Miracle-dz/UniFAR-FTUs huggingface dataset:
GAMMA: Generative Augmentation for Attentive Marine Debris Detection

We propose an efﬁcient and generative augmentation approach to solve the inade-quacy concern of underwater debris data for visual detection. We use cycleGAN as a data augmentation technique to convert openly available, abundant data of terrestrial plastic to underwater-style images. Prior works just focus on augmenting or enhancing existing data, which moreover adds bias to the dataset. Compared to our technique, which devises variation, transforming additional in-air plastic data to the marine background. We also propose a novel architecture for underwater debris detection using an attention mechanism. Our method helps to focus only on relevant instances of the image, thereby enhancing the detector performance, which is highly obliged while detecting the marine debris using Autonomous Underwater Vehicle (AUV). We perform extensive experiments for marine debris detection using our approach. Quantitative and qualitative results demonstrate the potential of our framework that signiﬁcantly outperforms the state-of-the-art methods.
Theory and simulation of ovoidal disclination loops in nematic liquid crystals under conical confinement

We present analysis, scaling and modelling based on a previously presented nonlinear nonlocal nematic elastica equation of disclination loop growth in nematic liquid crystals conﬁned to conical geometries with homeotropic anchoring conditions. The +1/2 disclination loops arise during the well-known planar radial to planar polar texture transformation and are attached to +1 singular core disclination at two branch points. The shape of the +1/2 loops is controlled by the axial speed of the branch points and the bending stiffness of the disclination both of which being affected by the confinement gradients (reduction in cross-sectional area) of a conical geometry. Motion towards the cone apex results in faster branch point motions and weaker curvature changes, but motion away from the apex results in slower branch point motion and stronger curvature changes. The simultaneous action of these effects results in novel ovoidal disclination loops. The numerical results are condensed into useful power laws and integrated into a shape/energy analysis that reveals the effects of confinement and its gradient on ovoidal disclination loops. These new findings are useful to characterise the Frank elasticity of new nematic mesophases and to predict novel defect structures under complex confinement.
HST Images of the Extremely Clumpy Radio Galaxy 1138–262 at z = 2.2

We present and discuss optical continuum images made with the Hubble Space Telescope of the radio galaxy 1138-262 at z = 2.2. This object possesses one of the clumpiest optical morphologies of all known high-redshift radio galaxies, consisting of a bright nucleus aligned along the radio axis and a number of smaller components distributed over a region as large as the radio source (130 kpc). The clumps have sizes ranging from 3 to 13 kpc and absolute visual magnitudes between MV = -21.8 and MV = -19.4. On the basis of HST and previous observations, we claim that these clumps are star-forming galaxies that will be accreted by the host galaxy of 1138-262. We compare this radio galaxy with other high-redshift objects and with the predictions of current scenarios of galaxy formation.
Fractional Path Integrals and its degeneration to Dimensional Regularization

In this work we study particles propagate in a fractional path and use fractional derivatives to extend the dynamic dimension of Quantum Field Theory. we construct the Lagrangian of fractional scalar, vector and spinor fields to obtain their propagators by path integral. Then we compute the typical tree level and one loop diagrams which correspond to QED cases. The calculations show the dimension dependence of amplitudes. Additionally, in one loop calculation we obtain results which are consistent with dimensional regularization as the dimension approaches to the Standard Model value. Therefore, the fractional Path Integrals can be regarded as an equivalent theoretical representation for regularizing the divergence in the normal Quantum Field Theory. We also derive the equation of motion for scalar, vector and spinor particles propagate in fractal paths and discuss the corresponding gauge symmetry, where we find a special non-local gauge transformation.
Semantically Enhanced Global Reasoning for Semantic Segmentation

Recent advances in pixel-level tasks (e.g., segmentation) illustrate the beneﬁt of long-range interactions between aggregated region-based representations that can enhance local features. However, such pixel-to-region associations and the resulting representation, which often take the form of attention, cannot model the underlying semantic struc-ture of the scene (e.g., individual objects and, by exten-sion, their interactions). In this work, we take a step to-ward addressing this limitation. Speciﬁcally, we propose an architecture where we learn to project image features into latent region representations and perform global reasoning across them, using a transformer, to produce contextualized and scene-consistent representations that are then fused with original pixel-level features. Our design enables the latent regions to represent semantically meaningful concepts, by ensuring that activated regions are spatially disjoint and unions of such regions correspond to connected object segments. The resulting semantic global reasoning (SGR) is end-to-end trainable and can be combined with any semantic segmentation framework and backbone. Com-bining SGR with DeepLabV3 results in a semantic segmentation performance that is competitive to the state-of-the-art, while resulting in more semantically interpretable and diverse region representations, which we show can effectively transfer to detection and instance segmentation. Further, we propose a new metric that allows us to measure the semantics of representations at both the object class and instance level.
Semiparametric Distribution Regression with Instruments and Monotonicity

