Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonconvex Matrix Completion with Linearly Parameterized Factors

Techniques of matrix completion aim to impute a large portion of missing entries in a data matrix through a small portion of observed ones, with broad machine learning applications including collaborative filtering, pairwise ranking, etc. In practice, additional structures are usually employed in order to improve the accuracy of matrix completion. Examples include subspace constraints formed by side information in collaborative filtering, and skew symmetry in pairwise ranking. This paper performs a unified analysis of nonconvex matrix completion with linearly parameterized factorization, which covers the aforementioned examples as special cases. Importantly, uniform upper bounds for estimation errors are established for all local minima, provided that the sampling rate satisfies certain conditions determined by the rank, condition number, and incoherence parameter of the ground-truth low rank matrix. Empirical efficiency of the proposed method is further illustrated by numerical simulations.
BiLingUNet: Image Segmentation by Modulating Top-Down and Bottom-Up Visual Processing with Referring Expressions

We present BiLingUNet, a state-of-the-art model for image segmentation using referring expressions. BiLingUNet uses language to customize visual filters and outperforms approaches that concatenate a linguistic representation to the visual input. We find that using language to modulate both bottom-up and top-down visual processing works better than just making the top-down processing language-conditional. We argue that common 1x1 language-conditional filters cannot represent relational concepts and experimentally demonstrate that wider filters work better. Our model achieves state-of-the-art performance on four referring expression datasets.
Self-similar orbit-averaged Fokker-Planck equation for isotropic spherical dense clusters (ii) physical properties and negative heat capacity of pre-collapse core

This is the second paper of a series of our works on the self-similar orbit-averaged Fokker-Planck (OAFP) equation and details physical properties of isotropic pre-collapse solution. The fundamental core collapse process at the late stage of relaxation evolution of spherical star clusters can be described by the self-similar OAFP equation. The accurate spectral solution was found recently in the first paper. The present work details the thermodynamic aspects of the model based on the stellar DF obtained from the solution. Our calculation shows the following local properties (i) Equation of state is $p=1.0\rho/\chi_\text{esc}$ in the core where $p$ is the pressure, $\rho$ the density and $\chi_\text{esc}$ the scaled escape energy, while it is $p=0.5\rho^{1.1}/\chi_\text{esc}$ at large radii. (ii) If we consider the center of the core is polytropic, the polytropic index is 177. Also, as a global property we construct caloric curves of the model to discuss the heat capacity together with Virial. Special focus is the cause of negative heat capacity of the core; the well-relaxed core can be directly compared to the isothermal sphere with positive heat capacity. Comparing our results to the previous works, we conclude, in the self-similar evolution, the negative heat capacity in the core holds due to collisionless and high-temperature stars that experience a rapid change in mean field potential through stellar- and heat- flows, rather than due to the (quasi-)isolation of the core from surroundings.
How human judgment impairs automated deception detection performance

Background: Deception detection is a prevalent problem for security practitioners. With a need for more large-scale approaches, automated methods using machine learning have gained traction. However, detection performance still implies considerable error rates. Findings from other domains suggest that hybrid human-machine integrations could offer a viable path in deception detection tasks. Method: We collected a corpus of truthful and deceptive answers about participants' autobiographical intentions (n=1640) and tested whether a combination of supervised machine learning and human judgment could improve deception detection accuracy. Human judges were presented with the outcome of the automated credibility judgment of truthful and deceptive statements. They could either fully overrule it (hybrid-overrule condition) or adjust it within a given boundary (hybrid-adjust condition). Results: The data suggest that in neither of the hybrid conditions did the human judgment add a meaningful contribution. Machine learning in isolation identified truth-tellers and liars with an overall accuracy of 69%. Human involvement through hybrid-overrule decisions brought the accuracy back to the chance level. The hybrid-adjust condition did not deception detection performance. The decision-making strategies of humans suggest that the truth bias - the tendency to assume the other is telling the truth - could explain the detrimental effect. Conclusion: The current study does not support the notion that humans can meaningfully add to the deception detection performance of a machine learning system.
MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation

Cross view feature fusion is the key to address the occlusion problem in human pose estimation. The current fusion methods need to train a separate model for every pair of cameras making them difficult to scale. In this work, we introduce MetaFuse, a pre-trained fusion model learned from a large number of cameras in the Panoptic dataset. The model can be efficiently adapted or finetuned for a new pair of cameras using a small number of labeled images. The strong adaptation power of MetaFuse is due in large part to the proposed factorization of the original fusion model into two parts—(1) a generic fusion model shared by all cameras, and (2) lightweight camera-dependent transformations. Furthermore, the generic model is learned from many cameras by a meta-learning style algorithm to maximize its adaptation capability to various camera poses. We observe in experiments that MetaFuse finetuned on the public datasets outperforms the state-of-the-arts by a large margin which validates its value in practice.
Role of subcutaneous suction drain in reducing surgical site infections after emergency laparotomy

Surgical site infection (SSI) previously termed postoperative wound infection is defined as that infection presenting up to 30 days after a surgical procedure if no prosthetic is placed and up to 1 year if a prosthetic is implanted in the patients. Surgical site infections (SSI) are still a major problem in general surgery, because they are responsible for significant discomfort for patients and excess morbidity and mortality, which also translates into a financial burden on the health system. In some reports SSI was found to be second only to urinary tract infection as the commonest hospital acquired infection and actually the most commonly encountered form of nosocomial infection in surgical patients. The risk of developing SSI is multifactorial and include the degree of microbial contamination of the operation site indicated by wound class as clean, clean contaminated, contaminated and dirty, and also by patient age, length of surgery, preoperative shaving of the operative site, hypothermia and co morbidities e.g. diabetes and obesity.
CNN-based Density Estimation and Crowd Counting: A Survey

Accurately estimating the number of objects in a single image is a challenging yet meaningful task and has been applied in many applications such as urban planning and public safety. In the various object counting tasks, crowd counting is particularly prominent due to its specific significance to social security and development. Fortunately, the development of the techniques for crowd counting can be generalized to other related fields such as vehicle counting and environment survey, if without taking their characteristics into account. Therefore, many researchers are devoting to crowd counting, and many excellent works of literature and works have spurted out. In these works, they are must be helpful for the development of crowd counting. However, the question we should consider is why they are effective for this task. Limited by the cost of time and energy, we cannot analyze all the algorithms. In this paper, we have surveyed over 220 works to comprehensively and systematically study the crowd counting models, mainly CNN-based density map estimation methods. Finally, according to the evaluation metrics, we select the top three performers on their crowd counting datasets and analyze their merits and drawbacks. Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields. We provide the density maps and prediction results of some mainstream algorithm in the validation set of NWPU dataset for comparison and testing. Meanwhile, density map generation and evaluation tools are also provided. All the codes and evaluation results are made publicly available at this https URL.
Plug-and-Play Algorithms for Large-Scale Snapshot Compressive Imaging

Snapshot compressive imaging (SCI) aims to capture the high-dimensional (usually 3D) images using a 2D sensor (detector) in a single snapshot. Though enjoying the advantages of low-bandwidth, low-power and low-cost, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging. The bottleneck lies in the reconstruction algorithms; they are either too slow (iterative optimization algorithms) or not flexible to the encoding process (deep learning based end-to-end networks). In this paper, we develop fast and flexible algorithms for SCI based on the plug-and-play (PnP) framework. In addition to the widely used PnP-ADMM method, we further propose the PnP-GAP (generalized alternating projection) algorithm with a lower computational workload and prove the {global convergence} of PnP-GAP under the SCI hardware constraints. By employing deep denoising priors, we first time show that PnP can recover a UHD color video (3840x1644x48 with PNSR above 30dB) from a snapshot 2D measurement. Extensive results on both simulation and real datasets verify the superiority of our proposed algorithm.
An Analytical Framework for mmWave-Enabled V2X Caching

Autonomous vehicles will rely heavily on vehicle-to-everything (V2X) communications to obtain a large amount of information required for navigation and road safety purposes. This can be achieved through: (i) leveraging millimeter-wave (mmWave) frequencies to achieve multi- Gbps data rates, and (ii) exploiting the temporal and spatial correlation of vehicular contents to offload a portion of the traffic from the infrastructure via caching. Characterizing such a system under mmWave directional beamforming, high vehicular mobility, channel fluctuations, and different caching strategies is a complex task. In this article, we propose the first stochastic geometry framework for caching in mmWave V2X networks, which is validated via rigorous Monte Carlo simulation. In addition to common parameters considered in stochastic geometry models, our derivations account for caching as well as the speed and the trajectory of the vehicles. Furthermore, our evaluations provide interesting design insights: (i) higher base station/vehicle densities does not necessarily improve caching performance; (ii) although using a narrower beam leads to a higher SINR, it also reduces the connectivity probability; and (iii) V2X caching can be an inexpensive way of compensating some of the unwanted mmWave channel characteristics.
Rapidity decorrelation of anisotropic flow caused by hydrodynamic fluctuations

