotheses that render some parameters nonidentifiable. First, we establish the asymptotic equivalence between the likelihood ratio test and the score test. Secondly, the score-test representation is used to derive the asymptotic distribution of the likelihood ratio test. These results are derived for general submodels of an exponential family without assuming compactness of the parameter space. We then exemplify the results on a class of multivariate normal models, where null hypotheses concerning the covariance structure lead to loss of identifiability of a parameter. Our motivating problem throughout the paper is to test a random intercepts model against an alternative covariance structure allowing for serial correlation. Copyright 2005, Oxford University Press.
MP-GELU Bayesian Neural Networks: Moment Propagation by GELU Nonlinearity

Bayesian neural networks (BNNs) have been an important framework in the study of uncertainty quantification. Deterministic variational inference, one of the inference methods, utilizes moment propagation to compute the predictive distributions and objective functions. Unfortunately, deriving the moments requires computationally expensive Taylor expansion in nonlinear functions, such as a rectified linear unit (ReLU) or a sigmoid function. Therefore, a new nonlinear function that realizes faster moment propagation than conventional functions is required. In this paper, we propose a novel nonlinear function named moment propagating-Gaussian error linear unit (MP-GELU) that enables the fast derivation of first and second moments in BNNs. MP-GELU enables the analytical computation of moments by applying nonlinearity to the input statistics, thereby reducing the computationally expensive calculations required for nonlinear functions. In empirical experiments on regression tasks, we observed that the proposed MP-GELU provides higher prediction accuracy and better quality of uncertainty with faster execution than those of ReLU-based BNNs.
M2M: A general method to perform various data analysis tasks from a differentially private sketch

Differential privacy is the standard privacy definition for performing analyses over sensitive data. Yet, its privacy budget bounds the number of tasks an analyst can perform with reasonable accuracy, which makes it challenging to deploy in practice. This can be alleviated by private sketching, where the dataset is compressed into a single noisy sketch vector which can be shared with the analysts and used to perform arbitrarily many analyses. However, the algorithms to perform specific tasks from sketches must be developed on a case-by-case basis, which is a major impediment to their use. In this paper, we introduce the generic moment-to-moment (M$^2$M) method to perform a wide range of data exploration tasks from a single private sketch. Among other things, this method can be used to estimate empirical moments of attributes, the covariance matrix, counting queries (including histograms), and regression models. Our method treats the sketching mechanism as a black-box operation, and can thus be applied to a wide variety of sketches from the literature, widening their ranges of applications without further engineering or privacy loss, and removing some of the technical barriers to the wider adoption of sketches for data exploration under differential privacy. We validate our method with data exploration tasks on artificial and real-world data, and show that it can be used to reliably estimate statistics and train classification models from private sketches.
EDGAR: Embedded Detection of Gunshots by AI in Real-time

Electronic shot counters allow armourers to perform preventive and predictive maintenance based on quantitative measurements, improving reliability, reducing the frequency of accidents, and reducing maintenance costs. To answer a market pressure for both low lead time to market and increased customisation, we aim to solve the shot detection and shot counting problem in a generic way through machine learning. In this study, we describe a method allowing one to construct a dataset with minimal labelling effort by only requiring the total number of shots fired in a time series. To our knowledge, this is the first study to propose a technique, based on learning from label proportions, that is able to exploit these weak labels to derive an instance-level classifier able to solve the counting problem and the more general discrimination problem. We also show that this technique can be deployed in heavily constrained microcontrollers while still providing hard real-time (<100ms) inference. We evaluate our technique against a state-of-the-art unsupervised algorithm and show a sizeable improvement, suggesting that the information from the weak labels is successfully leveraged. Finally, we evaluate our technique against human-generated state-of-the-art algorithms and show that it provides comparable performance and significantly outperforms them in some offline and real-world benchmarks.
Towards computer vision technologies: Semi-automated reading of automated utility meters

: In this report we analysed a possibility of using computer vision techniques for automated reading of utility meters. In our study, we focused on two computer vision techniques: an open-source solution Tensorﬂow Object Detection (Tensorﬂow) and a commercial solution Anyline. This report extends our previous publication [51]: We start with presentation of a structured analysis of related approaches. After that we provide a detailed comparison of two computer vision technologies, Tensorﬂow Object Detection (Tensorﬂow) and Anyline, applied to semi-automated reading of utility meters. In this paper, we discuss limitations and beneﬁts of each solution applied to utility meters reading, especially focusing on aspects such as accuracy and inference time. Our goal was to determine the solution that is the most suitable for this particular application area, where there are several speciﬁc challenges.
Bovine Tuberculosis in Britain: identifying signatures of polarisation and controversy on Twitter

Approaches to disease control are influenced by and reflected in public opinion, and the two are intrinsically entwined. Bovine tuberculosis (bTB) in British cattle and badgers is one example where there is a high degree of polarisation in opinion. Bovine viral diarrhoea (BVD), on the other hand, does not have the same controversy. In this paper we examine how language subjectivity on Twitter differs when comparing the discourses surrounding bTB and BVD, using a combination of network analysis and language and sentiment analysis. That data used for this study was collected from the Twitter public API over a two-year period. We investigated the network structure, language content, and user profiles of tweets featuring both diseases. While analysing network structure showed little difference between the two disease topics, elements of the structure allowed us to better investigate the language structure and profile of users. We found distinct differences between the language and sentiment used in tweets about each disease, and in the profile of the users who were doing the tweeting. We hope that this will guide further investigation and potential avenues for surveillance or the control of misinformation.
Matrix models and holography: Mass deformations of long quiver theories in 5d and 3d

