Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating scene typicality from human ratings and image features Citation

Scenes, like objects, are visual entities that can be categorized into functional and semantic groups. One of the core concepts of human categorization is the idea that category membership is graded: some exemplars are more typical than others. Here, we obtain human typicality rankings for more than 120,000 images from 706 scene categories through an online rating task on Amazon Mechanical Turk. We use these rankings to identify the most typical examples of each scene category. Using computational models of scene classification based on global image features, we find that images which are rated as more typical examples of their category are more likely to be classified correctly. This indicates that the most typical scene examples contain the diagnostic visual features that are relevant for their categorization. Objectless, holistic representations of scenes might serve as a good basis for understanding how semantic categories are defined in term of perceptual representations.
Unsupervised Visual Representation Learning by Tracking Patches in Video Supplementary Material

In this section, we introduce the evaluation details when transferring the pretrained models to the downstream tasks. Action recognition We follow the common practice [4] to construct an action recognition model. Specifically, we adopt a CNN encoder to extract the spatial-temporal feature from the raw video clip. The CNN encoder can be initialized from the pretrained models of various proxy tasks, e.g., VCOP [3], 3DRot [1] or our CtP. The feature extracted by the CNN encoder is averaged in both spatial and temporal dimensions, which leads to a feature vector of size 512 (for R3D-18 and R(2+1)D-18 in our work). We append a onelayer linear classifier based on the averaged feature. The entire action recognition model is finetuned on the target datasets. The input video clip has a temporal coverage of 32 frames. For UCF-101 and HMDB-51 datasets, we sample 16 frames with a temporal stride of 2. For Something-Something dataset, we successively sample 32 frames since this dataset emphasizes more fine-grained temporal relationships. The standard data augmentation strategy is applied in the training period, including random cropping, horizontal flip and color jitters. During inference time, the video frames are resized to a spatial resolution of 171 × 128, and we center crop the regions of shape 112 × 112. For each video, we temporal-uniformly pick 10 clips. The final classification decision of the video is averaged by the prediction results of these clips. The optimization lasts for 150 epochs with standard SGD algorithms. The learning rate is started from 0.01 and it is gradually decayed by a factor of 0.1 at 60th epoch and 120th epoch. The weight decay and the momentum value is set to 5×10−4 and 0.9, respectively. After the optimization, we adopt the model produced in the last epoch for evaluation. Video clip retrieval In the video clip retrieval task, the pretrained CNN encoder is directly used without further finetuning. We follow the same implementation as in VCOP [3]. The output feature of the CNN encoder is transferred to a fixed shape of 2 × 3 × 3 × 512 by an adaptive pooling operation, where each dimension denotes the temporal size, height, width and channels, respectively. For each video, we uniformly sample 10 clips and use the averaged feature of 0 20 40 60 80 100 120 140 Training epoch 0 1 2 3 4 5
SHREC 2021: CryoET Track

Cryo-electron tomography (cryo-ET) is an imaging technique that allows three-dimensional visualization of macro-molecular assemblies under near-native conditions. Cryo-ET comes with a number of challenges, mainly low signal-to-noise and inability to obtain images from all angles. Computational methods are key to analyze cryo-electron tomograms. To promote innovation in computational methods, we generate a novel simulated dataset to benchmark different methods of localization and classification of biological macromolecules in tomograms. Our publicly available dataset contains ten tomographic reconstructions of simulated cell-like volumes. Each volume contains twelve different types of complexes, varying in size, function and structure. In this paper, we have evaluated seven different methods of finding and classifying proteins. Seven research groups present results obtained with learning-based methods and trained on the simulated dataset, as well as a baseline template matching (TM), a traditional method widely used in cryoET research. We show that learning-based approaches can achieve notably better localization and classification performance than TM. We also experimentally confirm that there is a negative relationship between particle size and performance for all methods.
On the asymptotic behavior of distributions of work performed on diffusion particles in time-varying potentials

For single molecule experiments, the role of thermodynamic quantities such as work, heat, and entropy, when defined on the level of stochastic trajectories, has become an important field in nonequilibrium statistical mechanics [1]. A main achievement in this field is the discovery of detailed and integral fluctuation theorems. These theorems hold true universally and they can be viewed as a generalization of the second law of thermodynamics. The Jarzynski equality (JE) is perhaps the most prominent. It pertains to systems in contact with a heat reservoir at temperature T, which are initially in equilibrium and then are driven out of equilibrium by a change of control variable λ(t) during a time interval 0, . The JE states that < / B / B d ∆ / B , where w is the stochastic work done on the system during one realization (trajectory) of the process, p(w) is the distribution of work values, and ΔF=F(λ(tf))F(λ(0)) is the free energy difference between the macrostates belonging to the initial and final values of the control variable. The JE becomes particularly valuable in unidirectional experimental settings, where the work for the reversed protocol can not be measured, and accordingly other theorems, as, e.g., the Crooks theorem can not be applied.
Ferroelectric thin film on a silicon-based pn junction: Coupling photovoltaic properties