We investigate the effect of hydrodynamic fluctuations on the rapidity decorrelations of anisotropic flow in high-energy nuclear collisions using a (3+1)-dimensional integrated dynamical model. The integrated dynamical model consists of twisted initial conditions, fluctuating hydrodynamics, and hadronic cascades on an event-by-event basis. To understand the rapidity decorrelation, we analyze the factorization ratio in the longitudinal direction. Comparing the factorization ratios between fluctuating hydrodynamics and ordinary viscous hydrodynamics, we find a sizable effect of hydrodynamic fluctuations on rapidity decorrelations. We also propose to calculate the Legendre coefficients of the flow magnitude and the event-plane angle to understand the decorrelation of anisotropic flow in the longitudinal direction.
NLTE for APOGEE: simultaneous multi-element NLTE radiative transfer

Context. Relaxing the assumption of local thermodynamic equilibrium (LTE) in modelling stellar spectra is a necessary step to determine chemical abundances to better than about 10% in late-type stars. Aims. We describe our multi-element (Na, Mg, K, and Ca) non-LTE (NLTE) calculations, which can be applied to the APOGEE survey. Methods. The new version of TLUSTY allows for the calculation of restricted NLTE in cool stars using pre-calculated opacity tables. We demonstrate that TLUSTY gives consistent results with MULTI, a well-tested code for NLTE in cool stars. We used TLUSTY to perform LTE and a series of NLTE calculations that simultaneously used all combinations of one, two, three and four of the elements in NLTE. Results. We take into account that departures from LTE in one element can affect others through changes in the opacities of Na, Mg, K, and Ca. We find that atomic Mg, which provides strong UV opacity and exhibits significant departures from LTE in the low-energy states, can affect the NLTE populations of Ca, leading to abundance corrections as large as 0.07 dex. The differences in the derived abundances between the single-element and the multi-element cases can exceed those between the single-element NLTE determinations and an LTE analysis. We therefore caution that this is not always a second-order effect. Based on detailed tests for three stars with reliable atmospheric parameters (Arcturus, Procyon, and the Sun), we conclude that our NLTE calculations provide abundance corrections that can in the optical amount to 0.1, 0.2, and 0.7 dex for Ca, Na and K, but LTE is a good approximation for Mg. In the H-band, NLTE corrections are much smaller and always lower than 0.1 dex. The derived NLTE abundances in the optical and in the IR are consistent. In all three stars, NLTE line profiles fit the observations better than the LTE counterparts for all four elements. Conclusions. The atomic elements in ionisation stages where over-ionisation is an important NLTE mechanism are likely affected by departures from LTE in Mg. Particular care must be taken with the collisions that are adopted for high-lying levels when NLTE profiles of lines in the H-band are calculated. The derived NLTE corrections in the optical and in the H-band differ, but the derived NLTE abundances are consistent between the two spectral regions.
Companding algorithm for the detection of malignant lesions in HDR CT mandibular images

Compressing and expanding (companding) HDR computerized tomography (CT) images to a single LDR image is still a relevant challenge. Besides the general need for simplification of the window setting method for the purpose of diagnosis, there are specific clinical needs for the resection of malignant lesions in the mandible, for example, where the lesions may exist in both the soft tissue and the bone. A successful medical algorithm has to take into account the requirement to variously expose the same range of specific gray levels, when they are presented by different body tissues. As a solution, we propose an adaptive multi-scale contrast companding (AMCC) algorithm that is implemented by using soft threshold separation to two channels (bone and soft tissue). This separation is determined by the HU intensity values (that are already computed in the algorithm) in the specific resolution that is best fitted to the teeth. Each channel contains different set of parameters. The AMCC algorithm successfully and adaptively compands a large variety of mandibular CT HDR images as well as natural images. Two collaborating physicians who evaluated tumor boundaries, by the 'single presentation' method, reported that 92% of the algorithm output images were at least as useful for diagnosis as those in the window method, while 50% of the algorithm output images were better. When each slice was evaluated simultaneously, by the window setting method (bone and soft tissue) and algorithm output images, 93% of the evaluations declared a preference for the output images of the algorithm. We describe here a low-cost method for companding the HDR images with the ability to facilitate resections of mandibular lesions by providing optimal boundary definition.
A Framework for Online Investment Algorithms

