Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification

Person re-identification (ReID) aims to re-identify a person from non-overlapping camera views. Since person ReID data contains sensitive personal information, researchers have adopted federated learning, an emerging distributed training method, to mitigate the privacy leakage risks. However, existing studies rely on data labels that are laborious and time-consuming to obtain. We present FedUReID, a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy. FedUReID enables in-situ model training on edges with unlabeled data. A cloud server aggregates models from edges instead of centralizing raw data to preserve data privacy. Moreover, to tackle the problem that edges vary in data volumes and distributions, we personalize training in edges with joint optimization of cloud and edge. Specifically, we propose personalized epoch to reassign computation throughout training, personalized clustering to iteratively predict suitable labels for unlabeled data, and personalized update to adapt the server aggregated model to each edge. Extensive experiments on eight person ReID datasets demonstrate that FedUReID not only achieves higher accuracy but also reduces computation cost by 29%. Our FedUReID system with the joint optimization will shed light on implementing federated learning to more multimedia tasks without data labels.
Cyclic cellular automata and Greenberg–Hastings models on regular trees

We study the cyclic cellular automaton (CCA) and the Greenberg-Hastings model (GHM) with $\kappa\ge 3$ colors and contact threshold $\theta\ge 2$ on the infinite $(d+1)$-regular tree, $T_d$. When the initial state has the uniform product distribution, we show that these dynamical systems exhibit at least two distinct phases. For sufficiently large $d$, we show that if $\kappa(\theta-1) \le d - O(\sqrt{d\kappa \ln(d)})$, then every vertex almost surely changes its color infinitely often, while if $\kappa\theta \ge d + O(\kappa\sqrt{d\ln(d)})$, then every vertex almost surely changes its color only finitely many times. Roughly, this implies that as $d\to \infty$, there is a phase transition where $\kappa\theta/d = 1$. For the GHM dynamics, in the scenario where every vertex changes color finitely many times, we moreover give an exponential tail bound for the distribution of the time of the last color change at a given vertex.
Mixing by stirring: Optimizing shapes and strategies

The mixing of binary ﬂuids by stirrers is a commonplace procedure in many industrial and natural settings, and mixing eﬃciency directly translates into more homogeneous ﬁnal products, more enriched compounds, and often substantial economic savings in energy and input ingredients. Enhancements in mixing eﬃciency can be accomplished by unorthodox stirring protocols as well as modiﬁed stirrer shapes that utilize unsteady hydrodynamics and vortex-shedding features to instigate the formation of ﬂuid ﬁlaments which ultimately succumb to diﬀusion and produce a homogeneous mixture. We propose a PDE-constrained optimization approach to address the problem of mixing enhancement for binary ﬂuids. Within a gradient-based framework, we target the stirring strategy as well as the cross-sectional shape of the stirrers to achieve improved mixedness over a given time horizon and within a prescribed energy budget. The optimization produces a signiﬁcant enhancement in homogeneity in the initially separated ﬂuids, suggesting promising modiﬁcations to traditional stirring protocols.
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency

The goal of No-Reference Image Quality Assessment (NR-IQA) is to estimate the perceptual image quality in accordance with subjective evaluations, it is a complex and unsolved problem due to the absence of the pristine reference image. In this paper, we propose a novel model to address the NR-IQA task by leveraging a hybrid approach that benefits from Convolutional Neural Networks (CNNs) and self-attention mechanism in Transformers to extract both local and non-local features from the input image. We capture local structure information of the image via CNNs, then to circumvent the locality bias among the extracted CNNs features and obtain a non-local representation of the image, we utilize Transformers on the extracted features where we model them as a sequential input to the Transformer model. Furthermore, to improve the monotonicity correlation between the subjective and objective scores, we utilize the relative distance information among the images within each batch and enforce the relative ranking among them. Last but not least, we observe that the performance of NR-IQA models degrades when we apply equivariant transformations (e.g. horizontal flipping) to the inputs. Therefore, we propose a method that leverages self-consistency as a source of self-supervision to improve the robustness of NR-IQA models. Specifically, we enforce self-consistency between the outputs of our quality assessment model for each image and its transformation (horizontally flipped) to utilize the rich self-supervisory information and reduce the uncertainty of the model. To demonstrate the effectiveness of our work, we evaluate it on seven standard IQA datasets (both synthetic and authentic) and show that our model achieves state-of-the-art results on various datasets.1
Circuit complexity in Proca theory

