Miracle-dz/UniFAR-FTUs huggingface dataset:
$CP$ violation studies in three-body charmless $B^\pm$ decays and contributions to the LHCb SciFi Tracker

In the first part of the thesis we present studies on three body charmless B± decays. The analysis is performed using LHCb dataset from protonproton collisions at the centre-of-mass energy of 7 TeV and 8 TeV collected in 2011 and 2012, respectively, corresponding to an integrated luminosity of 3.1 fb−1. We measured the inclusive CP asymmetry of the four channels: B± → K±π+π−, B± → K±K+K−, B± → π±π+π− and B± → π±K+K−. CP asymmetries were also studied along the phase space. The second part of this thesis is devoted to my contribution to the LHCb SciFi Tracker, a detector made of scintillating fibres. It presents the experimental setups used to characterise the scintillating fibres which needs to meet specific requirements to be able to operate under the running conditions foreseen for the LHCb upgrade. In addition, we discuss the results on the development of fibres made of a new class of scintillating material denominated as Nanostructured Organosilicon Luminophores (NOL). They were found to have a decay time constant at least two times shorter than the fastest scintillating fibres.
On the reach of isometric embeddings into Wasserstein type spaces

We study the reach (in the sense of Federer) of the natural isometric embedding $X\hookrightarrow W_p(X)$ of $X$ inside its $p$-Wasserstein space, where $(X,\operatorname{dist})$ is a geodesic metric space. We prove that if a point $x\in X$ can be joined to another point $y\in X$ by two minimizing geodesics, then $\operatorname{reach}(x, X\subset W_p(X)) = 0$. This includes the cases where $X$ is a compact manifold or a non-simply connected one. On the other hand, we show that $\operatorname{reach}(X\subset W_p(X)) = \infty$ when $X$ is a CAT(0) space. The infinite reach enables us to examine the regularity of the projection map. Furthermore, we replicate these findings by considering the isometric embedding $X\hookrightarrow W_\vartheta(X)$ into an Orlicz--Wasserstein space, a generalization by Sturm of the classical Wasserstein space. Lastly, we establish the nullity of the reach for the isometric embedding of $X$ into $\operatorname{Dgm}_\infty$, the space of persistence diagrams equipped with the bottleneck distance.
The Complexity of Satisfiability Checking for Symbolic Finite Automata

We study the satisfiability problem of symbolic finite automata and decompose it into the satisfiability problem of the theory of the input characters and the monadic second-order theory of the indices of accepted words. We use our decomposition to obtain tight computational complexity bounds on the decision problem for this automata class and an extension that considers linear arithmetic constraints on the underlying effective Boolean algebra.
Bluefish: A Relational Framework for Graphic Representations

Complex graphic representations -- such as annotated visualizations, molecular structure diagrams, or Euclidean geometry -- convey information through overlapping perceptual relations. To author such representations, users are forced to use rigid, purpose-built tools with limited flexibility and expressiveness. User interface (UI) frameworks provide only limited relief as their tree-based models are a poor fit for expressing overlaps. We present Bluefish, a diagramming framework that extends UI architectures to support overlapping perceptual relations. Bluefish graphics are instantiated as relational scenegraphs: hierarchical data structures augmented with adjacency relations. Authors specify these relations with scoped references to components found elsewhere in the scenegraph. For layout, Bluefish lazily materializes necessary coordinate transformations. We demonstrate that Bluefish enables authoring graphic representations across a diverse range of domains while preserving the compositional and abstractional affordances of traditional UI frameworks. Moreover, we show how relational scenegraphs capture previously latent semantics that can later be retargeted (e.g., for screen reader accessibility).
On Finding Constrained Independent Sets in Cycles

A subset of $[n] = \{1,2,\ldots,n\}$ is called stable if it forms an independent set in the cycle on the vertex set $[n]$. In 1978, Schrijver proved via a topological argument that for all integers $n$ and $k$ with $n \geq 2k$, the family of stable $k$-subsets of $[n]$ cannot be covered by $n-2k+1$ intersecting families. We study two total search problems whose totality relies on this result. In the first problem, denoted by $\mathsf{Schrijve}r(n,k,m)$, we are given an access to a coloring of the stable $k$-subsets of $[n]$ with $m = m(n,k)$ colors, where $m \leq n-2k+1$, and the goal is to find a pair of disjoint subsets that are assigned the same color. While for $m = n-2k+1$ the problem is known to be $\mathsf{PPA}$-complete, we prove that for $m<d \cdot \lfloor \frac{n}{2k+d-2} \rfloor$, with $d$ being any fixed constant, the problem admits an efficient algorithm. For $m = \lfloor n/2 \rfloor-2k+1$, we prove that the problem is efficiently reducible to the $\mathsf{Kneser}$ problem. Motivated by the relation between the problems, we investigate the family of unstable $k$-subsets of $[n]$, which might be of independent interest. In the second problem, called Unfair Independent Set in Cycle, we are given $\ell$ subsets $V_1, \ldots, V_\ell$ of $[n]$, where $\ell \leq n-2k+1$ and $|V_i| \geq 2$ for all $i \in [\ell]$, and the goal is to find a stable $k$-subset $S$ of $[n]$ satisfying the constraints $|S \cap V_i| \leq |V_i|/2$ for $i \in [\ell]$. We prove that the problem is $\mathsf{PPA}$-complete and that its restriction to instances with $n=3k$ is at least as hard as the Cycle plus Triangles problem, for which no efficient algorithm is known. On the contrary, we prove that there exists a constant $c$ for which the restriction of the problem to instances with $n \geq c \cdot k$ can be solved in polynomial time.
Statistical ensembles and logarithmic corrections to black hole entropy