This paper proposes IV-based estimators for the semiparametric distribution regression model in the presence of an endogenous regressor, which are based on an extension of IV probit estimators. We discuss the causal interpretation of the estimators and two methods (monotone rearrangement and isotonic regression) to ensure a monotonically increasing distribution function. Asymptotic properties and simulation evidence are provided. An application to wage equations reveals statistically significant and heterogeneous differences to the inconsistent OLS-based estimator.
Improving trajectory localization accuracy via direction-of-arrival derivative estimation

Sound source localization is crucial in acoustic sensing and monitoring-related applications. In this paper, we do a comprehensive analysis of improvement in sound source localization by combining the direction of arrivals (DOAs) with their derivatives which quantify the changes in the positions of sources over time. This study uses the SALSA-Lite feature with a convolutional recurrent neural network (CRNN) model for predicting DOAs and their first-order derivatives. An update rule is introduced to combine the predicted DOAs with the estimated derivatives to obtain the final DOAs. The experimental validation is done using TAU-NIGENS Spatial Sound Events (TNSSE) 2021 dataset. We compare the performance of the networks predicting DOAs with derivative vs. the one predicting only the DOAs at low SNR levels. The results show that combining the derivatives with the DOAs improves the localization accuracy of moving sources.
A Simple Nadaraya-Watson Head can offer Explainable and Calibrated Classification

In this paper, we empirically analyze a simple, non-learnable, and nonparametric Nadaraya-Watson (NW) prediction head that can be used with any neural network architecture. In the NW head, the prediction is a weighted average of labels from a support set. The weights are computed from distances between the query feature and support features. This is in contrast to the dominant approach of using a learnable classiﬁcation head (e.g., a fully-connected layer) on the features, which can be challenging to interpret and can yield poorly calibrated predictions. Our empirical results on an array of computer vision tasks demonstrate that the NW head can yield better calibration than its parametric counterpart, while having comparable accuracy and with minimal computational overhead. To further increase inference-time eﬃciency, we propose a simple approach that involves a clustering step run on the training set to create a relatively small distilled support set. In addition to using the weights as a means of interpreting model predictions, we further present an easy-to-compute “support inﬂuence function,” which quantiﬁes the inﬂuence of a support element on the prediction for a given query. As we demonstrate in our experiments, the inﬂuence function can allow the user to debug a trained model. We believe that the NW head is a ﬂexible, interpretable, and highly useful building block that can be used in a range of applications.
Learning Action-Effect Dynamics from Pairs of Scene-graphs

‘Actions’ play a vital role in how humans interact with the world. Thus, autonomous agents that would assist us in everyday tasks also require the capability to perform ‘Reasoning about Actions & Change’ (RAC). Recently, there has been growing interest in the study of RAC with visual and lin- guistic inputs. Graphs are often used to represent semantic structure of the visual content (i.e. objects, their attributes and relationships among objects), commonly referred to as scene-graphs. In this work, we propose a novel method that lever- ages scene-graph representation of images to reason about the effects of actions described in natural language. We experi- ment with existing CLEVR HYP (Sampat et al. 2021) dataset and show that our proposed approach is effective in terms of performance, data efﬁciency, and generalization capability compared to existing models.
Dark and bright autoionizing states in resonant high-order harmonic generation: Simulation via a one-dimensional helium model

We study the role of dark and bright autoionizing states (AIS) in photoionization and high harmonic generation (HHG) using a 1D helium model. This model allows numerical integration of the time-dependent Schr\"odinger equation beyond the singe-electron approximation completely taking into account electronic correlation. We find the level structure of the system and the spatial distribution of the electronic density for several states including AIS. Studying the HHG efficiency as a function of the detuning from the resonances with AIS we find the HHG enhancement lines. The shapes of these lines are different from the corresponding Fano lines in the photoelectronic spectra, in agreement with the experimental studies in helium. Moreover, we simulate HHG under the conditions when the fundamental frequency is close to the even-order multiphoton resonance with the dark AIS. We find the enhanced generation of the neighbouring odd harmonics. The details of the enhancement lines for these harmonics can be understood taking into account the temporal delay between the emission of the non-resonant and resonant XUV; this delay is defined by the AIS lifetime. Finally, our simulations show that the HHG enhancement due to the dark and the bright AIS is comparable in the studied system.
Recognizing when a preference system is close to admitting a master list

