Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Space Newton Differentiability for Solution Maps of Unilateral and Bilateral Obstacle Problems

We prove that the solution operator of the classical unilateral obstacle problem on a nonempty open bounded set $\Omega \subset \mathbb{R}^d$, $d \in \mathbb{N}$, is Newton differentiable as a function from $L^p(\Omega)$ to $H_0^1(\Omega)$ whenever $\max(1, 2d/(d+2))<p \leq \infty$. By exploiting this Newton differentiability property, results on angled subspaces in $H^{-1}(\Omega)$, and a formula for orthogonal projections onto direct sums, we further show that the solution map of the classical bilateral obstacle problem is Newton differentiable as a function from $L^p(\Omega)$ to $H_0^1(\Omega)\cap L^q(\Omega)$ whenever $\max(1, d/2)<p \leq \infty$ and $1 \leq q<\infty$. For both the unilateral and the bilateral case, we provide explicit formulas for the Newton derivative. As a concrete application example for our results, we consider the numerical solution of an optimal control problem with $H_0^1(\Omega)$-controls and box-constraints by means of a semismooth Newton method.
Unraveling anomalies in Deeply Virtual Compton Scattering

We calculate the one-loop quark box diagrams relevant to polarized and unpolarized Deeply Virtual Compton Scattering by introducing an off-forward momentum $l^\mu$ as an infrared regulator. This regularization approach allows us to reveal the poles associated with the chiral anomaly in the polarized scenario, as well as the trace anomaly in the unpolarized case. We provide an interpretation of our findings in the context of pertinent Generalized Parton Distributions (GPDs). Furthermore, we discuss the implications of these poles on the QCD factorization pertaining to Compton amplitudes.
Scalable and Configurable Tracking for Any Rowhammer Threshold

The Rowhammer vulnerability continues to get worse, with the Rowhammer Threshold (TRH) reducing from 139K activations to 4.8K activations over the last decade. Typical Rowhammer mitigations rely on tracking aggressor rows. The number of possible aggressors increases with lowering thresholds, making it difficult to reliably track such rows in a storage-efficient manner. At lower thresholds, academic trackers such as Graphene require prohibitive SRAM overheads (hundreds of KBs to MB). Recent in-DRAM trackers from industry, such as DSAC-TRR, perform approximate tracking, sacrificing guaranteed protection for reduced storage overheads, leaving DRAM vulnerable to Rowhammer attacks. Ideally, we seek a scalable tracker that tracks securely and precisely, and incurs negligible dedicated SRAM and performance overheads, while still being able to track arbitrarily low thresholds. To that end, we propose START - a Scalable Tracker for Any Rowhammer Threshold. Rather than relying on dedicated SRAM structures, START dynamically repurposes a small fraction the Last-Level Cache (LLC) to store tracking metadata. START is based on the observation that while the memory contains millions of rows, typical workloads touch only a small subset of rows within a refresh period of 64ms, so allocating tracking entries on demand significantly reduces storage. If the application does not access many rows in memory, START does not reserve any LLC capacity. Otherwise, START dynamically uses 1-way, 2-way, or 8-way of the cache set based on demand. START consumes, on average, 9.4% of the LLC capacity to store metadata, which is 5X lower compared to dedicating a counter in LLC for each row in memory. We also propose START-M, a memory-mapped START for large-memory systems. Our designs require only 4KB SRAM for newly added structures and perform within 1% of idealized tracking even at TRH of less than 100.
PronounFlow: A Hybrid Approach for Calibrating Pronouns in Sentences

Flip through any book or listen to any song lyrics, and you will come across pronouns that, in certain cases, can hinder meaning comprehension, especially for machines. As the role of having cognitive machines becomes pervasive in our lives, numerous systems have been developed to resolve pronouns under various challenges. Commensurate with this, it is believed that having systems able to disambiguate pronouns in sentences will help towards the endowment of machines with commonsense and reasoning abilities like those found in humans. However, one problem these systems face with modern English is the lack of gender pronouns, where people try to alternate by using masculine, feminine, or plural to avoid the whole issue. Since humanity aims to the building of systems in the full-bodied sense we usually reserve for people, what happens when pronouns in written text, like plural or epicene ones, refer to unspecified entities whose gender is not necessarily known? Wouldn't that put extra barriers to existing coreference resolution systems? Towards answering those questions, through the implementation of a neural-symbolic system that utilizes the best of both worlds, we are employing PronounFlow, a system that reads any English sentence with pronouns and entities, identifies which of them are not tied to each other, and makes suggestions on which to use to avoid biases. Undertaken experiments show that PronounFlow not only alternates pronouns in sentences based on the collective human knowledge around us but also considerably helps coreference resolution systems with the pronoun disambiguation process.
Local Lipschitz Filters for Bounded-Range Functions