In this paper, we study circuit complexity in Proca theory with Nielsen’s approach and Fubini-Study (FS) metric approach. We place the fields on a lattice to gain a regularized theory, and obtain the ground state by adopting proper coordinates. We calculate complexities of the ground and thermofield double (TFD) states with Nielsen’s approach, complexity of the TFD state is found to grows like a logarithmic function. We quantize the Proca fields and give the approximate ground state and TFD state by acting unitary circuit operators on the associated reference states. The circuit lengths are calculated with FS metric, the minimal lengths are given according to the associated geometric spaces. The complexity of TFD state is found to grows linearly with time.
Rare decays of the positronium ion and molecule, 
Ps−
 
→
 
e−γ
 and 
Ps2
 
→
 
e+e−γ
, 
γγ
, 
e+e−

Decay rates of the positronium molecule $\text{Ps}_{2}$ into two photons and into an electron-positron pair are determined. Previous studies find that these rates are very different, \[ \Gamma\left(\text{Ps}_{2}\to e^{+}e^{-}\right)/\Gamma\left(\text{Ps}_{2}\to\gamma\gamma\right)\simeq250\text{ (previous studies)}. \] This is puzzling since both processes have two body final states and are of the same order in the fine structure constant. We propose a simple calculational method and test it with the well-established decay of the positronium ion into an electron and a photon. We then employ it to correct predictions for both these $\text{Ps}_{2}$ decays. We find that previous studies overestimated the $e^{+}e^{-}$ and underestimated the $\gamma\gamma$ channel by factors of about 5.44 and 3.93 respectively. Our results give $\Gamma\left(\text{Ps}_2\to e^+e^-\right)/\Gamma\left(\text{Ps}_2 \to \gamma\gamma\right) \simeq 11.7$.
Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital Pathology

Self-supervised learning (SSL) has recently shown tremendous potential to learn generic visual representations useful for many image analysis tasks. Despite their notable success, the existing SSL methods fail to generalize to downstream tasks when the number of labeled training instances is small or if the domain shift between the transfer domains is significant. In this paper, we attempt to improve self-supervised pretrained representations through the lens of curriculum learning by proposing a hardness-aware dynamic curriculum learning (HaDCL) approach. To improve the robustness and generalizability of SSL, we dynamically leverage progressive harder examples via easy-to-hard and hard-to-very-hard samples during mini-batch downstream fine-tuning. We discover that by progressive stage-wise curriculum learning, the pretrained representations are significantly enhanced and adaptable to both in-domain and out-of-domain distribution data.We performed extensive validation on three histology benchmark datasets on both patch-wise and slide-level classification problems. Our curriculum based fine-tuning yields a significant improvement over standard fine-tuning, with a minimum improvement in area-under-the-curve (AUC) score of 1.7% and 2.2% on in-domain and out-of-domain distribution data, respectively. Further, we empirically show that our approach is more generic and adaptable to any SSL methods and does not impose any additional overhead complexity. Besides, we also outline the role of patch-based versus slide-based curriculum learning in histopathology to provide practical insights into the success of curriculum based fine-tuning of SSL methods.1
A Diffusion-Map-Based Algorithm for Gradient Computation on Manifolds and Applications