We enlarge the dictionary between matrix models for long linear quivers preserving eight supercharges in d=5d=5 and d=3d=3 and type IIB supergravity backgrounds with AdS_{d+1}d+1 factors. We introduce mass deformations of the field theory that break the quiver into a collection of interacting linear quivers, which are decoupled at the end of the RG flow. We find and solve a Laplace problem in supergravity which realises these deformations holographically. The free energy and expectation values of antisymmetric Wilson loops are calculated on both sides of the proposed duality, finding agreement. With our matching procedure, the free energy satisfies a strong version of the F-theorem.
Identifiability of nonlinear ODE Models with Time-Varying Parameters: the General Analytical Solution and Applications in Viral Dynamics

Identifiability is a structural property of any ODE model characterized by a set of unknown parameters. It describes the possibility of determining the values of these parameters from fusing the observations of the system inputs and outputs. This paper finds the general analytical solution of this fundamental problem and, based on this, provides a general and automated analytical method to determine the identifiability of the unknown parameters. In particular, the method can handle any model, regardless of its complexity and type of non-linearity, and provides the identifiability of the parameters even when they are time-varying. In addition, it is automatic as it simply needs to follow the steps of a systematic procedure that only requires to perform the calculation of derivatives and matrix ranks. Time-varying parameters are treated as unknown inputs and their identification is based on the very recent analytical solution of the unknown input observability problem, recently published on Information Fusion journal. The method is used to determine the identifiability of the unknown time-varying parameters that characterize two non-linear models in the field of viral dynamics (HIV and Covid-19). New fundamental properties that characterize these viral models are determined and discussed in detail through a comparison with the state-of-the-art results. In particular, regarding the very popular HIV ODE model here investigated, the method automatically finds a new important result that is in contrast with the results in the current literature.
S-wave resonances in the positron–hydrogen system with screened Coulomb potentials

We have made a first calculation for S-wave resonances in positron–hydrogen scattering under the influence of Debye plasma environments. A screened Coulomb potential obtained from the Debye model is used to represent the interaction between the nucleus, the electron and the positron. Hylleraas-type wavefunctions are used to represent the correlation effect between the charge particles. The stabilization method is used to calculate the density of resonance states, from which resonance energies and widths are deduced. We have found two resonances of which one is associated with the hydrogen n = 2 threshold, and the other one is found lying below the Ps(2S) threshold. The resonance parameters (energies and widths) calculated for various Debye lengths are reported.
Near-infrared Spectroscopy of Ultracompact H ii Regions in W51A with NIFS/ALTAIR

W51A is the most active star formation region of the giant H ii region W51. It harbors the two massive protoclusters W51e and W51 IRS2, which are very rare in the Galaxy. We aim to identify the newborn massive stars and ultracompact H ii regions to derive its distance and age. We performed Integral Field Unit observations with NIFS+ALTAIR of nine targets in the W51A subregion. The distance modulus was obtained using the spectral classification in the K band and a reddening law appropriate to the inner Galactic plane. We derived the distance and the spectral types for five of the targets, ranging from O8 to O9.5, similar to those derived from radio continuum data, except for two sources to which we assigned a somewhat later spectral type. We included another seven objects with precise spectral classification from other works, which allowed us to better constrain the distance estimate. Our spectrophotometric distance d = 4.80 ± 1.27 kpc is in good agreement with those derived from the Galactic rotation model and trigonometric parallaxes, placing the region near the tangent point of the Sagittarius arm. We conclude that the stars studied in this work have an age spread of 1.5–4 Myr, substantially older than thought to date.
Counter-ﬂow induced clustering in two species ASEP with impurity activated ﬂips under periodic boundary condition

We analyze the cluster formation in a non-ergodic stochastic system as a result of counter-ﬂow, with the aid of an exactly solvable model. To illustrate the clustering, a two species asymmetric simple exclusion process with impurities on a periodic lattice is considered, where the impurity can activate ﬂips between the two non-conserved species. Exact analytical results, supported by Monte Carlo simulations, show two distinct phases, free ﬂowing phase and clustering phase . The clustering phase is characterized by constant density and vanishing current of the non-conserved species, whereas the free ﬂowing phase is identiﬁed with non-monotonic density and non-monotonic ﬁnite current of the same. The n -point spatial correlation between n consecutive vacancies grows with increasing n in the clustering phase, indicating the formation of two macroscopic clusters in this phase, one of the vacancies and the other consisting of all the particles. We deﬁne a rearrangement parameter that permutes the ordering of particles in the initial conﬁguration, keeping all the input parameters ﬁxed. This rearrangement parameter reveals the signiﬁcant eﬀect of non-ergodicity on the onset of clustering. For a special choice of the microscopic dynamics, we connect the present model to a system of run and tumble particles used to model active matter, where the two species having opposite net bias manifest the two possible run directions of the run and tumble particles, and the impurities act as tumbling reagents that enable the tumbling process.
A Novel Surface Reconstruction Framework from Point Clouds without Normal

In this paper, we proposed a novel surface reconstruction framework from point clouds without normal. The proposed method involves three processes: partition of unit, which divide the point clouds into sub-domains using octree structure, generation of the sub-surface, which fit the sub-surface by implicit function in each sub-domain, the normal alignment, which compute normal of sub-surface and inference the global normal of surface by iteratively propagate algorithm. During the last process, sub-surfaces are blended to form entire surface reconstruction. The method is suitable to reconstruct surface with lack of information. The experimental results demonstrate that the method is effective in surface reconstruction.