We study local filters for the Lipschitz property of real-valued functions $f: V \to [0,r]$, where the Lipschitz property is defined with respect to an arbitrary undirected graph $G=(V,E)$. We give nearly optimal local Lipschitz filters both with respect to $\ell_1$-distance and $\ell_0$-distance. Previous work only considered unbounded-range functions over $[n]^d$. Jha and Raskhodnikova (SICOMP `13) gave an algorithm for such functions with lookup complexity exponential in $d$, which Awasthi et al. (ACM Trans. Comput. Theory) showed was necessary in this setting. We demonstrate that important applications of local Lipschitz filters can be accomplished with filters for functions with bounded-range. For functions $f: [n]^d\to [0,r]$, we circumvent the lower bound and achieve running time $(d^r\log n)^{O(\log r)}$ for the $\ell_1$-respecting filter and $d^{O(r)}\text{polylog } n$ for the $\ell_0$-respecting filter. Our local filters provide a novel Lipschitz extension that can be implemented locally. Furthermore, we show that our algorithms have nearly optimal dependence on $r$ for the domain $\{0,1\}^d$. In addition, our lower bound resolves an open question of Awasthi et al., removing one of the conditions necessary for their lower bound for general range. We prove our lower bound via a reduction from distribution-free Lipschitz testing and a new technique for proving hardness for {\em adaptive} algorithms. We provide two applications of our local filters to arbitrary real-valued functions. In the first application, we use them in conjunction with the Laplace mechanism for differential privacy and noisy binary search to provide mechanisms for privately releasing outputs of black-box functions, even in the presence of malicious clients. In the second application, we use our local filters to obtain the first nontrivial tolerant tester for the Lipschitz property.
Finite-Block-Length Analysis in Classical and Quantum Information Theory ( Review paper )

Coding technology is used in several information processing tasks. In particular, when noise during transmission disturbs communications, coding technology is employed to protect the information. However, there are two types of coding technology: coding in classical information theory and coding in quantum information theory. Although the physical media used to transmit information ultimately obey quantum mechanics, we need to choose the type of coding depending on the kind of information device, classical or quantum, that is being used. In both branches of information theory, there are many elegant theoretical results under the ideal assumption that an infinitely large system is available. In a realistic situation, we need to account for finite size effects. The present paper reviews finite size effects in classical and quantum information theory with respect to various topics, including applied
The effects of height and BMI on prostate cancer incidence and mortality: a Mendelian randomized study in 20,848 cases and 20,214 controls from the PRACTICAL consortium, Cancer

Background Epidemiological studies suggest a potential role for obesity and determinants of adult stature in prostate cancer risk and mortality, but the relationships described in the literature are complex. To address uncertainty over the causal nature of previous observational findings, we investigated associations of heightand adiposity-related genetic variants with prostate cancer risk and mortality. Methods We conducted a case–control study based on 20,848 prostate cancers and 20,214 controls of European ancestry from 22 studies in the PRACTICAL consortium. We constructed genetic risk scores that summed each man’s number of height and BMI increasing alleles across multiple single nucleotide polymorphisms robustly associated with each phenotype from published genome-wide association studies. Results The genetic risk scores explained 6.31 and 1.46 % of the variability in height and BMI, respectively. There was only weak evidence that genetic variants previously associated with increased BMI were associated with a lower prostate cancer risk (odds ratio per standard deviation PRACTICAL consortium is provided in appendix section. Electronic supplementary material The online version of this article (doi:10.1007/s10552-015-0654-9) contains supplementary material, which is available to authorized users. & Richard M. Martin richard.martin@bristol.ac. uk Neil M. Davies neil.davies@bristol.ac. uk 1 School of Social and Community Medicine, University of Bristol, Bristol, UK 2 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK 3 School of Clinical Sciences, University of Bristol, Bristol BS10 5NB, UK 4 Nuffield Department of Surgery, University of Oxford, Oxford, UK 5 University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD, Australia 6 The Institute of Cancer Research, London SM2 5NG, UK 7 The Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK 8 Strangeways Laboratory, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Worts Causeway, Cambridge, UK 9 Institute of Population Health, University of Manchester, Manchester, UK 10 Cancer Epidemiology Centre, The Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC, Australia 11 Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia 123 Cancer Causes Control (2015) 26:1603–1616 DOI 10.1007/s10552-015-0654-9
Trust in Construction AI-Powered Collaborative Robots: A Qualitative Empirical Analysis

