Miracle-dz/UniFAR-FTUs huggingface dataset:
MIRI MRS Observations of Beta Pictoris I. The Inner Dust, the Planet, and the Gas

We present JWST MIRI Medium Resolution Spectrograph (MRS) observations of the $\beta$ Pictoris system. We detect an infrared excess from the central unresolved point source from 5 to 7.5 $\mu$m which is indicative of dust within the inner $\sim$7 au of the system. We perform PSF subtraction on the MRS data cubes and detect a spatially resolved dust population emitting at 5 $\mu$m. This spatially resolved hot dust population is best explained if the dust grains are in the small grain limit (2$\pi$a$\ll$$\lambda$). The combination of unresolved and resolved dust at 5 $\mu$m could suggest that dust grains are being produced in the inner few au of the system and are then radiatively driven outwards, where the particles could accrete onto the known planets in the system $\beta$ Pic b and c. We also report the detection of an emission line at 6.986 $\mu$m that we attribute to be [Ar II]. We find that the [Ar II] emission is spatially resolved with JWST and appears to be aligned with the dust disk. Through PSF subtraction techniques, we detect $\beta$ Pic b at the 5$\sigma$ level in our MRS data cubes and present the first mid-IR spectrum of the planet from 5 to 7 $\mu$m. The planet's spectrum is consistent with having absorption from water vapor between 5 and 6.5 $\mu$m. We perform atmosphere model grid fitting on spectra and photometry of $\beta$ Pic b and find that the planet's atmosphere likely has a sub-stellar C/O ratio.
Territorial Unbalances in Quality of Life. A focus on Italian Inner and Rural Areas

The Italian National Strategy for Inner Areas explicitly draws policymakers’ attention to inner municipalities. It stresses the importance of improving socio-economic conditions of people as the only way to reverse negative demographic trends in those areas. To this respect, improving quality of life (QoL) represents one of the key drivers. Given such an important policy implication, this work provides a statistical tool to measure existing gaps in QoL levels across Italian NUTS 3 regions, by explicitly disentangling urban and inner areas. Nevertheless, QoL is a multidimensional concept, thus a composite indicator is computed following a non-compensatory approach: the QoL Mazziotta-Pareto Index. Firstly, we consider the variability of the comprehensive indicator across Italy, with respect to the presence of inner areas. As a major result, this analysis seems breaking down the supposed negative relationship between QoL and presence of inner areas, which the paper proves to be mostly overlapping with rural ones, when controlling for sub-national structural divides occurring throughout Italy. Secondly, spatial aspects make the picture even more complex. Even the neighbouring space is expected to affect QoL at local level. In particular, by means of both global and local indicators of spatial autocorrelation, groups of NUTS 3 regions sharing similar QoL levels with their neighbours are detected. From a policy perspective, such a locked-in path among neighbouring regions can influence the effectiveness of place-based policies.
WetSpongeCake: a Surface Appearance Model Considering Porosity and Saturation

Wet powdered materials, such as wet ground or moist walls, are common in the real world. Despite their particle size being larger than the wavelength, they remain invisible from a macro view. Reproducing these appearances accurately is crucial for various applications. Existing methods use different approaches, such as Monte Carlo path tracing on implicit shapes, which is accurate but computationally expensive. Another approach involves modeling powdered materials with a medium using the radiative transfer equation, but these methods are computationally intensive and lack intuitive parameters. Some works represent porosity with cylinder-shaped holes on surfaces, but they have limitations. In this paper, we propose a practical BSDF model called WetSpongeCake for wet powdered materials. This model includes controllable physical parameters to faithfully reproduce real-world appearances while remaining computationally efficient. We reformulate Monte Carlo light transport on implicit shapes into a medium, utilizing an equivalent phase function for light transport within ellipsoid-shaped particles and a modified RTE for porosity and saturation effects. Our novel WetSpongeCake BSDF integrates this medium into the SpongeCake framework, allowing representation of various wet powdered material appearances, including both reflection and transmission. We showcase our model with examples, such as wet paper, sand saturated with different liquids, and sculptures made of multiple particles.
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series

In this study, we introduce a Fourier series-based trainable filter for computed tomography (CT) reconstruction within the filtered backprojection (FBP) framework. This method overcomes the limitation in noise reduction, inherent in conventional FBP methods, by optimizing Fourier series coefficients to construct the filter. This method enables robust performance across different resolution scales and maintains computational efficiency with minimal increment for the trainable parameters compared to other deep learning frameworks. Additionally, we propose Gaussian edge-enhanced (GEE) loss function that prioritizes the $L_1$ norm of high-frequency magnitudes, effectively countering the blurring problems prevalent in mean squared error (MSE) approaches. The model's foundation in the FBP algorithm ensures excellent interpretability, as it relies on a data-driven filter with all other parameters derived through rigorous mathematical procedures. Designed as a plug-and-play solution, our Fourier series-based filter can be easily integrated into existing CT reconstruction models, making it a versatile tool for a wide range of practical applications. Our research presents a robust and scalable method that expands the utility of FBP in both medical and scientific imaging.
A Single-Mode Quasi Riemannian Gradient Descent Algorithm for Low-Rank Tensor Recovery