ABSTRACT PbTiO3 (PTO) thin film was deposited on a Si-based pn-junction to form a ferroelectric-pn junction structure (PTO/pn cell). It exhibits a coupling photovoltaic property. Voc of the PTO/pn cell is approximately equal to Voc summation of single pn-junction structure (pn cell) and single PTO films (PTO cell). Isc and internal quantum efficiency of the PTO/pn cell are much higher than that of the PTO cell. Photovoltaic properties of the PTO/pn cell can be tuned by controlling the direction of the polarization voltage applied on the cell. Reasons for the tuning and enhanced photovoltaic properties are analyzed.
Computers in Biology and Medicine

Differential equations-based epidemic compartmental models and deep neural networks-based artificial intelligence (AI) models are powerful tools for analyzing and fighting the transmission of COVID-19. However, the capability of compartmental models is limited by the challenges of parameter estimation, while AI models fail to discover the evolutionary pattern of COVID-19 and lack explainability. This paper aims to provide a novel method (called Epi-DNNs) by integrating compartmental models and deep neural networks (DNNs) to model the complex dynamics of COVID-19. In the proposed Epi-DNNs method, the neural network is designed to express the unknown parameters in the compartmental model and the Runge–Kutta method is implemented to solve the ordinary differential equations (ODEs) so as to give the values of the ODEs at a given time. Specifically, the discrepancy between predictions and observations is incorporated into the loss function, then the defined loss is minimized and applied to identify the best-fitted parameters governing the compartmental model. Furthermore, we verify the performance of Epi-DNNs on the real-world reported COVID-19 data on the Omicron epidemic in Shanghai covering February 25 to May 27, 2022. The experimental findings on the synthesized data have revealed its effectiveness in COVID-19 transmission modeling. Moreover, the inferred parameters from the proposed Epi-DNNs method yield a predictive compartmental model, which can serve to forecast future dynamics.
Bimodality based on the generalized skew-normal distribution

ABSTRACT This paper focuses on the development of a new extension of the generalized skew-normal distribution introduced in Gómez et al. [Generalized skew-normal models: properties and inference. Statistics. 2006;40(6):495–505]. To produce the generalization a new parameter is introduced, the signal of which has the flexibility of yielding unimodal as well as bimodal distributions. We study its properties, derive a stochastic representation and state some expressions that facilitate moments derivation. Maximum likelihood is implemented via the EM algorithm which is based on the stochastic representation derived. We show that the Fisher information matrix is singular and discuss ways of getting round this problem. An illustration using real data reveals that the model can capture well special data features such as bimodality and asymmetry.
The Psychological Impact of COVID-19 Pandemic on Women’s Mental Health during Pregnancy: A Rapid Evidence Review

Background: The perinatal period is a particularly vulnerable period in women’s lives that implies significant physiological and psychological changes that can place women at higher risk for depression and anxiety symptoms. In addition, the ongoing pandemic of coronavirus disease 2019 (COVID-19) is likely to increase this vulnerability and the prevalence of mental health problems. This review aimed to investigate the existing literature on the psychological impact of the COVID-19 pandemic on women during pregnancy and the first year postpartum. Method: The literature search was conducted using the following databases: Pubmed, Scopus, WOS—web of science, PsycInfo and Google Scholar. Out of the total of 116 initially selected papers, 17 have been included in the final work, according to the inclusion criteria. Results: The reviewed contributions report a moderate to severe impact of the COVID-19 outbreak on the mental health of pregnant women, mainly in the form of a significant increase in depression—up to 58% in Spain—and anxiety symptoms—up to 72% in Canada. In addition to the common psychological symptoms, COVID-19-specific worries emerged with respect to its potential effects on pregnancy and the well-being of the unborn child. Social support and being engaged in regular physical activities appear to be protective factors able to buffer against the effects of the pandemic on maternal mental health. Conclusions: Despite the limitations of the study design, the evidence suggests that it is essential to provide appropriate psychological support to pregnant women during the emergency in order to protect their mental health and to minimize the risks of long-term effects on child development.
spacetime: Spatio-Temporal Data in R

