Miracle-dz/UniFAR-FTUs huggingface dataset:
Superdiffusion from Nonabelian Symmetries in Nearly Integrable Systems

The Heisenberg spin chain is a canonical integrable model. As such, it features stable ballistically propagating quasiparticles, but spin transport is subballistic at any nonzero temperature: An initially localized spin fluctuation spreads in time t to a width t2/3. This exponent as well as the functional form of the dynamical spin correlation function suggest that spin transport is in the Kardar–Parisi–Zhang (KPZ) universality class. However, the full counting statistics of magnetization is manifestly incompatible with KPZ scaling. A simple two-mode hydrodynamic description, derivable from microscopic principles, captures both the KPZ scaling of the correlation function and the coarse features of the full counting statistics, but remains to be numerically validated. These results generalize to any integrable spin chain invariant under a continuous nonabelian symmetry and are surprisingly robust against moderately strong integrability-breaking perturbations that respect the nonabelian symmetry. Expected final online publication date for the Annual Review of Condensed Matter Physics, Volume 15 is March 2024. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
"Virtual source" methods for diusive fields

The phrase virtual source refers to two different techniques in this dissertation. In the first half of the dissertation, it refers to interferometry and in the second half it uses the concept of the synthetic aperture technique. In interferometry, a receiver is converted to a “virtual source”. In the synthetic aperture technique, a large “virtual source” is constructed by adding small sources. Interferometry has been popularly used in the seismology community since the beginning of this century. The wave propagation between two receivers can be reconstructed by cross correlating the wave fields excited by uncorrelated sources on a closed surface surrounding two receivers, providing the medium is enclosed by sources. Cross-correlation interferometry is also proven to be applicable to diffusive fields. In this thesis, I pursue the application of interferometry to low frequency diffusive electromagnetic fields used in Controlled Source Electromagnetics (CSEM). For such diffusive fields, interferometry requires a volume distribution of sources to reconstruct the diffusion response between receivers. In practice, finding the optimal distribution of sources for interferometry is critical because there are only a finite number of sources. I study the required source distribution in crosscorrelation interferometry for both wave propagation and diffusion. Although it is possible to apply interferometry to diffusive fields using a finite number of sources, sources close to the receivers have the largest contribution in the reconstruction. Therefore, there is no obvious advantage to apply interferometry to diffusive fields. I also investigate the feasibility of applying multi-dimensional-deconvolution interferometry to low frequency diffusive electromagnetic fields. I show that the imprint of reservoirs significantly increases after applying multi-dimensional-deconvolution interferometry
Double Descent of Discrepancy: A Task-, Data-, and Model-Agnostic Phenomenon

In this paper, we studied two identically-trained neural networks (i.e. networks with the same architecture, trained on the same dataset using the same algorithm, but with different initialization) and found that their outputs discrepancy on the training dataset exhibits a"double descent"phenomenon. We demonstrated through extensive experiments across various tasks, datasets, and network architectures that this phenomenon is prevalent. Leveraging this phenomenon, we proposed a new early stopping criterion and developed a new method for data quality assessment. Our results show that a phenomenon-driven approach can benefit deep learning research both in theoretical understanding and practical applications.
An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization

Finite-difference methods are a class of algorithms designed to solve black-box optimization problems by approximating a gradient of the target function on a set of directions. In black-box optimization, the non-smooth setting is particularly relevant since, in practice, differentiability and smoothness assumptions cannot be verified. To cope with nonsmoothness, several authors use a smooth approximation of the target function and show that finite difference methods approximate its gradient. Recently, it has been proved that imposing a structure in the directions allows improving performance. However, only the smooth setting was considered. To close this gap, we introduce and analyze O-ZD, the first structured finite-difference algorithm for non-smooth black-box optimization. Our method exploits a smooth approximation of the target function and we prove that it approximates its gradient on a subset of random {\em orthogonal} directions. We analyze the convergence of O-ZD under different assumptions. For non-smooth convex functions, we obtain the optimal complexity. In the non-smooth non-convex setting, we characterize the number of iterations needed to bound the expected norm of the smoothed gradient. For smooth functions, our analysis recovers existing results for structured zeroth-order methods for the convex case and extends them to the non-convex setting. We conclude with numerical simulations where assumptions are satisfied, observing that our algorithm has very good practical performances.
Give Me More Details: Improving Fact-Checking with Latent Retrieval