Construction technology researchers and forward-thinking companies are experimenting with collaborative robots (aka cobots), powered by artificial intelligence (AI), to explore various automation scenarios as part of the digital transformation of the industry. Intelligent cobots are expected to be the dominant type of robots in the future of work in construction. However, the black-box nature of AI-powered cobots and unknown technical and psychological aspects of introducing them to job sites are precursors to trust challenges. By analyzing the results of semi-structured interviews with construction practitioners using grounded theory, this paper investigates the characteristics of trustworthy AI-powered cobots in construction. The study found that while the key trust factors identified in a systematic literature review -- conducted previously by the authors -- resonated with the field experts and end users, other factors such as financial considerations and the uncertainty associated with change were also significant barriers against trusting AI-powered cobots in construction.
Propagation of Chaos for Mean Field Interacting Particle System with Multiplicative Noise

In this paper, the quantitative propagation of chaos for mean field interacting particle system with multiplicative noise in $L^2$-Wasserstein distance is derived. When the diffusion is distribution free, the quantitative propagation of chaos in relative entropy as well as total variation distance is also obtained, where the initial distribution of mean field interacting particles is allowed to be singular with that of the limit equation. Furthermore, a general result on the long time quantitative propagation of chaos in relative entropy as well as in $L^2$-Wasserstein distance is provided and is applied in (path dependent) mean field interacting particle system as well as stochastic Hamiltonian system under dissipative condition.
Generative Model for Models: Rapid DNN Customization for Diverse Tasks and Resource Constraints

Unlike cloud-based deep learning models that are often large and uniform, edge-deployed models usually demand customization for domain-specific tasks and resource-limited environments. Such customization processes can be costly and time-consuming due to the diversity of edge scenarios and the training load for each scenario. Although various approaches have been proposed for rapid resource-oriented customization and task-oriented customization respectively, achieving both of them at the same time is challenging. Drawing inspiration from the generative AI and the modular composability of neural networks, we introduce NN-Factory, an one-for-all framework to generate customized lightweight models for diverse edge scenarios. The key idea is to use a generative model to directly produce the customized models, instead of training them. The main components of NN-Factory include a modular supernet with pretrained modules that can be conditionally activated to accomplish different tasks and a generative module assembler that manipulate the modules according to task and sparsity requirements. Given an edge scenario, NN-Factory can efficiently customize a compact model specialized in the edge task while satisfying the edge resource constraints by searching for the optimal strategy to assemble the modules. Based on experiments on image classification and object detection tasks with different edge devices, NN-Factory is able to generate high-quality task- and resource-specific models within few seconds, faster than conventional model customization approaches by orders of magnitude.
CommunityFish: A Poisson-based Document Scaling With Hierarchical Clustering

Document scaling has been a key component in text-as-data applications for social scientists and a major field of interest for political researchers, who aim at uncovering differences between speakers or parties with the help of different probabilistic and non-probabilistic approaches. Yet, most of these techniques are either built upon the agnostically bag-of-word hypothesis or use prior information borrowed from external sources that might embed the results with a significant bias. If the corpus has long been considered as a collection of documents, it can also be seen as a dense network of connected words whose structure could be clustered to differentiate independent groups of words, based on their co-occurrences in documents, known as communities. This paper introduces CommunityFish as an augmented version of Wordfish based on a hierarchical clustering, namely the Louvain algorithm, on the word space to yield communities as semantic and independent n-grams emerging from the corpus and use them as an input to Wordfish method, instead of considering the word space. This strategy emphasizes the interpretability of the results, since communities have a non-overlapping structure, hence a crucial informative power in discriminating parties or speakers, in addition to allowing a faster execution of the Poisson scaling model. Aside from yielding communities, assumed to be subtopic proxies, the application of this technique outperforms the classic Wordfish model by highlighting historical developments in the U.S. State of the Union addresses and was found to replicate the prevailing political stance in Germany when using the corpus of parties' legislative manifestos.
Helping Fact-Checkers Identify Fake News Stories Shared through Images on WhatsApp

Digital news outlets have largely replaced traditional newspapers and television as the primary channels for information consumption in Brazil, and WhatsApp plays a crucial role in this context, by disseminating specific news stories. The creation of large public chat groups with numerous users and the ease of message forwarding have made the app popular among Brazilians as an affordable and immediate communication alternative. However, this has also put the platform in a central position in spreading misinformation campaigns. The platform’s closed architecture, protected by end-to-end encryption, poses a challenge for investigating and fact-checking WhatsApp content, hampering efforts to combat this problem. In this work, we explore automatic ranking-based strategies to propose a “fakeness score” model as a means to help fact-checking agencies identify fake news stories shared through images on WhatsApp. Based on the results, we design a tool and integrate it into a real system that has been used extensively for monitoring content during the 2018 Brazilian general election. Our experimental evaluation shows that this tool can reduce by up to 40% the amount of effort required to identify 80% of the fake news in the data when compared to current mechanisms practiced by the fact-checking agencies for the selection of news stories to be checked.
Optimization via conformal Hamiltonian systems on manifolds

