Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift

A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution. However, this assumption is violated in almost all practical applications: machine learning systems are regularly tested on data that are structurally different from the training set, either due to temporal correlations, particular end users, or other factors. In this work, we consider the setting where test examples are not drawn from the training distribution. Prior work has approached this problem by attempting to be robust to all possible test time distributions, which may degrade average performance, or by "peeking" at the test examples during training, which is not always feasible. In contrast, we propose to learn models that are adaptable, such that they can adapt to distribution shift at test time using a batch of unlabeled test data points. We acquire such models by learning to adapt to training batches sampled according to different sub-distributions, which simulate structural distribution shifts that may occur at test time. We introduce the problem of adaptive risk minimization (ARM), a formalization of this setting that lends itself to meta-learning methods. Compared to a variety of methods under the paradigms of empirical risk minimization and robust optimization, our approach provides substantial empirical gains on image classification problems in the presence of distribution shift.
Opinion piece Just click on the mouse

The mouse genome is being sequenced in an efficient, coordinated manner. The map is complete and an assembly of whole genome shotgun data is available at a click of the mouse. In the final finished sequence phase, it seems a targeted approach would be more useful than random coverage. There are currently rich pickings available for mouse geneticists — that is to say, as long as their particular region of interest has been covered. Hard lessons learnt from the Human Genome Project have been put into practice, resulting in the elucidation of the mouse genome taking place with greater efficiency. The various genome centres have worked with coordination and a common strategy to generate a draft of the mouse genome in under a year, an achievement that would not have been deemed plausible a few years ago.
Parasite levels in blue-black grassquits correlate with male displays but not female mate preference

The handicap principle proposes that male sexual ornaments and displays provide honest indicators of quality. Female preference for high-quality males, however, may be driven not only by genetic benefits but also by indirect benefits. We investigated the impact of parasitism on morphological, ornamental, and behavioral characteristics of male and female blue-black grassquits (Volatinia jacarina) in captivity. First, we tested whether male displays and morphology were influenced by parasitism. Second, we assessed if females were attentive to variation in male morphology and displays linked to parasitism. Third, we tested whether parasitism in females influenced health and mate preferences. We maintained 2 groups of birds in captivity: nonmedicated birds developed high levels of coccidian parasitism, whereas medicated birds were free of parasitism. Parasitized males developed, relative to nonparasitized males, lower weight/tarsus indices and mass. They also showed relative deficiencies in their displays, with less persistence and lower rates. Despite the negative effects of parasitism on males, females did not prefer nonparasitized males. This held for both parasitized and nonparasitized females. Our data suggest that coccidian parasitism has adverse effects on morphological condition and expression of displays. These effects, however, appear not to be attended to by females; moreover, female mate preferences appear not to be impacted by the threat of parasitism. It thus seems that female mate preferences may not depend only on sexual characters affected by parasitism in this species.
TilinGNN

We introduce the first neural optimization framework to solve a classical instance of the tiling problem. Namely, we seek a non-periodic tiling of an arbitrary 2D shape using one or more types of tiles---the tiles maximally fill the shape's interior without overlaps or holes. To start, we reformulate tiling as a graph problem by modeling candidate tile locations in the target shape as graph nodes and connectivity between tile locations as edges. Further, we build a graph convolutional neural network, coined TilinGNN, to progressively propagate and aggregate features over graph edges and predict tile placements. TilinGNN is trained by maximizing the tiling coverage on target shapes, while avoiding overlaps and holes between the tiles. Importantly, our network is self-supervised, as we articulate these criteria as loss terms defined on the network outputs, without the need of ground-truth tiling solutions. After training, the runtime of TilinGNN is roughly linear to the number of candidate tile locations, significantly outperforming traditional combinatorial search. We conducted various experiments on a variety of shapes to showcase the speed and versatility of TilinGNN. We also present comparisons to alternative methods and manual solutions, robustness analysis, and ablation studies to demonstrate the quality of our approach.
Dust Reverberation Mapping in Distant Quasars from Optical and Mid-infrared Imaging Surveys