This paper focuses on recovering a low-rank tensor from its incomplete measurements. We propose a novel algorithm termed the Single Mode Quasi Riemannian Gradient Descent (SM-QRGD). By exploiting the benefits of both fixed-rank matrix tangent space projection in Riemannian gradient descent and sequentially truncated high-order singular value decomposition (ST-HOSVD), SM-QRGD achieves a much faster convergence speed than existing state-of-the-art algorithms. Theoretically, we establish the convergence of SM-QRGD through the Tensor Restricted Isometry Property (TRIP) and the geometry of the fixed-rank matrix manifold. Numerically, extensive experiments are conducted, affirming the accuracy and efficacy of the proposed algorithm.
Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms

We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets. Such RECs allow its members to exchange production surplus through an internal market. The objective is to control their assets in order to minimise the sum of individual electricity bills. These bills account for the electricity exchanged through the REC and with the retailers. Typically, for large companies, another important part of the bills are the costs related to the power peaks; in our framework, they are determined from the energy exchanges with the retailers. We compare rule-based control strategies with the two following control algorithms. The first one is derived from model predictive control techniques, and the second one is built with reinforcement learning techniques. We also compare variants of these algorithms that neglect the peak power costs. Results confirm that using policies accounting for the power peaks lead to a significantly lower sum of electricity bills and thus better control strategies at the cost of higher computation time. Furthermore, policies trained with reinforcement learning approaches appear promising for real-time control of the communities, where model predictive control policies may be computationally expensive in practice. These findings encourage pursuing the efforts toward development of scalable control algorithms, operating from a centralised standpoint, for renewable energy communities equipped with controllable assets.
Simulation of a Rohksar-Kivelson ladder on a NISQ device

We present a quantum-classical algorithm to study the dynamics of the Rohksar-Kivelson plaquette ladder on NISQ devices. We show that complexity is largely reduced using gauge invariance, additional symmetries, and a crucial property associated to how plaquettes are blocked against ring-exchange in the ladder geometry. This allows for an efficient simulation of sizable plaquette ladders with a small number of qubits, well suited for the capabilities of present NISQ devices. We illustrate the procedure for ladders with simulation of up to $8$ plaquettes in an IBM-Q machine, employing scaled quantum gates.
Squares, ultrafilters and forcing axioms

We study relationships between various set theoretic compactness principles, focusing on the interplay between the three families of combinatorial objects or principles mentioned in the title. Specifically, we show the following. (1) Strong forcing axioms, in general incompatible with the existence of indexed squares, can be made compatible with weaker versions of indexed squares. (2) Indexed squares and indecomposable ultrafilters with suitable parameters can coexist. As a consequence, the amount of stationary reflection known to be implied by the existence of a uniform indecomposable ultrafilter is optimal. (3) The Proper Forcing Axiom implies that any cardinal carrying a uniform indecomposable ultrafilter is either measurable or a supremum of countably many measurable cardinals. Leveraging insights from the preceding sections, we demonstrate that the conclusion cannot be improved.
Survey on Digital Watermarking Techniques

This paper presents a literature survey on Digital Watermarking within an image. It describes the early work carried out on digital watermarks, including the brief analysis of various watermarking schemes and its applications. This paper also makes a comparison between various watermarking schemes . This paper also gives us a brief introduction about the procedure of digital watermarking
Data-Free Generalized Zero-Shot Learning

Deep learning models have the ability to extract rich knowledge from large-scale datasets. However, the sharing of data has become increasingly challenging due to concerns regarding data copyright and privacy. Consequently, this hampers the effective transfer of knowledge from existing data to novel downstream tasks and concepts. Zero-shot learning (ZSL) approaches aim to recognize new classes by transferring semantic knowledge learned from base classes. However, traditional generative ZSL methods often require access to real images from base classes and rely on manually annotated attributes, which presents challenges in terms of data restrictions and model scalability. To this end, this paper tackles a challenging and practical problem dubbed as data-free zero-shot learning (DFZSL), where only the CLIP-based base classes data pre-trained classifier is available for zero-shot classification. Specifically, we propose a generic framework for DFZSL, which consists of three main components. Firstly, to recover the virtual features of the base data, we model the CLIP features of base class images as samples from a von Mises-Fisher (vMF) distribution based on the pre-trained classifier. Secondly, we leverage the text features of CLIP as low-cost semantic information and propose a feature-language prompt tuning (FLPT) method to further align the virtual image features and textual features. Thirdly, we train a conditional generative model using the well-aligned virtual image features and corresponding semantic text features, enabling the generation of new classes features and achieve better zero-shot generalization. Our framework has been evaluated on five commonly used benchmarks for generalized ZSL, as well as 11 benchmarks for the base-to-new ZSL. The results demonstrate the superiority and effectiveness of our approach. Our code is available in https://github.com/ylong4/DFZSL.
Scalable Reinforcement Learning for Linear-Quadratic Control of Networks

Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give near-optimal performance. More specifically, we consider networked linear-quadratic controllers with decoupled costs and spatially exponentially decaying dynamics. We aim to exploit the structure in the problem to design a scalable reinforcement learning algorithm for learning a distributed controller. Recent work has shown that the optimal controller can be well approximated only using information from a $\kappa$-neighborhood of each agent. Motivated by these results, we show that similar results hold for the agents' individual value and Q-functions. We continue by designing an algorithm, based on the actor-critic framework, to learn distributed controllers only using local information. Specifically, the Q-function is estimated by modifying the Least Squares Temporal Difference for Q-functions method to only use local information. The algorithm then updates the policy using gradient descent. Finally, we evaluate the algorithm through simulations that indeed suggest near-optimal performance.
Condensation mass sink and intensification of tropical storms

Intensification of tropical storms measured as the central pressure tendency represents a subtle imbalance, of the order of $10^{-3}$, between the inflow and outflow of air in the storm core. Factors driving this imbalance, especially in cases of rapid intensification, remain elusive. Here, using an analysis of intensification rates and precipitation in North Atlantic cyclones, it is shown that the storms on average deepen at a rate with which maximum local precipitation removes mass from the atmospheric column. Means for lifetime maximum intensification rate and maximum concurrent precipitation (multiplied by the acceleration of gravity) are, respectively, $23$ and $17$ hPa day$^{-1}$. This equivalence is not limited to average values: both intensification rates and precipitation have the same dependence on the inverse radius of maximum wind. It is further shown using a numerical model that with the mass sink switched off, storms driven by sensible and latent heat alone either do not develop at all or develop significantly more slowly reaching lower maximum intensities. It is discussed that the conclusions of previous studies about the relative insignificance of the mass sink arose from a long-standing misinterpretation of mass nonconservation assessments for assesments of the actual impact of the mass sink on storm dynamics. Condensation mass sink provides for a fundamental positive feedback between surface pressure and vertical velocity that was earlier shown to be instrumental in analytical descriptions of storm intensification. This feedback allows the storm circulation to get more compact during intensification in contrast to modeled heat-driven storms that increase their radius of maximum wind as they intensify. These findings indicate that the condensation mass sink is a dominant process governing the dynamics of tropical storms.
Completeness of uniformly discrete translates in $L^p(\mathbb{R})$

We construct a real sequence $\{\lambda_n\}_{n=1}^{\infty}$ satisfying $\lambda_n = n + o(1)$, and a Schwartz function $f$ on $\mathbb{R}$, such that for any $N$ the system of translates $\{f(x - \lambda_n)\}$, $n>N$, is complete in the space $L^p(\mathbb{R})$ for every $p>1$. The same system is also complete in a wider class of Banach function spaces on $\mathbb{R}$.
Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing

We address the Individualized continuous treatment effect (ICTE) estimation problem where we predict the effect of any continuous valued treatment on an individual using ob- servational data. The main challenge in this estimation task is the potential confounding of treatment assignment with in- dividual’s covariates in the training data, whereas during in- ference ICTE requires prediction on independently sampled treatments. In contrast to prior work that relied on regularizers or unstable GAN training, we advocate the direct approach of augmenting training individuals with independently sam- pled treatments and inferred counterfactual outcomes. We in- fer counterfactual outcomes using a two-pronged strategy: a Gradient Interpolation for close-to-observed treatments, and a Gaussian Process based Kernel Smoothing which allows us to down weigh high variance inferences. We evaluate our method on five benchmarks and show that our method out- performs six state-of-the-art methods on the counterfactual estimation error. We analyze the superior performance of our method by showing that (1) our inferred counterfactual re- sponses are more accurate, and (2) adding them to the train- ing data reduces the distributional distance between the con- founded training distribution and test distribution where treat- ment is independent of covariates. Our proposed method is model-agnostic and we show that it improves ICTE accuracy of several existing models.
Knowledge-Aware Neuron Interpretation for Scene Classification