We present a technique to estimate the Riemannian gradient of a given function defined on interior points of a Riemannian submanifold in the Euclidean space based on a sample of function evaluations at points in the submanifold. It applies to cases where the only available information consists of sample points evaluated on an unknown manifold. This approach is based on the estimates of the Laplace-Beltrami operator proposed in the diffusion-map theory. Analytical convergence results of the Riemannian gradient expansion are proved. The methodology provides a new algorithm to compute the gradient in cases where classical methods for numerical derivatives fail. For instance, in classification problems, and in cases where the information is provided in an unknown nonlinear lower-dimensional submanifold lying in high-dimensional spaces. The results obtained in this article connect the theory of diffusion maps with the theory of learning gradients on manifolds. We compare our methods with the results of the latter theory. We apply the Riemannian gradient estimate in a gradient-based algorithm providing a derivative-free optimization method. We test and validate several applications, including tomographic reconstruction from an unknown random angle distribution, and the sphere packing problem in dimensions 2 and 3. In the latter problem we test our methods against the PSO, Nelder-Mead, and Directgo methods.
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models

Consistency, which refers to the capability of generating the same predictions for semantically similar contexts, is a highly desirable property for a sound language understanding model. Although recent pretrained language models (PLMs) deliver outstanding performance in various downstream tasks, they should exhibit consistent behaviour provided the models truly understand language. In this paper, we propose a simple framework named consistency analysis on language understanding models (CALUM)} to evaluate the model's lower-bound consistency ability. Through experiments, we confirmed that current PLMs are prone to generate inconsistent predictions even for semantically identical inputs. We also observed that multi-task training with paraphrase identification tasks is of benefit to improve consistency, increasing the consistency by 13% on average.
Neutrino Transport with the Monte Carlo Method. II. Quantum Kinetic Equations

Neutrinos have a unique quantum feature as flavor conversions. Recent studies suggested that collective neutrino oscillations play important roles in high-energy astrophysical phenomena. The quantum kinetic equation (QKE) is capable of describing the neutrino flavor conversion, transport, and matter collision self-consistently. However, we have experienced many technical difficulties in their numerical implementation. In this paper, we present a new QKE solver based on a Monte Carlo (MC) approach. This is an upgraded version of our classical MC neutrino transport solver; in essence, a flavor degree of freedom including mixing state is added into each MC particle. This extension requires updating numerical treatments of collision terms, in particular for scattering processes. We deal with the technical problem by generating a new MC particle at each scattering event. To reduce statistical noise inherent in MC methods, we develop the effective mean free path method. This suppresses a sudden change of flavor state due to collisions without increasing the number of MC particles. We present a suite of code tests to validate these new modules with comparison to the results reported in previous studies. Our QKE-MC solver is developed with fundamentally different philosophy and design from other deterministic and mesh methods, suggesting that it will be complementary to others and potentially provide new insights into physical processes of neutrino dynamics.
Near-Optimal No-Regret Learning in General Games

We show that Optimistic Hedge – a common variant of multiplicative-weights-updates with recency bias – attains poly(log T ) regret in multi-player general-sum games. In particular, when every player of the game uses Optimistic Hedge to iteratively update her strategy in response to the history of play so far, then after T rounds of interaction, each player experiences total regret that is poly(log T ) . Our bound improves, exponentially, the O ( T 1 / 2 ) regret attainable by standard no-regret learners in games, the O ( T 1 / 4 ) regret attainable by no-regret learners with recency bias [SALS15], and the O ( T 1 / 6 ) bound that was recently shown for Optimistic Hedge in the special case of two-player games [CP20]. A corollary of our bound is that Optimistic Hedge converges to coarse correlated equilibrium in general games at a rate of ˜ O (cid:0) 1 T (cid:1) .
Mithril: Cooperative Row Hammer Protection on Commodity DRAM Leveraging Managed Refresh