Evidence plays a crucial role in automated fact-checking. When verifying real-world claims, existing fact-checking systems either assume the evidence sentences are given or use the search snippets returned by the search engine. Such methods ignore the challenges of collecting evidence and may not provide sufficient information to verify real-world claims. Aiming at building a better fact-checking system, we propose to incorporate full text from source documents as evidence and introduce two enriched datasets. The first one is a multilingual dataset, while the second one is monolingual (English). We further develop a latent variable model to jointly extract evidence sentences from documents and perform claim verification. Experiments indicate that including source documents can provide sufficient contextual clues even when gold evidence sentences are not annotated. The proposed system is able to achieve significant improvements upon best-reported models under different settings.
Proper Scoring and Sufficiency

Logarithmic score and information divergence appear in both information theory, statistics, statistical mechanics, and portfolio theory. We demonstrate that all these topics involve some kind of optimization that leads directly to the use of Bregman divergences. If a sufficiency condition is also fulfilled the Bregman divergence must be proportional to information divergence. The sufficiency condition has quite different consequences in the different areas of application, and often it is not fulfilled. Therefore the sufficiency condition can be used to explain when results from one area can be transferred directly from one area to another and when one will experience differences.
Markov Decision Process with an External Temporal Process

Most reinforcement learning algorithms treat the context under which they operate as a stationary, isolated and undisturbed environment. However, in the real world, the environment is constantly changing due to a variety of external influences. To address this problem, we study Markov Decision Processes (MDP) under the influence of an external temporal process. We formalize this notion and discuss conditions under which the problem becomes tractable with suitable solutions. We propose a policy iteration algorithm to solve this problem and theoretically analyze its performance.
On practical naturalness and its implications for weak scale supersymmetry

We revisit the various measures of naturalness for models of weak scale supersymmetry including 1. electroweak (EW) naturalness, 2. naturalness via sensitivity to high scale parameters (EENZ/BG), 3. sensitivity of Higgs soft term due to high scale (HS) radiative corrections and 4. stringy naturalness (SN) from the landscape. The EW measure is most conservative and seems unavoidable; it is also model independent in that its value is fixed only by the weak scale spectra which ensues, no matter which model is used to generate it. The EENZ/BG measure is ambiguous depending on which ``parameters of ignorance'' one includes in the low energy effective field theory (LE-EFT). For models with calculable soft breaking terms, then the EENZ/BG measure reduces to the tree-level EW measure. The HS measure began life as a figurative expression and probably shouldn't be taken more seriously than that. SN is closely related to EW naturalness via the atomic principle, although it is also sensitive to the distribution of soft terms on the landscape. If the landscape favors large soft terms, as in a power law distribution, then it favors m(h) ~ 125 GeV along with sparticles beyond present LHC reach. In this context, SN appears as a probability measure where more natural models are expected to be more prevalent on the landscape than finetuned models. We evaluate by how much the different measures vary against one another with an eye to determining by how much they may overestimate finetuning; we find overestimates can range up to a factor of over 1000. In contrast to much of the literature, we expect the string landscape to favor EW natural SUSY models over finetuned models so that the landscape is not an alternative to naturalness.
ORIGIN AND EVOLUTION OF X-RAY BINARIES AND BINARY RADIO PULSARS

a companion star. Optical obser­ vations have shown that there are two main classes of such X-ray binaries: the high-mass X-ray binaries, in which the mass donor is an 0 or B star, and the low-mass X-ray binaries, which have donors less massive than the Sun. It has become clear during the past decade that the neutron star in an X-ray binary may switch on as a radio pulsar when the mass transfer stops. Accordingly, two classes of radio pulsars in binaries can be delin­ eated. Those in which the radio pulsar has another neutron star or a relatively massive (�IM 0) white dwarf as a companion probably have evolved from a high-mass X-ray binary. Those in which the neutron star is accompanied by a low-mass white dwarf (;5 0.5M 0) may have evolved from low-mass X-ray binaries. The origin and evolution of these X-ray binaries and binary radio pulsars are the subject of this review. Origin of high-mass and oflow-mass X-ray binaries and their evolution into binary radio pulsars are discussed in Section 3 and in Section 4. In order not to have to interrupt our discussion of the evolutionary scenarios with the technical explanations of the various processes occurring in them, we start with a separate discussion of these
How many samples are needed to leverage smoothness?