Although neural models have achieved remarkable performance, they still encounter doubts due to the intransparency. To this end, model prediction explanation is attracting more and more attentions. However, current methods rarely incorporate external knowledge and still suffer from three limitations: (1) Neglecting concept completeness. Merely selecting concepts may not sufficient for prediction. (2) Lacking concept fusion. Failure to merge semantically-equivalent concepts. (3) Difficult in manipulating model behavior. Lack of verification for explanation on original model. To address these issues, we propose a novel knowledge-aware neuron interpretation framework to explain model predictions for image scene classification. Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts. Our method, incorporating complete concepts, effectively provides better prediction explanations compared to baselines. Furthermore, for concept fusion, we introduce a knowledge graph-based method known as Concept Filtering, which produces over 23% point gain on neuron behaviors for neuron interpretation. At last, we propose Model Manipulation, which aims to study whether the core concepts based on ConceptNet could be employed to manipulate model behavior. The results show that core concepts can effectively improve the performance of original model by over 26%.
APIGen: Generative API Method Recommendation

Automatic API method recommendation is an essential task of code intelligence, which aims to suggest suitable APIs for programming queries. Existing approaches can be categorized into two primary groups: retrieval-based and learning-based approaches. Although these approaches have achieved remarkable success, they still come with notable limitations. The retrieval-based approaches rely on the text representation capabilities of embedding models, while the learning-based approaches require extensive task-specific labeled data for training. To mitigate the limitations, we propose APIGen, a generative API recommendation approach through enhanced in-context learning (ICL). APIGen involves two main components: (1) Diverse Examples Selection. APIGen searches for similar posts to the programming queries from the lexical, syntactical, and semantic perspectives, providing more informative examples for ICL. (2) Guided API Recommendation. APIGen enables large language models (LLMs) to perform reasoning before generating API recommendations, where the reasoning involves fine-grained matching between the task intent behind the queries and the factual knowledge of the APIs. With the reasoning process, APIGen makes recommended APIs better meet the programming requirement of queries and also enhances the interpretability of results. We compare APIGen with four existing approaches on two publicly available benchmarks. Experiments show that APIGen outperforms the best baseline CLEAR by 105.8% in method-level API recommendation and 54.3% in class-level API recommendation in terms of SuccessRate@1. Besides, APIGen achieves an average 49.87% increase compared to the zero-shot performance of popular LLMs such as GPT-4 in method-level API recommendation regarding the SuccessRate@3 metric.
Randomized query composition and product distributions

Let R_eps denote randomized query complexity for error probability eps, and R:=R_{1/3}. In this work we investigate whether a perfect composition theorem R(f o g^n)=Omega(R(f). R(g)) holds for a relation f in {0,1}^n * S and a total inner function g:{0,1}^m \to {0, 1}. Let D^(prod) denote the maximum distributional query complexity with respect to any product (over variables) distribution. In this work we show the composition theorem R(f o g^n)=Omega(R(f). D^{prod}(g)) up to logarithmic factors. In light of the minimax theorem which states that R(g) is the maximum distributional complexity of g over any distribution, our result makes progress towards answering the composition question. We prove our result by means of a complexity measure R^(prod)_(eps) that we define for total Boolean functions. We show it to be equivalent (up to logarithmic factors) to the sabotage complexity measure RS() defined by Ben-David and Kothari (ICALP 2019): RS(g) = Theta(R^(prod)_(1/3)(g)) (up to log factors). We ask if our bound RS(g) = Omega(D^(prod)(g)) (up to log factors) is tight. We answer this question in the negative, by showing that for the NAND tree function, sabotage complexity is polynomially larger than D^(prod). Our proof yields an alternative and different derivation of the tight lower bound on the bounded error randomized query complexity of the NAND tree function (originally proved by Santha in 1985), which may be of independent interest. Our result gives an explicit polynomial separation between R and D^(prod) which, to our knowledge, was not known prior to our work.
DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans

Gastrointestinal (GI) tract cancers pose a global health challenge, demanding precise radiotherapy planning for optimal treatment outcomes. This paper introduces a cutting-edge approach to automate the segmentation of GI tract regions in magnetic resonance imaging (MRI) scans. Leveraging advanced deep learning architectures, the proposed model integrates Inception-V4 for initial classification, UNet++ with a VGG19 encoder for 2.5D data, and Edge UNet for grayscale data segmentation. Meticulous data preprocessing, including innovative 2.5D processing, is employed to enhance adaptability, robustness, and accuracy. This work addresses the manual and time-consuming segmentation process in current radiotherapy planning, presenting a unified model that captures intricate anatomical details. The integration of diverse architectures, each specializing in unique aspects of the segmentation task, signifies a novel and comprehensive solution. This model emerges as an efficient and accurate tool for clinicians, marking a significant advancement in the field of GI tract image segmentation for radiotherapy planning.