In this work we propose a method to perform optimization on manifolds. We assume to have an objective function $f$ defined on a manifold and think of it as the potential energy of a mechanical system. By adding a momentum-dependent kinetic energy we define its Hamiltonian function, which allows us to write the corresponding Hamiltonian system. We make it conformal by introducing a dissipation term: the result is the continuous model of our scheme. We solve it via splitting methods (Lie-Trotter and leapfrog): we combine the RATTLE scheme, approximating the conserved flow, with the exact dissipated flow. The result is a conformal symplectic method for constant stepsizes. We also propose an adaptive stepsize version of it. We test it on an example, the minimization of a function defined on a sphere, and compare it with the usual gradient descent method.
Total Selfie: Generating Full-Body Selfies

We present a method to generate full-body selfies -- photos that you take of yourself, but capturing your whole body as if someone else took the photo of you from a few feet away. Our approach takes as input a pre-captured video of your body, a target pose photo, and a selfie + background pair for each location. We introduce a novel diffusion-based approach to combine all of this information into high quality, well-composed photos of you with the desired pose and background.
Audio Deepfake Detection: A Survey

Audio deepfake detection is an emerging active topic. A growing number of literatures have aimed to study deepfake detection algorithms and achieved effective performance, the problem of which is far from being solved. Although there are some review literatures, there has been no comprehensive survey that provides researchers with a systematic overview of these developments with a unified evaluation. Accordingly, in this survey paper, we first highlight the key differences across various types of deepfake audio, then outline and analyse competitions, datasets, features, classifications, and evaluation of state-of-the-art approaches. For each aspect, the basic techniques, advanced developments and major challenges are discussed. In addition, we perform a unified comparison of representative features and classifiers on ASVspoof 2021, ADD 2023 and In-the-Wild datasets for audio deepfake detection, respectively. The survey shows that future research should address the lack of large scale datasets in the wild, poor generalization of existing detection methods to unknown fake attacks, as well as interpretability of detection results.
The Accuracy of Clinical Staging of Stage I-IIIa Non-Small Cell Lung Cancer An Analysis Based on Individual Participant Data

BACKGROUND: Clinical staging of non-small cell lung cancer (NSCLC) helps determine the prognosis and treatment of patients; few data exist on the accuracy of clinical staging and the impact on treatment and survival of patients. We assessed whether participant or trial characteristics were associated with clinical staging accuracy as well as impact on survival. METHODS: We used individual participant data from randomized controlled trials (RCTs), supplied for a meta-analysis of preoperative chemotherapy ( (cid:1) radiotherapy) vs surgery alone ( (cid:1) radiotherapy) in NSCLC. We assessed agreement between clinical TNM (cTNM) stage at randomization and pathologic TNM (pTNM) stage, for participants in the control group. RESULTS: Results are based on 698 patients who received surgery alone ( (cid:1) radiotherapy) with data for cTNM and pTNM stage. Forty-six percent of cases were cTNM stage I, 23% were cTNM stage II, and 31% were cTNM stage IIIa. cTNM stage disagreed with pTNM stage in 48% of cases, with 34% clinically understaged and 14% clinically overstaged. Agreement was not associated with age ( P ¼ .12), sex ( P ¼ .62), histology ( P ¼ .82), staging method ( P ¼ .32), or year of randomization ( P ¼ .98). Poorer survival in understaged patients was explained by the underlying pTNM stage. Clinical staging failed to detect T4 disease in 10% of cases and misclassi ﬁ ed nodal disease in 38%. CONCLUSIONS: This study demonstrates suboptimal agreement between clinical and pathologic staging. Discrepancies between clinical and pathologic T and N staging could have led to different treatment decisions in 10% and 38% of cases, respectively. There is therefore a need for further research into improving staging accuracy for patients
Spatio-temporal MLP-graph network for 3D human pose estimation

Graph convolutional networks and their variants have shown significant promise in 3D human pose estimation. Despite their success, most of these methods only consider spatial correlations between body joints and do not take into account temporal correlations, thereby limiting their ability to capture relationships in the presence of occlusions and inherent ambiguity. To address this potential weakness, we propose a spatio-temporal network architecture composed of a joint-mixing multi-layer perceptron block that facilitates communication among different joints and a graph weighted Jacobi network block that enables communication among various feature channels. The major novelty of our approach lies in a new weighted Jacobi feature propagation rule obtained through graph filtering with implicit fairing. We leverage temporal information from the 2D pose sequences, and integrate weight modulation into the model to enable untangling of the feature transformations of distinct nodes. We also employ adjacency modulation with the aim of learning meaningful correlations beyond defined linkages between body joints by altering the graph topology through a learnable modulation matrix. Extensive experiments on two benchmark datasets demonstrate the effectiveness of our model, outperforming recent state-of-the-art methods for 3D human pose estimation.