This document describes classes and methods designed to deal with different types of spatio-temporal data in R implemented in the R package spacetime, and provides examples for analyzing them. It builds upon the classes and methods for spatial data from package sp, and for time series data from package xts. The goal is to cover a number of useful representations for spatio-temporal sensor data, and results from predicting (spatial and/or temporal interpolation or smoothing), aggregating, or subsetting them, and to represent trajectories. The goals of this paper are to explore how spatio-temporal data can be sensibly represented in classes, and to find out which analysis and visualisation methods are useful and feasible. We discuss the time series convention of representing time intervals by their starting time only. This vignette is the main reference for the R package spacetime; it has been published as Pebesma (2012), but is kept up-to-date with the software.
Identification of nonlinear ship model parameters based on the turning circle test

This work presents a contribution to solving the problem of identification of ship model parameters using the experimental results from a particular trial test. The innovation of this paper lies in the fact that for this identification purpose it is necessary to know only the turning radius that describes the ship during the performance of the turning test trial. A relatively complex nonlinear model of Norrbin has been chosen as a basis because it represents the ship's dynamics appropriately, as proven through experimental measurements obtained during the course change test. The proposed algorithm of identification of the four ship model parameters is based on an adaptive procedure and the backstepping theory. Another additional coefficient can be determined by an alternative procedure. The knowledge of the true values that characterize the dynamic of a ship is fundamental in the ship steering control that is carried out by autopilots. The simulation results show the suitability of the proposed procedure.
MARL-PPS: Multi-agent Reinforcement Learning with Periodic Parameter Sharing

We present a multi-agent reinforcement learning algorithm that is a simple, yet effective modification of a known algorithm. External agents are modeled as a time-varying environment, whose policy parameters are updated periodically at a slower rate than the planner to make learning stable and more efficient. Replay buffer, which is used to store the experiences, is also reset with the same large period to draw samples from a fixed environment. This enables us to address challenging cooperative control problems in highway navigation. The resulting Multi-agent Reinforcement Learning with Periodic Parameter Sharing (MARL-PPS) algorithm outperforms the baselines in multi-agent highway scenarios we tested.
State generalization method with support vector machines in reinforcement learning

The conventional reinforcement learning assumes discrete state space. Therefore, it is necessary to make states discrete in order to handle continuous state environments. However, if a simple discretization is applied, the number of states increases exponentially with the dimension of the state space, and the learning time increases. In this paper, we propose a state generalization that is able to quickly adapt to environments by using Support Vector Machines. We conducted an experiment on the simulation task that navigates a robot to a goal. As a result of the experiment, the proposed method adapted to environment by a few trials.
Unsupervised Adversarially-Robust Representation Learning on Graphs

Recent works have demonstrated that deep learning on graphs is vulnerable to adversarial attacks, in that imperceptible perturbations on input data can lead to dramatic performance deterioration. In this paper, we focus on the underlying problem of learning robust representations on graphs via mutual information. In contrast to previous works measure the task-specific robustness based on the label space, we here take advantage of the representation space to study a task-free robustness measure given the joint input space w.r. t graph topology and node attributes. We formulate this problem as a constrained saddle point optimization problem and solve it efficiently in a reduced search space. Furthermore, we provably establish theoretical connections between our task-free robustness measure and the robustness of downstream classifiers. Extensive experiments demonstrate that our proposed method is able to enhance robustness against adversarial attacks on graphs, yet even increases natural accuracy.
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization

In this paper, we present a novel framework that can achieve multimodal image-to-image translation by simply encouraging the statistical dependence between the latent code and the output image in conditional generative adversarial networks. In addition, by incorporating a U-net generator into our framework, our method only needs to train a one-sided translation model from the source image domain to the target image domain for both supervised and unsupervised multi-modal image-to-image translation. Furthermore, our method also achieves disentanglement between the source domain content and the target domain style for free. We conduct experiments under supervised and unsupervised settings on various benchmark image-to-image translation datasets compared with the state-of-the-art methods, showing the effectiveness and simplicity of our method to achieve multimodal and high-quality results.
Relationship Between Perceived Social Support and Attitudes Towards Menopause among Women and Affecting Factors