The size of the dust torus in active galactic nuclei (AGNs) and their high-luminosity counterparts, quasars, can be inferred from the time delay between UV/optical accretion disk continuum variability and the response in the mid-infrared (MIR) torus emission. This dust reverberation mapping (RM) technique has been successfully applied to ∼70 z ≲ 0.3 AGNs and quasars. Here we present first results of our dust RM program for distant quasars covered in the Sloan Digital Sky Survey Stripe 82 region combining ∼20 yr ground-based optical light curves with 10 yr MIR light curves from the WISE satellite. We measure a high-fidelity lag between W1 band (3.4 μm) and g band for 587 quasars over 0.3 ≲ z ≲ 2 ( ) and two orders of magnitude in quasar luminosity. They tightly follow (intrinsic scatter ∼0.17 dex in lag) the IR lag–luminosity relation observed for z < 0.3 AGNs, revealing a remarkable size–luminosity relation for the dust torus over more than four decades in AGN luminosity, with little dependence on additional quasar properties such as Eddington ratio and variability amplitude. This study motivates further investigations in the utility of dust RM for cosmology and strongly endorses a compelling science case for the combined 10 yr Vera C. Rubin Observatory Legacy Survey of Space and Time (optical) and 5 yr Nancy Grace Roman Space Telescope 2 μm light curves in a deep survey for low-redshift AGN dust RM with much lower luminosities and shorter, measurable IR lags. The compiled optical and MIR light curves for 7384 quasars in our parent sample are made public with this work.
Continuous-time finite-horizon ADP for automated vehicle controller design with high efficiency

Automated vehicle controller’s design can be formulated into a general optimal control problem. Existing control methods can not meet the millisecond-level time requirements of onboard standard controllers, especially for nonlinear dynamics with non-affine and saturated controller. This paper presents a continuous-time (CT) finite-horizon approximate dynamic programming (ADP) method, which can synthesis off-line near optimal control policy with analytical vehicle dynamics. Firstly, we develop a finite-horizon variant of the CT Hamilton-Jacobi-Bellman (HJB) equation. To find the nearly optimal solution of the finite-horizon HJB, i.e., the nearly optimal policy, both value function and policy are approximated by neural networks to map the system states to cost-to-go and control inputs respectively. Then the CT finite-horizon ADP algorithm is proposed based on the generalized policy iteration framework to gradually approach the optimal solution of the finite-horizon HJB. The proposed algorithm can converge to the nearly optimal policy and value function. Finally, we apply the proposed algorithm to the automated vehicle trajectory tracking control in both linear and nonlinear cases. The simulation results demonstrate that the proposed finite-horizon ADP algorithm can obtain the nearly optimal control policy and high online operation efficiency (500 times faster than the nonlinear ipopt solver).
Dark Matter as Perfect Fluid in Low Surface Brightness Galaxies

When dark matter is a perfect fluid, using the equation of state can get the density profile in the static and spherically symmetric space-time. If the equation of state is independent of the scaling transformation, its lower order approximation can naturally lead to a special case, i.e. $p=\zeta\rho+2\epsilon V_{rot}^{2}\rho$, where $p$ and $\rho$ are the the pressure and density, $V_{rot}$ is the rotation velocity of galaxy, $\zeta$ and $ \epsilon$ are positive constants. Then we use this equation of state to derive the mass density profiles of dark matter halos. It can obtain a profile which is similar to the pseudo-isothermal halo model when $\epsilon$ is around $0.15$. It can perfectly fit the observed rotation curves of low surface brightness (LSB) galaxies. When $\zeta=0$, there exits a power law density in the very outer region which surround a black hole, and its power index is $-\frac{1+4\epsilon}{4\epsilon}$. The term $\zeta\rho$ can lead to a constant-density core.
Decomposing Linear Representations of Finite Groups

We develop a package using the computer algebra system GAP for computing the decomposition of a representation $\rho$ of a finite group $G$ over $\mathbb{C}$ into irreducibles, as well as the corresponding decomposition of the centraliser ring of $\rho(G)$. Currently, the only open-source programs for decomposing representations are for non-zero characteristic fields. While methods for characteristic zero are known, there are no open-source computer programs that implement these methods, nor are details on how to achieve good performance of such an implementation published. We aim to record such details and demonstrate an application of our program in reducing the size of semidefinite programs.
Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval

The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries, which are usually ineffective for complex queries that carry far more complex semantics. Recently, embedding-based paradigm has emerged as a popular approach. It aims to map the queries and videos into a shared embedding space where semantically-similar texts and videos are much closer to each other. Despite its simplicity, it forgoes the exploitation of the syntactic structure of text queries, making it suboptimal to model the complex queries. To facilitate video retrieval with complex queries, we propose a Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos. Specifically, given a complex user query, we first recursively compose a latent semantic tree to structurally describe the text query. We then design a tree-augmented query encoder to derive structure-aware query representation and a temporal attentive video encoder to model the temporal characteristics of videos. Finally, both the query and videos are mapped into a joint embedding space for matching and ranking. In this approach, we have a better understanding and modeling of the complex queries, thereby achieving a better video retrieval performance. Extensive experiments on large scale video retrieval benchmark datasets demonstrate the effectiveness of our approach.
Learning Personalized Risk Preferences for Recommendation