A preference system $\mathcal{I}$ is an undirected graph where vertices have preferences over their neighbors, and $\mathcal{I}$ admits a master list if all preferences can be derived from a single ordering over all vertices. We study the problem of deciding whether a given preference system $\mathcal{I}$ is close to admitting a master list based on three different distance measures. We determine the computational complexity of the following questions: can $\mathcal{I}$ be modified by (i) $k$ swaps in the preferences, (ii) $k$ edge deletions, or (iii) $k$ vertex deletions so that the resulting instance admits a master list? We investigate these problems in detail from the viewpoint of parameterized complexity and of approximation. We also present two applications related to stable and popular matchings.
MobileTL: On-device Transfer Learning with Inverted Residual Blocks

Transfer learning on edge is challenging due to on-device limited resources. Existing work addresses this issue by training a subset of parameters or adding model patches. Developed with inference in mind, Inverted Residual Blocks (IRBs) split a convolutional layer into depthwise and pointwise convolutions, leading to more stacking layers, e.g., convolution, normalization, and activation layers. Though they are efficient for inference, IRBs require that additional activation maps are stored in memory for training weights for convolution layers and scales for normalization layers. As a result, their high memory cost prohibits training IRBs on resource-limited edge devices, and making them unsuitable in the context of transfer learning. To address this issue, we present MobileTL, a memory and computationally efficient on-device transfer learning method for models built with IRBs. MobileTL trains the shifts for internal normalization layers to avoid storing activation maps for the backward pass. Also, MobileTL approximates the backward computation of the activation layer (e.g., Hard-Swish and ReLU6) as a signed function which enables storing a binary mask instead of activation maps for the backward pass. MobileTL fine-tunes a few top blocks (close to output) rather than propagating the gradient through the whole network to reduce the computation cost. Our method reduces memory usage by 46% and 53% for MobileNetV2 and V3 IRBs, respectively. For MobileNetV3, we observe a 36% reduction in floating-point operations (FLOPs) when fine-tuning 5 blocks, while only incurring a 0.6% accuracy reduction on CIFAR10. Extensive experiments on multiple datasets demonstrate that our method is Pareto-optimal (best accuracy under given hardware constraints) compared to prior work in transfer learning for edge devices.
Bayesian Forecasting in the 21st Century: A Modern Review

The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem – model, parameters, latent states – is factored into the forecast distribution, with forecasts conditioned only on what is known or observed. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large, or complex. The current state of play in Bayesian forecasting is the subject of this review. The aim is to provide readers with an overview of modern approaches to the field, set in some historical context. Whilst our primary focus is on applications in the fields of economics and finance, and their allied disciplines, sufficient general details about implementation are provided to aid and inform all investigators.
Transfer Reactions in Nuclear Astrophysics

Transfer reactions are important tool in nuclear astrophysics. These reactions allow us to identify states in nuclei and to find the corresponding energies, to determine if these states can contribute to astrophysical nuclear reactions and ultimately to determine the strength of that contribution. In this paper,the basic details of how transfer reactions may be used in nuclear astrophysics are set out along with some common pitfalls to avoid.
Solving the Side-Chain Packing Arrangement of Proteins from Reinforcement Learned Stochastic Decision Making

Protein structure prediction is a fundamental problem in computational molecular biology. Classical algorithms such as ab-initio or threading as well as many learning methods have been proposed to solve this challenging problem. However, most reinforcement learning methods tend to model the state-action pairs as discrete objects. In this paper, we develop a reinforcement learning (RL) framework in a continuous setting and based on a stochastic parametrized Hamiltonian version of the Pontryagin maximum principle (PMP) to solve the side-chain packing and protein-folding problem. For special cases our formulation can be reduced to previous work where the optimal folding trajectories are trained using an explicit use of Langevin dynamics. Optimal continuous stochastic Hamiltonian dynamics folding pathways can be derived with use of different models of molecular energetics and force fields. In our RL implementation we adopt a soft actor-critic methodology however we can replace this other RL training based on A2C, A3C or PPO.
EFFICACITE DES ALGORITHMES GENETIQUES PROCEDANT PAR REDUCTION SUCCESSIVE POUR L'IDENTIFICATION DES PARAMETRES

Cet article presente un algorithme d’identification des parametres, base sur les algorithmes genetiques et qui procede par reduction successive du domaine de recherche des differents parametres a identifier. Pour pouvoir etudier la convergence de cet algorithme, quel que soit le domaine de recherche initial, nous avons utilise plusieurs intervalles initiaux et deux types de mutation : la mutation uniforme et la mutation gaussienne. L’approche proposee est testee sur un systeme non lineaire modelise par des equations differentielles non lineaires representant le systeme de Monod. Les resultats obtenus confirment le fait que la reduction des intervalles ameliore la convergence des algorithmes genetiques.
Finding the Right Curve: Optimal Design of Constant Function Market Makers