Since its public introduction in the mid-2010s, the Row Hammer (RH) phenomenon has drawn significant attention from the research community due to its security implications. Although many RH-protection schemes have been proposed by processor vendors, DRAM manufacturers, and academia, they still have shortcomings. Solutions implemented in the memory controller (MC) incur increasingly higher costs due to their conservative design for the worst case in terms of the number of DRAM banks and RH threshold to support. Meanwhile, DRAM-side implementation either has a limited time margin for RH-protection measures or requires extensive modifications to the standard DRAM interface. Recently, a new command for RH-protection has been introduced in the DDR5/LPDDR5 standards, referred to as refresh management (RFM). RFM enables the separation of the tasks for RH-protection to both MC and DRAM by having the former generate an RFM command at a specific activation frequency and the latter take proper RH-protection measures within a given time window. Although promising, no existing study presents and analyzes RFM-based solutions for RH-protection. In this paper, we propose Mithril, the first RFM interface-compatible, DRAM-MC cooperative RH-protection scheme providing deterministic protection guarantees. Mithril has minimal energy overheads for common use cases without adversarial memory access patterns. We also introduce Mithril+, an optional extension to provide minimal performance overheads at the expense of a tiny modification to the MC, while utilizing existing DRAM commands.
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation

Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables. Hierarchical tables challenge numerical reasoning by complex hierarchical indexing, as well as implicit relationships of calculation and semantics. We present a new dataset, HiTab, to study question answering (QA) and natural language generation (NLG) over hierarchical tables. HiTab is a cross-domain dataset constructed from a wealth of statistical reports and Wikipedia pages, and has unique characteristics: (1) nearly all tables are hierarchical, and (2) QA pairs are not proposed by annotators from scratch, but are revised from real and meaningful sentences authored by analysts. (3) to reveal complex numerical reasoning in statistical reports, we provide fine-grained annotations of quantity and entity alignment. Experiments suggest that this HiTab presents a strong challenge for existing baselines and a valuable benchmark for future research. Targeting hierarchical structure, we devise a hierarchy-aware logical form for symbolic reasoning over tables, which shows high effectiveness. Targeting table reasoning, we leverage entity and quantity alignment to explore partially supervised training in QA and conditional generation in NLG, and largely reduce spurious predictions in QA and produce better descriptions in NLG.
Cooling-induced Vortex Decay in Keplerian Disks

Vortices are readily produced by hydrodynamical instabilities, such as the Rossby wave instability, in protoplanetary disks. However, large-scale asymmetries indicative of dust-trapping vortices are uncommon in submillimeter continuum observations. One possible explanation is that vortices have short lifetimes. In this paper, we explore how radiative cooling can lead to vortex decay. Elliptical vortices in Keplerian disks go through adiabatic heating and cooling cycles. Radiative cooling modifies these cycles and generates baroclinicity that changes the potential vorticity of the vortex. We show that the net effect is typically a spin down, or decay, of the vortex for a subadiabatic radial stratification. We perform a series of two-dimensional shearing box simulations, varying the gas cooling (or relaxation) time, t cool, and initial vortex strength. We measure the vortex decay half-life, t half, and find that it can be roughly predicted by the timescale ratio t cool/t turn, where t turn is the vortex turnaround time. Decay is slow in both the isothermal (t cool ≪ t turn) and adiabatic (t cool ≫ t turn) limits; it is fastest when t cool ∼ 0.1 t turn, where t half is as short as ∼300 orbits. At tens of astronomical units where disk rings are typically found, t turn is likely much longer than t cool, potentially placing vortices in the fast decay regime.
Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways

Well-designed data visualizations can lead to more powerful and intuitive processing by a viewer. To help a viewer intuitively compare values to quickly generate key takeaways, visualization designers can manipulate how data values are arranged in a chart to afford particular comparisons. Using simple bar charts as a case study, we empirically tested the comparison affordances of four common arrangements: vertically juxtaposed, horizontally juxtaposed, overlaid, and stacked. We asked participants to type out what patterns they perceived in a chart and we coded their takeaways into types of comparisons. In a second study, we asked data visualization design experts to predict which arrangement they would use to afford each type of comparison and found both alignments and mismatches with our findings. These results provide concrete guidelines for how both human designers and automatic chart recommendation systems can make visualizations that help viewers extract the “right” takeaway.
On singular values of data matrices with general independent columns