A core principle in statistical learning is that smoothness of target functions allows to break the curse of dimensionality. However, learning a smooth function seems to require enough samples close to one another to get meaningful estimate of high-order derivatives, which would be hard in machine learning problems where the ratio between number of data and input dimension is relatively small. By deriving new lower bounds on the generalization error, this paper formalizes such an intuition, before investigating the role of constants and transitory regimes which are usually not depicted beyond classical learning theory statements while they play a dominant role in practice.
MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation

Efficient utilisation of both intra- and extra-textual context remains one of the critical gaps between machine and human translation. Existing research has primarily focused on providing individual, well-defined types of context in translation, such as the surrounding text or discrete external variables like the speaker's gender. This work introduces MTCue, a novel neural machine translation (NMT) framework that interprets all context (including discrete variables) as text. MTCue learns an abstract representation of context, enabling transferability across different data settings and leveraging similar attributes in low-resource scenarios. With a focus on a dialogue domain with access to document and metadata context, we extensively evaluate MTCue in four language pairs in both translation directions. Our framework demonstrates significant improvements in translation quality over a parameter-matched non-contextual baseline, as measured by BLEU (+0.88) and Comet (+1.58). Moreover, MTCue significantly outperforms a"tagging"baseline at translating English text. Analysis reveals that the context encoder of MTCue learns a representation space that organises context based on specific attributes, such as formality, enabling effective zero-shot control. Pre-training on context embeddings also improves MTCue's few-shot performance compared to the"tagging"baseline. Finally, an ablation study conducted on model components and contextual variables further supports the robustness of MTCue for context-based NMT.
Representation Online Matters: Practical End-to-End Diversification in Search and Recommender Systems

As the use of online platforms continues to grow across all demographics, users often express a desire to feel represented in the content. To improve representation in search results and recommendations, we introduce end-to-end diversification, ensuring that diverse content flows throughout the various stages of these systems, from retrieval to ranking. We develop, experiment, and deploy scalable diversification mechanisms in multiple production surfaces on the Pinterest platform, including Search, Related Products, and New User Homefeed, to improve the representation of different skin tones in beauty and fashion content. Diversification in production systems includes three components: identifying requests that will trigger diversification, ensuring diverse content is retrieved from the large content corpus during the retrieval stage, and finally, balancing the diversity-utility trade-off in a self-adjusting manner in the ranking stage. Our approaches, which evolved from using Strong-OR logical operator to bucketized retrieval at the retrieval stage and from greedy re-rankers to multi-objective optimization using determinantal point processes for the ranking stage, balances diversity and utility while enabling fast iterations and scalable expansion to diversification over multiple dimensions. Our experiments indicate that these approaches significantly improve diversity metrics, with a neutral to a positive impact on utility metrics and improved user satisfaction, both qualitatively and quantitatively, in production.
A new Mertens decomposition of $\mathscr{Y}^{g,\xi}$-submartingale systems. Application to BSDEs with weak constraints at stopping times

We first introduce the concept of $\mathscr{Y}^{g,\xi}$-submartingale systems, where the nonlinear operator $\mathscr{Y}^{g,\xi}$ corresponds to the first component of the solution of a reflected BSDE with generator $g$ and lower obstacle $\xi$. We first show that, in the case of a left-limited right-continuous obstacle, any $\mathscr{Y}^{g,\xi}$-submartingale system can be aggregated by a process which is right-lower semicontinuous. We then prove a \textit{Mertens decomposition}, by using an original approach which does not make use of the standard penalization technique. These results are in particular useful for the treatment of control/stopping game problems and, to the best of our knowledge, they are completely new in the literature. As an application, we introduce a new class of \textit{Backward Stochastic Differential Equations (in short BSDEs) with weak constraints at stopping times}, which are related to the partial hedging of American options. We study the wellposedness of such equations and, using the $\mathscr{Y}^{g,\xi}$-Mertens decomposition, we show that the family of minimal time-$t$-values $Y_t$, with $(Y,Z)$ a supersolution of the BSDE with weak constraints, admits a representation in terms of a reflected backward stochastic differential equation.
Identifying physics beyond SMEFT in the angular distribution of $\Lambda_b\rightarrow \Lambda_c(\rightarrow\Lambda\pi)\tau\bar\nu_\tau$ decay

Indirect searches of physics beyond the Standard Model (BSM) may be performed using the tool of effective field theory (EFT). In the Standard Model Effective Field Theory (SMEFT), the $SU(2)_L\times U(1)_Y$ symmetry of the Standard Model is linearly realized. However, it is possible that more general EFTs such as the Higgs Effective Field Theory (HEFT) are needed to describe the data. We explore the effects of scalar and vector new-physics operators that contribute to the charged-current processes $b\rightarrow c \tau\nu_\tau$. The angular distribution of $\Lambda_b\rightarrow \Lambda_c(\rightarrow\Lambda\pi)\tau\bar\nu_\tau$ decay is sensitive to the 6-dimensional effective operator $O_V^{LR}\equiv(\bar{\tau}\gamma^\mu P_L\nu_\tau)(\bar{c}\gamma_\mu P_R b)$, which is present in HEFT but suppressed in SMEFT. We identify the angular observables that can have significant contribution from $O_V^{LR}$, and hence would be useful for probing not only BSM physics but also physics beyond SMEFT.
Image Enhancement by Local Operators