The artificial segmentation of an investment management process into a workflow with silos of offline human operators can restrict silos from collectively and adaptively pursuing a unified optimal investment goal. To meet the investor's objectives, an online algorithm can provide an explicit incremental approach that makes sequential updates as data arrives at the process level. This is in stark contrast to offline (or batch) processes that are focused on making component level decisions prior to process level integration. Here we present and report results for an integrated, and online framework for algorithmic portfolio management. This article provides a workflow that can in-turn be embedded into a process level learning framework. The workflow can be enhanced to refine signal generation and asset-class evolution and definitions. Our results confirm that we can use our framework in conjunction with resampling methods to outperform naive market capitalisation benchmarks while making clear the extent of back-test over-fitting. We consider such an online update framework to be a crucial step towards developing intelligent portfolio selection algorithms that integrate financial theory, investor views, and data analysis with process-level learning.
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining

Pre-trained neural language models bring significant improvement for various NLP tasks, by fine-tuning the models on task-specific training sets. During fine-tuning, the parameters are initialized from pre-trained models directly, which ignores how the learning process of similar NLP tasks in different domains is correlated and mutually reinforced. In this paper, we propose an effective learning procedure named Meta Fine-Tuning (MFT), served as a meta-learner to solve a group of similar NLP tasks for neural language models. Instead of simply multi-task training over all the datasets, MFT only learns from typical instances of various domains to acquire highly transferable knowledge. It further encourages the language model to encode domain-invariant representations by optimizing a series of novel domain corruption loss functions. After MFT, the model can be fine-tuned for each domain with better parameter initializations and higher generalization ability. We implement MFT upon BERT to solve several multi-domain text mining tasks. Experimental results confirm the effectiveness of MFT and its usefulness for few-shot learning.
Fluctuation-enhanced quantum metrology.

The main obstacle for practical quantum technology is the noise, which can induce the decoherence and destroy the potential quantum advantages. The fluctuation of a field, which induces the dephasing of the system, is one of the most common noises and widely regarded as detrimental to quantum technologies. Here we show, contrary to the conventional belief, the fluctuation can be used to improve the precision limits in quantum metrology for the estimation of various parameters. Specifically, we show that for the estimation of the direction and rotating frequency of a field, the achieved precisions at the presence of the fluctuation can even surpass the highest precision achievable under the unitary dynamics which have been widely taken as the ultimate limit. We provide explicit protocols, which employs the adaptive quantum error correction, to achieve the higher precision limits with the fluctuating fields. Our study provides a completely new perspective on the role of the noises in quantum metrology. It also opens the door for higher precisions beyond the limit that has been believed to be ultimate.
Designing Network Design Spaces

In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs.
A kinematically hot population of young stars in the solar neighbourhood

In the last three decades several hundred nearby members of young stellar moving groups (MGs) have been identified, but there has been less systematic effort to quantify or characterise young stars that do not belong to previously identified MGs. Using a kinematically unbiased sample of 225 lithium-rich stars within 100 pc, we find that only $50 \pm 10$ per cent of young ($\lesssim 125$ Myr), low-mass ($0.5<M/M_{\odot}<1.0$) stars, are kinematically associated with known MGs. Whilst we find some evidence that six of the non-MG stars may be connected with the Lower Centaurus-Crux association, the rest form a kinematically "hotter" population, much more broadly dispersed in velocity, and with no obvious concentrations in space. The mass distributions of the MG members and non-MG stars is similar, but the non-MG stars may be older on average. We briefly discuss several explanations for the origin of the non-MG population.
Controllability of Network Opinion in Erdős-Rényi Graphs using Sparse Control Inputs

This paper considers a social network modeled as an Erdos Renyi random graph. Each individual in the network updates her opinion using the weighted average of the opinions of her neighbors. We explore how an external manipulative agent can drive the opinions of these individuals to a desired state with a limited additive influence on their innate opinions. We show that the manipulative agent can steer the network opinion to any arbitrary value in finite time (i.e., the system is controllable) almost surely when there is no restriction on her influence. However, when the control input is sparsity constrained, the network opinion is controllable with some probability. We lower bound this probability using the concentration properties of random vectors based on the Levy concentration function and small ball probabilities. Further, through numerical simulations, we compare the probability of controllability in Erdos Renyi graphs with that of power-law graphs to illustrate the key differences between the two models in terms of controllability. Our theoretical and numerical results shed light on how the controllability of the network opinion depends on the parameters such as the size and the connectivity of the network, and the sparsity constraints faced by the manipulative agent.