In this paper, we analyse singular values of a large $p\times n$ data matrix $\mathbf{X}_n= (\mathbf{x}_{n1},\ldots,\mathbf{x}_{nn})$ where the column $\mathbf{x}_{nj}$'s are independent $p$-dimensional vectors, possibly with different distributions. Such data matrices are common in high-dimensional statistics. Under a key assumption that the covariance matrices $\mathbf{\Sigma}_{nj}=\text{Cov}(\mathbf{x}_{nj})$ can be asymptotically simultaneously diagonalizable, and appropriate convergence of their spectra, we establish a limiting distribution for the singular values of $\mathbf{X}_n$ when both dimension $p$ and $n$ grow to infinity in a comparable magnitude. The matrix model goes beyond and includes many existing works on different types of sample covariance matrices, including the weighted sample covariance matrix, the Gram matrix model and the sample covariance matrix of linear times series models. Furthermore, we develop two applications of our general approach. First, we obtain the existence and uniqueness of a new limiting spectral distribution of realized covariance matrices for a multi-dimensional diffusion process with anisotropic time-varying co-volatility processes. Secondly, we derive the limiting spectral distribution for singular values of the data matrix for a recent matrix-valued auto-regressive model. Finally, for a generalized finite mixture model, the limiting spectral distribution for singular values of the data matrix is obtained.
The VMC survey – XLIII. The spatially resolved star formation history across the Large Magellanic Cloud

We derive the spatially resolved star formation history (SFH) for a 96 deg2 area across the main body of the Large Magellanic Cloud (LMC), using the near-infrared photometry from the VISTA survey of the Magellanic Clouds (VMC). The data and analyses are characterized by a great degree of homogeneity and a low sensitivity to the interstellar extinction. 756 subregions of size 0.125 deg2 – corresponding to projected sizes of about $296\times 322\, \mathrm{pc}^{2}$ in the LMC – are analysed. The resulting SFH maps, with typical resolution of 0.2–0.3 dex in logarithm of age, reveal main features in the LMC disc at different ages: the patchy star formation at recent ages, the concentration of star formation on three spiral arms and on the Bar up to ages of ∼1.6 Gyr, and the wider and smoother distribution of older populations. The period of most intense star formation occurred roughly between 4 and 0.5 Gyr ago, at rates of $\sim \! 0.3\, \mbox{$\mathrm{M}_{\odot }$}\mathrm{yr}^{-1}$. We compare young and old star formation rates with the observed numbers of RR Lyrae and Cepheids. We also derive a mean extinction and mean distance for every subregion, and the plane that best describes the spatial distribution of the mean distances. Our results cover an area about 50 per cent larger than the classical SFH maps derived from optical data. Main differences with respect to those maps are lower star formation rates at young ages, and a main peak of star formation being identified at ages slightly younger than 1 Gyr.
Statistical inference for the slope parameter in functional linear regression

In this paper we consider the linear regression model Y = SX + ε with functional regressors and responses. We develop new inference tools to quantify deviations of the true slope S from a hypothesized operator S0 with respect to the Hilbert–Schmidt norm ~S−S0~, as well as the prediction error E‖SX−S0X‖. Our analysis is applicable to functional time series and based on asymptotically pivotal statistics. This makes it particularly user friendly, because it avoids the choice of tuning parameters inherent in long-run variance estimation or bootstrap of dependent data. We also discuss two sample problems as well as change point detection. Finite sample properties are investigated by means of a simulation study. Mathematically our approach is based on a sequential version of the popular spectral cut-off estimator ŜN for S. It is well-known that the L-minimax rates in the functional regression model, both in estimation and prediction, are substantially slower than 1/ √ N (where N denotes the sample size) and that standard estimators for S do not converge weakly to non-degenerate limits. However, we demonstrate that simple plug-in estimators such as ~ŜN − S0~ for ~S − S0~ are √ N -consistent and its sequential versions satisfy weak invariance principles. These results are based on the smoothing effect of L-norms and established by a new proof-technique, the smoothness shift, which has potential applications in other statistical inverse problems.