The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions. With this information, they can infer the quality of products to reduce the risk of purchase. Specifically, items with high rating scores and good reviews tend to be less risky, while items with low rating scores and bad reviews might be risky to purchase. On the other hand, the purchase behaviors will also be influenced by consumers' tolerance of risks, known as the risk attitudes. Economists have studied risk attitudes for decades. These studies reveal that people are not always rational enough when making decisions, and their risk attitudes may vary in different circumstances. Most existing works over recommendation systems do not consider users' risk attitudes in modeling, which may lead to inappropriate recommendations to users. For example, suggesting a risky item to a risk-averse person or a conservative item to a risk-seeking person may result in the reduction of user experience. In this paper, we propose a novel risk-aware recommendation framework that integrates machine learning and behavioral economics to uncover the risk mechanism behind users' purchasing behaviors. Concretely, we first develop statistical methods to estimate the risk distribution of each item and then draw the Nobel-award winning Prospect Theory into our model to learn how users choose from probabilistic alternatives that involve risks, where the probabilities of the outcomes are uncertain. Experiments on several e-commerce datasets demonstrate that by taking user risk preferences into consideration, our approach can achieve better performance than many classical recommendation approaches, and further analyses also verify the advantages of risk-aware recommendation beyond accuracy.
The Effects of a Multicomponent Lower Extremity Training Technique on Physical Function in Healthy Older Adults: A Randomized Controlled Trial

Objective: We aimed to examine the outcomes of our novel multicomponent lower extremity training (MLT) technique on physical function in older adults. Methods: Participants were randomly divided into a training group (TG) or a control group (CG). The TG (4 men, 14 women) received MLT for 24 weeks, once per week. MLT contains strength, balance, and flexibility components. The CG (5 men, 10 women) did not receive any training for 24 weeks. Nine lower extremity range of motions (ROMs; hip flexion, hip abduction, hip adduction, hip extension, internal and external hip rotations, knee flexion, ankle dorsiflexion, and ankle plantar flexion) and two muscle strength assessments (knee extension and flexion) were collected. Physical performance tests were also performed, including the functional reach test, timed up and go test (TUGT), and five times sit-to-stand test (FTSST). Results: After 24 weeks, significant increases were observed in the TG in all ROMs (with the exception of knee flexion), knee extension strength, and performance in the TUGT and FTSST. Conclusions: MLT significantly improved ROM, muscle strength, and physical performance in healthy older adults. We suggest that it is an efficacious intervention in the maintenance and improvement of mobility and functional independence in healthy older adults. Trial registration: UMIN CTR, UMIN000037463. https://upload.umin. ac.jp/cgi-open-bin/ctr_e/ctr_view. cgi? recptno=R000041955
Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey

Automatic target recognition (ATR) for military applications is one of the core processes toward enhancing intelligence and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this article surveys and assesses current SAR ATR algorithms that employ the most popular dataset for the SAR domain, namely the moving and stationary target acquisition and recognition (MSTAR) dataset. Specifically, we perform a direct comparison between current SAR ATR methods and highlight the strengths and weaknesses of each technique under both standard and extended operational conditions. Additionally, despite MSTAR being the standard SAR ATR benchmarking dataset, we also highlight its weaknesses and suggest future research directions.
Insight into Potential Well Based Nanoscale FDSOI MOSFET Using Doped Silicon Tubs- A Simulation and Device Physics Based Study: Part I: Theory and Methodology

A novel planar device having doped silicon regions (tubs) under the source and drain of an FDSOI MOSFET is reported at 20 nm gate length. The doped silicon regions result in formation of potential wells (PW) in the source and drain regions of FDSOI MOSFET and thus, the device being called as Potential Well Based FDSOI MOSFET (PWFDSOI MOSFET). Simulation and device physics study on PWFDSOI MOSFET showed reduction in the OFF current of the device by orders of magnitude. A low IOF F of 22 pA/um, high ION /IOF F ratio of 1.5 x 107 and subthreshold swing of 76 mV/decade were achieved in 20 nm gate length PWFDSOI MOSFET. The study was performed on devices with unstrained silicon channel.
Isolation and Immortalization of Fibroblasts from Different Tumoral Stages