In this paper, we consider general statistical ensembles and compute logarithmic corrections to the microcanonical entropy resulting due to thermodynamic fluctuations which are controlled by the boundary conditions, i.e. due to choice of ensemble. The framework is applied to the case of non-extremal black holes to give certain logarithmic corrections to the Bekenstein–Hawking entropy. We argue that within the framework of black hole chemistry, where the cosmological constant is identified with bulk pressure, the isoenthalpic-isobaric entropy rather than microcanonical entropy carries a more natural and consistent thermodynamic interpretation as black hole entropy. Logarithmic corrections to both microcanonical and isoenthalpic-isobaric entropies of black holes are computed, and we show that the latter set of corrections in black hole chemistry are of the same form as corrections to the microcanonical entropy in theories where the cosmological constant is not interpreted as a thermodynamic pressure. Finally, we compute logarithmic corrections to entropy in the framework of holographic black hole chemistry. We emphasize upon the choice of statistical ensemble, both in the bulk and on the boundary, in order to have a consistent comparison between them. The corrections studied in this paper are distinct from those obtained from Euclidean quantum gravity.
Utilizing a Capture-Recapture Strategy to Accelerate Infectious Disease Surveillance (preprint)

Monitoring key elements of disease dynamics (e.g., prevalence, case counts) is of great importance in infectious disease prevention and control, as emphasized during the COVID-19 pandemic. To facilitate this effort, we propose a new capture-recapture (CRC) analysis strategy that takes misclassification into account from easily-administered, imperfect diagnostic test kits, such as the Rapid Antigen Test-kits or saliva tests. Our method is based on a recently proposed "anchor stream" design, whereby an existing voluntary surveillance data stream is augmented by a smaller and judiciously drawn random sample. It incorporates manufacturer-specified sensitivity and specificity parameters to account for imperfect diagnostic results in one or both data streams. For inference to accompany case count estimation, we improve upon traditional Wald-type confidence intervals by developing an adapted Bayesian credible interval for the CRC estimator that yields favorable frequentist coverage properties. When feasible, the proposed design and analytic strategy provides a more efficient solution than traditional CRC methods or random sampling-based biased-corrected estimation to monitor disease prevalence while accounting for misclassification. We demonstrate the benefits of this approach through simulation studies that underscore its potential utility in practice for economical disease monitoring among a registered closed population.
Meta-training with Demonstration Retrieval for Efficient Few-shot Learning

Large language models show impressive results on few-shot NLP tasks. However, these models are memory and computation-intensive. Meta-training allows one to leverage smaller models for few-shot generalization in a domain-general and task-agnostic manner; however, these methods alone results in models that may not have sufficient parameterization or knowledge to adapt quickly to a large variety of tasks. To overcome this issue, we propose meta-training with demonstration retrieval, where we use a dense passage retriever to retrieve semantically similar labeled demonstrations to each example for more varied supervision. By separating external knowledge from model parameters, we can use meta-training to train parameter-efficient models that generalize well on a larger variety of tasks. We construct a meta-training set from UnifiedQA and CrossFit, and propose a demonstration bank based on UnifiedQA tasks. To our knowledge, our work is the first to combine retrieval with meta-training, to use DPR models to retrieve demonstrations, and to leverage demonstrations from many tasks simultaneously, rather than randomly sampling demonstrations from the training set of the target task. Our approach outperforms a variety of targeted parameter-efficient and retrieval-augmented few-shot methods on QA, NLI, and text classification tasks (including SQuAD, QNLI, and TREC). Our approach can be meta-trained and fine-tuned quickly on a single GPU.
Why do CNNs excel at feature extraction? A mathematical explanation