Constant Function Market Makers (CFMMs) are a tool for creating exchange markets, have been deployed effectively in prediction markets, and are now especially prominent in the Decentralized Finance ecosystem. We show that for any set of beliefs about future asset prices, an optimal CFMM trading function exists that maximizes the fraction of trades that a CFMM can settle. We formulate a convex program to compute this optimal trading function. This program, therefore, gives a tractable framework for market-makers to compile their belief function on the future prices of the underlying assets into the trading function of a maximally capital-efficient CFMM. Our convex optimization framework further extends to capture the tradeoffs between fee revenue, arbitrage loss, and opportunity costs of liquidity providers. Analyzing the program shows how the consideration of profit and loss leads to a qualitatively different optimal trading function. Our model additionally explains the diversity of CFMM designs that appear in practice. We show that careful analysis of our convex program enables inference of a market-maker's beliefs about future asset prices, and show that these beliefs mirror the folklore intuition for several widely used CFMMs. Developing the program requires a new notion of the liquidity of a CFMM, and the core technical challenge is in the analysis of the KKT conditions of an optimization over an infinite-dimensional Banach space.
UAVs for Industries and Supply Chain Management

This work aims at showing that it is feasible and safe to use a swarm of Unmanned Aerial Vehicles (UAVs) indoors alongside humans. UAVs are increasingly being integrated under the Industry 4.0 framework. UAV swarms are primarily deployed outdoors in civil and military applications, but the opportunities for using them in manufacturing and supply chain management are immense. There is extensive research on UAV technology, e.g., localization, control, and computer vision, but less research on the practical application of UAVs in industry. UAV technology could improve data collection and monitoring, enhance decision-making in an Internet of Things framework and automate time-consuming and redundant tasks in the industry. However, there is a gap between the technological developments of UAVs and their integration into the supply chain. Therefore, this work focuses on automating the task of transporting packages utilizing a swarm of small UAVs operating alongside humans. MoCap system, ROS, and unity are used for localization, inter-process communication and visualization. Multiple experiments are performed with the UAVs in wander and swarm mode in a warehouse like environment.
Modelling Correlated Bernoulli Data Part II: Inference

Binary data are highly common in many applications, however it is usually modelled with the assumption that the data are independently and identically distributed. This is typically not the case in many real-world examples and such the probability of a success can be dependent on the outcome successes of past events. The de Bruijn process (DBP) was introduced in Kimpton et al. [2022]. This is a correlated Bernoulli process which can be used to model binary data with known correlation. The correlation structures are included through the use of de Bruijn graphs, giving an extension to Markov chains. Given the DBP and an observed sequence of binary data, we present a method of inference using Bayes’ factors. Results are applied to the Oxford and Cambridge annual boat race.
Improve Bilingual TTS Using Dynamic Language and Phonology Embedding

In most cases, bilingual TTS needs to handle three types of input scripts: first language only, second language only, and second language embedded in the first language. In the latter two situations, the pronunciation and intonation of the second language are usually quite different due to the influence of the first language. Therefore, it is a big challenge to accurately model the pronunciation and intonation of the second language in different contexts without mutual interference. This paper builds a Mandarin-English TTS system to acquire more standard spoken English speech from a monolingual Chinese speaker. We introduce phonology embedding to capture the English differences between different phonology. Embedding mask is applied to language embedding for distinguishing information between different languages and to phonology embedding for focusing on English expression. We specially design an embedding strength modulator to capture the dynamic strength of language and phonology. Experiments show that our approach can produce significantly more natural and standard spoken English speech of the monolingual Chinese speaker. From analysis, we find that suitable phonology control contributes to better performance in different scenarios.
Predictive densities for multivariate normal models based on extended models and shrinkage Bayes methods

We investigate predictive densities for multivariate normal models with unknown mean vectors and known covariance matrices. Bayesian predictive densities based on shrinkage priors often have complex representations, although they are eﬀective in various problems. We consider extended normal models with mean vectors and covariance matrices as parameters, and adopt predictive densities that belong to the extended models including the original normal model. We adopt predictive densities that are optimal with respect to the posterior Bayes risk in the extended models. The proposed predictive density based on a superharmonic shrinkage prior is shown to dominate the Bayesian predictive density based on the uniform prior under a loss function based on the Kullback–Leibler divergence. Our method provides an alternative to the empirical Bayes method, which is widely used to construct tractable predictive densities.