[Abstract] Tumour microenvironment and cancer-associated fibroblasts in particular exhibit tumour promoting abilities that are not present in their normal counterparts (Calvo et al., 2013; Hanahan and Coussens, 2012). Therefore, functional and molecular characterization of the modifications occurring in fibroblasts during tumour progression is essential to fully understand their role in tumour progression. Previous studies have addressed this issue using human fibroblasts and comparing normal and adjacent fibroblasts to tumour-associated fibroblasts (Kalluri and Zeisberg, 2006). However, these studies are hampered by the intrinsic variability of human samples (e.g. pairing, age, genomic landscape, etc). In order to overcome these issues, we used a fully characterised mouse breast cancer model, MMTV-PyMT (Guy et al., 1992; Lin et al., 2003). MMTV-PyMT transgenic mice express the Polyoma Virus middle T antigen under the direction of the mouse mammary tumor virus promoter/enhancer. This is a multifocal luminal breast cancer model that goes through well defined and characterised stages (namely, hyperplasia, adenoma, carcinoma and invasive carcinoma). Interestingly, this model has a 100% incidence, is very desmoplastic (presenting high concentration of fibroblasts) and gives raise to spontaneous metastasis in the lung with 80-94% incidence. Importantly, at least for the inguinal mammary glands (glands 4 and 9), the different tumoral stages are well correlated to the age of the mouse: hyperplasia arising at 6 weeks of age, adenoma between 6-8 weeks of age, carcinoma and invasive carcinoma from 8 weeks onwards. This model allowed us to confidently isolate fibroblasts from different tumoral stages and carefully characterise their functional and molecular properties (Calvo et al., 2013).
Robust Prediction of Punctuation and Truecasing for Medical ASR

Automatic speech recognition (ASR) systems in the medical domain that focus on transcribing clinical dictations and doctor-patient conversations often pose many challenges due to the complexity of the domain. ASR output typically undergoes automatic punctuation to enable users to speak naturally, without having to vocalize awkward and explicit punctuation commands, such as “period”, “add comma” or “exclamation point”, while truecasing enhances user readability and improves the performance of downstream NLP tasks. This paper proposes a conditional joint modeling framework for prediction of punctuation and truecasing using pretrained masked language models such as BERT, BioBERT and RoBERTa. We also present techniques for domain and task specific adaptation by fine-tuning masked language models with medical domain data. Finally, we improve the robustness of the model against common errors made in ASR by performing data augmentation. Experiments performed on dictation and conversational style corpora show that our proposed model achieves 5% absolute improvement on ground truth text and 10% improvement on ASR outputs over baseline models under F1 metric.
Themes Related to Experienced Quality of Care in Nursing Homes From the Resident’s Perspective: A Systematic Literature Review and Thematic Synthesis

Background: The culture change from task-centered care to person- and relationship-centered care has resulted in the resident’s voice gaining importance when assessing experienced quality of care in nursing homes. This review aimed to identify which factors contribute to experienced quality of care in nursing homes worldwide from the resident’s perspective. Method: A systematic literature review and thematic data synthesis were performed. The databases PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycInfo, and Business Source Complete were searched to identify qualitative studies aimed at retrieving factors related to residents’ experienced quality of care in nursing homes. Only studies in which residents themselves were interviewed were included. Results: This literature review included 27 publications covering 14 countries. Thematic analysis revealed three overarching themes related to residents’ care experiences: (a) The nursing home environment consisted of the physical environment and caring environment, (b) individual aspects of living in the nursing home consisted of personhood and coping with change, and (c) social engagement consisted of meaningful relationships and care provision. Discussion: To achieve high experienced quality of care in nursing homes, residents’ care experiences need to be assessed and used in quality management.
Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation

Satirical news is regularly shared in modern social media because it is entertaining with smartly embedded humor. However, it can be harmful to society because it can sometimes be mistaken as factual news, due to its deceptive character. We found that in satirical news, the lexical and pragmatical attributes of the context are the key factors in amusing the readers. In this work, we propose a method that differentiates the satirical news and true news. It takes advantage of satirical writing evidence by leveraging the difference between the prediction loss of two language models, one trained on true news and the other on satirical news, when given a new news article. We compute several statistical metrics of language model prediction loss as features, which are then used to conduct downstream classification. The proposed method is computationally effective because the language models capture the language usage differences between satirical news documents and traditional news documents, and are sensitive when applied to documents outside their domains.