Over the past decade deep learning has revolutionized the field of computer vision, with convolutional neural network models proving to be very effective for image classification benchmarks. However, a fundamental theoretical questions remain answered: why can they solve discrete image classification tasks that involve feature extraction? We address this question in this paper by introducing a novel mathematical model for image classification, based on feature extraction, that can be used to generate images resembling real-world datasets. We show that convolutional neural network classifiers can solve these image classification tasks with zero error. In our proof, we construct piecewise linear functions that detect the presence of features, and show that they can be realized by a convolutional network.
Asymptotic properties of maximum likelihood estimator for determinantal point processes

We obtain the almost sure consistency and the Berry-Esseen type bound of the maximum likelihood estimator for determinantal point processes (DPPs), completing and extending previous work initiated in Brunel, Moitra, Rigollet, and Urschel [BMRU17]. We also give explicit formula and a detailed discussion for the maximum likelihood estimator for blocked determinantal matrix of two by two submatrices and compare it with the frequency method.
Long-Tailed Continual Learning For Visual Food Recognition

Deep learning based food recognition has achieved remarkable progress in predicting food types given an eating occasion image. However, there are two major obstacles that hinder deployment in real world scenario. First, as new foods appear sequentially overtime, a trained model needs to learn the new classes continuously without causing catastrophic forgetting for already learned knowledge of existing food types. Second, the distribution of food images in real life is usually long-tailed as a small number of popular food types are consumed more frequently than others, which can vary in different populations. This requires the food recognition method to learn from class-imbalanced data by improving the generalization ability on instance-rare food classes. In this work, we focus on long-tailed continual learning and aim to address both aforementioned challenges. As existing long-tailed food image datasets only consider healthy people population, we introduce two new benchmark food image datasets, VFN-INSULIN and VFN-T2D, which exhibits on the real world food consumption for insulin takers and individuals with type 2 diabetes without taking insulin, respectively. We propose a novel end-to-end framework for long-tailed continual learning, which effectively addresses the catastrophic forgetting by applying an additional predictor for knowledge distillation to avoid misalignment of representation during continual learning. We also introduce a novel data augmentation technique by integrating class-activation-map (CAM) and CutMix, which significantly improves the generalization ability for instance-rare food classes to address the class-imbalance issue. The proposed method show promising performance with large margin improvements compared with existing methods.
DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum

In the rapidly evolving landscape of medical imaging diagnostics, achieving high accuracy while preserving computational efficiency remains a formidable challenge. This work presents \texttt{DeepMediX}, a groundbreaking, resource-efficient model that significantly addresses this challenge. Built on top of the MobileNetV2 architecture, DeepMediX excels in classifying brain MRI scans and skin cancer images, with superior performance demonstrated on both binary and multiclass skin cancer datasets. It provides a solution to labor-intensive manual processes, the need for large datasets, and complexities related to image properties. DeepMediX's design also includes the concept of Federated Learning, enabling a collaborative learning approach without compromising data privacy. This approach allows diverse healthcare institutions to benefit from shared learning experiences without the necessity of direct data access, enhancing the model's predictive power while preserving the privacy and integrity of sensitive patient data. Its low computational footprint makes DeepMediX suitable for deployment on handheld devices, offering potential for real-time diagnostic support. Through rigorous testing on standard datasets, including the ISIC2018 for dermatological research, DeepMediX demonstrates exceptional diagnostic capabilities, matching the performance of existing models on almost all tasks and even outperforming them in some cases. The findings of this study underline significant implications for the development and deployment of AI-based tools in medical imaging and their integration into point-of-care settings. The source code and models generated would be released at https://github.com/kishorebabun/DeepMediX.
Quantum interference between quasi-2D Fermi surface sheets in UTe2

UTe$_2$ is a promising spin-triplet superconductor candidate for which high quality samples with long mean free paths have recently become available, thereby enabling quantum oscillation measurements to probe the Fermi surface of this material. The dimensionality of Fermi surface sections of a triplet superconductor can have important implications regarding the topological properties of the superconductivity. For example, UTe$_2$ has been proposed to possess a chiral superconducting order parameter, which could result in the formation of topologically protected Majorana surface states. Here, we report the observation of oscillatory components in the magnetoconductance of UTe$_2$ at high magnetic fields. We find that these oscillations are very well described by quantum interference between quasiparticles traversing semiclassical trajectories spanning magnetic breakdown networks. Our observations are fully consistent with a quasi-2D model of this material's Fermi surface based on prior dHvA-effect measurements. Our results indicate that UTe$_2$ -- which exhibits incredibly complex physical properties -- possesses a remarkably simple Fermi surface consisting exclusively of two quasi-2D cylindrical sections.
An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis

Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons. To address this issue, MRI synthesis is a potential solution. Recent deep learning-based methods have achieved good performance in combining multiple available sequences for missing sequence synthesis. Despite their success, these methods lack the ability to quantify the contributions of different input sequences and estimate the quality of generated images, making it hard to be practical. Hence, we propose an explainable task-specific synthesis network, which adapts weights automatically for specific sequence generation tasks and provides interpretability and reliability from two sides: (1) visualize the contribution of each input sequence in the fusion stage by a trainable task-specific weighted average module; (2) highlight the area the network tried to refine during synthesizing by a task-specific attention module. We conduct experiments on the BraTS2021 dataset of 1251 subjects, and results on arbitrary sequence synthesis indicate that the proposed method achieves better performance than the state-of-the-art methods. Our code is available at \url{https://github.com/fiy2W/mri_seq2seq}.
Investigating Possible Correlations between Gamma-Ray and Optical Lightcurves for TeV-Detected Northern Blazars over 8 Years of Observations

Blazars are a subclass of active galactic nuclei (AGN) having relativistic jets aligned within a few degrees of our line-of-sight and form the majority of the AGN detected in the TeV regime. The Fermi-Large Area Telescope (LAT) is a pair-conversion telescope, sensitive to photons having energies between 20 MeV and 2 TeV, and is capable of scanning the entire gamma-ray sky every three hours. Despite the remarkable success of the Fermi mission, many questions still remain unanswered, such as the site of gamma-ray production and the emission mechanisms involved. The Asteroid Terrestrial-impact Last Alert System (ATLAS) is a high cadence all sky survey system optimized to be efficient for finding potentially dangerous asteroids, as well as in tracking and searching for highly variable and transient sources, such as AGN. In this study, we investigate possible correlations between the Fermi-LAT observations in the 100 MeV–300 GeV energy band and the ATLAS optical data in the R-band, centered at 679 nm, for a sample of 18 TeV-detected northern blazars over 8 years of observations between 2015 and 2022. Under the assumption that the optical and gamma-ray flares are produced by the same outburst propagating down the jet, the strong correlations found for some sources suggest a single-zone leptonic model of emission.
Surgical Fine-Tuning for Grape Bunch Segmentation Under Visual Domain Shifts

Mobile robots will play a crucial role in the transition towards sustainable agriculture. To autonomously and effectively monitor the state of plants, robots ought to be equipped with visual perception capabilities that are robust to the rapid changes that characterise agricultural settings. In this paper, we focus on the challenging task of segmenting grape bunches from images collected by mobile robots in vineyards. In this context, we present the first study that applies surgical fine-tuning to instance segmentation tasks. We show how selectively tuning only specific model layers can support the adaptation of pre-trained Deep Learning models to newly-collected grape images that introduce visual domain shifts, while also substantially reducing the number of tuned parameters.
Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation

Referring video object segmentation (RVOS) aims to segment the target object in a video sequence described by a language expression. Typical multimodal Transformer based RVOS approaches process video sequence in a frame-independent manner to reduce the high computational cost, which however restricts the performance due to the lack of inter-frame interaction for temporal coherence modeling and spatio-temporal representation learning of the referred object. Besides, the absence of sufficient cross-modal interactions results in weak correlation between the visual and linguistic features, which increases the difficulty of decoding the target information and limits the performance of the model. In this paper, we propose a bidirectional correlation-driven inter-frame interaction Transformer, dubbed BIFIT, to address these issues in RVOS. Specifically, we design a lightweight and plug-and-play inter-frame interaction module in the Transformer decoder to efficiently learn the spatio-temporal features of the referred object, so as to decode the object information in the video sequence more precisely and generate more accurate segmentation results. Moreover, a bidirectional vision-language interaction module is implemented before the multimodal Transformer to enhance the correlation between the visual and linguistic features, thus facilitating the language queries to decode more precise object information from visual features and ultimately improving the segmentation performance. Extensive experimental results on four benchmarks validate the superiority of our BIFIT over state-of-the-art methods and the effectiveness of our proposed modules.
Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm

We consider minimizing a smooth function subject to a summation constraint over its variables. By exploiting a connection between the greedy 2-coordinate update for this problem and equality-constrained steepest descent in the 1-norm, we give a convergence rate for greedy selection under a proximal Polyak-Lojasiewicz assumption that is faster than random selection and independent of the problem dimension $n$. We then consider minimizing with both a summation constraint and bound constraints, as arises in the support vector machine dual problem. Existing greedy rules for this setting either guarantee trivial progress only or require $O(n^2)$ time to compute. We show that bound- and summation-constrained steepest descent in the L1-norm guarantees more progress per iteration than previous rules and can be computed in only $O(n \log n)$ time.