Medical image is never the correct representation of the object under observation; it is always ruined by degradations during acquisition and within the imaging system itself. These include noise, distortion and smudge. Enhancement of such corrupted images is an important and challenging issue in medical image processing. These local operations on medical imaging have their own assumptions, advantages and disadvantages. This paper has been discussed and applied the local operators for the removal of noise from the medical images.
The Atmospheric Dynamics of Pulsar Companions

Pulsars emit radiation over an extremely wide frequency range, from radio through gamma. Recently, systems in which this radiation significantly alters the atmospheres of low-mass pulsar companions have been discovered. These systems, ranging from ones with highly anisotropic heating to those with transient X-ray emissions, represent an exciting opportunity to investigate pulsars through the changes they induce in their companions. In this work, we present both analytic and numerical work investigating these phenomena, with a particular focus on atmospheric heat transport, transient phenomena, and the possibility of deep heating via gamma rays. We find that certain classes of binary systems may explain decadal-timescale X-ray transient phenomena, as well as the formation of so-called redback companion systems. We also posit an explanation for the formation of high-eccentricity millisecond pulsars with white dwarf companions. In addition, we examine the temperature anisotropy induced by the Pulsar in its companion, and demonstrate that this may be used to infer properties of both the companion and the Pulsar wind. Finally, we explore the possibility of spontaneously generated banded winds in rapidly rotating convecting objects.
The efficacy and safety of Capivasertib (AZD5363) in the treatment of patients with solid tumor: a systematic review and meta-analysis of randomized clinical trials

ABSTRACT Objective To evaluate the clinical efficacy and safety of Capivasertib on patients with solid tumors. Methods Data from four RCTs were pooled to create a systematic review and meta-analysis focusing on Capivasertib-treated patients with solid tumor. Progression-free survival (PFS) and adverse events (AE) were the primary outcomes. Results A total of 540 individuals from four RCTs were included. The analysis showed that Capivasertib improved PFS for the ITT population with an HR of 0.75 (95% CI = 0.62–0.90, p = 0.002), whereas it did not show improvement in PFS of the PI3K/AKT/PTEN-altered group with an HR = 0.61 (95% CI = 0.32–1.16, p = 0.13). The analysis also showed that Capivasertib improved OS for the ITT population with an HR = 0.61 (95% CI = 0.47–0.78, p = 0.0001). For safety, four studies were included; statistical differences between Capivasertib and placebo were found in discontinuation of Capivasertib due to toxicity or AE (RR = 2.37, 95% CI = 1.37–4.10, p = 0.002). Conclusion Capivasertib plus chemotherapy or hormonal therapy combination has shown promising antitumor efficacy and promising safety profile in the treatment of individuals with solid tumor.
Systematic Development of a Simple Human Gait Index

Human gait analysis aims to assess gait mechanics and to identify the deviations from “normal” gait patterns by using meaningful parameters extracted from gait data. As each parameter indicates different gait characteristics, a proper combination of key parameters is required to perform an overall gait assessment. Therefore, in this study, we introduced a simple gait index derived from the most important gait parameters (walking speed, maximum knee flexion angle, stride length, and stance-swing phase ratio) to quantify overall gait quality. We performed a systematic review to select the parameters and analyzed a gait dataset (120 healthy subjects) to develop the index and to determine the healthy range (0.50 – 0.67). To validate the parameter selection and to justify the defined index range, we applied a support vector machine algorithm to classify the dataset based on the selected parameters and achieved a high classification accuracy (∼95%). Also, we explored other published datasets that are in good agreement with the proposed index prediction, reinforcing the reliability and effectiveness of the developed gait index. The gait index can be used as a reference for preliminary assessment of human gait conditions and to quickly identify abnormal gait patterns and possible relation to health issues.
Age‐dependent single‐species population dynamics with delayed argument

The model of age‐dependent population dynamics was for the first time described by McKendrick (1926). This model was based on the first‐order partial differential equation with the standard initial condition and the non‐local boundary condition in integral form. Gurtin and MacCamy in their paper (1974) analyzed a more general model, where the progress of the population depends on its number. They proved the existence of the unique solution to their model for all time. In our paper the results of Gurtin and MacCamy will be generalized on the case, when the dependence on a number of a population is delayed. Copyright © 2009 John Wiley & Sons, Ltd.