Objective: The aim of the study was to investigate affecting factors on perceived social support and attitudes towards menopause and the relationship between perceived social support and attitudes towards menopause of Turkish women. Methods: The study was planned as a descriptive and cross-sectional design. The data were collected with questionnaire form, Multidimensional Scale of Perceived Social Support and Attitudes towards Menopause Scale. Results: A majority of women (66.7%) had a negative attitude towards menopause. Strong perceived social support positively affects women’s attitudes towards menopause. Woman who have higher education level, being age 50 and under, working, being single, having normal BMI, and being in a nuclear family had more positive attitude towards menopause. Other factors were a husband who is a civil servant and with higher education, having a higher income perception and positive health perception, living in a province, physical exercise, being premenopausal, being informed about menopause. The perceived social support scale scores were higher than for other women in the following categories: age 50 and under, higher education level, working, higher income perception, single, living in a province, physical exercise, and positive health perception. Conclusion: In conclusion, the majority of women had negative attitudes toward menopause and perceived social support during menopause was at low levels. The perceived social support and attitude towards menopause correlated. Informational programs need to be focused on developing positive attitudes in both women and men to help couples and strengthening women’s social support and preparation during menopause.
Two-laser multiphoton adiabatic passage in the frame of the Floquet theory . Applications to ( 1 + 1 ) and ( 2 + 1 ) STIRAP

We develop an adiabatic two-mode Floquet theory to analyse multiphoton coherent population transfer in N-level systems by two delayed laser pulses, which is a generalization of the three-state stimulated Raman adiabatic passage (STIRAP). The main point is that, under conditions of non-crossing and adiabaticity, the outcome and feasibility of a STIRAP process can be determined by the analysis of two features: (i) the lifting of degeneracy of dressed states at the beginning and at the end of the laser pulses, and (ii) the connectivity of these degeneracy-lifted branches in the quasienergy diagram. Both features can be determined by stationnary perturbation theory in the Floquet representation. As an illustration, we study the corrections to the RWA of the (1+1) STIRAP in strong fields and for large detunings. We analyse the possible breakdown of connectivity. In strong fields, the complete transfer is achieved, but the intermediate state, unpopulated within the RWA, can become populated during the process. In the (2+1) STIRAP, we show a residual degeneracy in a four-level system, that can be lifted by additional Stark shifts. The complete transfer is achieved under conditions of connectivity. PACS. 42.50. Hz Strong-field excitation of optical transitions in quantum systems; multiphoton processes; dynamic Stark shift – 33.80. Be Level crossing and optical pumping – 42.65. Dr Stimulated Raman scattering; CARS
TENSILE: A Tensor granularity dynamic GPU memory scheduler method towards multiple dynamic workloads system

Recently, deep learning has been an area of intense researching. However, as a kind of computing intensive task, deep learning highly relies on the scale of GPU memory, which is usually pro-hibitive and scarce. Although there are some extensive works have been proposed for dynamic GPU memory management, they are hard to be applied to systems with multiple dynamic workloads, such as in-database machine learning system. In this paper
A New Confidence Interval for Odds Ratio

We consider the problem of interval estimation of the odds ratio. An asymptotic confidence interval is widely applied in medical research. Unfortunately that confidence interval has a poor coverage probability: it is significantly smaller than the nominal confidence level. In this paper a new confidence interval is proposed. The construction needs only information on sample sizes and sample odds ratio. The coverage probability of the proposed confidence interval is at least the nominal confidence level.
Information Fusion

Dependency parsing is a crucial step towards deep language understanding and, therefore, widely demanded by numerous Natural Language Processing applications. In particular, left-to-right and top-down transition-based algorithms that rely on Pointer Networks are among the most accurate approaches for performing dependency parsing. Additionally, it has been observed for the top-down algorithm that Pointer Networks’ sequential decoding can be improved by implementing a hierarchical variant, more adequate to model dependency structures. Considering all this, we develop a bottom-up oriented Hierarchical Pointer Network for the left-to-right parser and propose two novel transition-based alternatives: an approach that parses a sentence in right-to-left order and a variant that does so from the outside in. We empirically test the proposed neural architecture with the different algorithms on a wide variety of languages, outperforming the original approach in practically all of them and setting new state-of-the-art results on the English and Chinese Penn Treebanks for non-contextualized and BERT-based embeddings.
Multilingual Neural Machine Translation for Low-Resource Languages

Neural Machine Translation (NMT) has been shown to be more effective in translation tasks compared to phrase-based statistical machine translation. However, NMT systems are limited in translating low-resourced languages, due to the signiﬁcant amount of parallel data that is required to learn useful mappings between languages. In this work, we show how so-called multilingual NMT can help to tackle the challenges associated with low-resourced language translation. The principle of multilingual NMT is to force the creation of hidden representations of words in a shared semantic space across multiple languages, thus enabling a positive parameter transfer across languages. In particular, we present multilingual translation experiments with three languages (English, Italian, Romanian) covering six translation directions, utilizing both recurrent neural networks and transformer (or self-attentive) neural networks. We then focus on the zero-shot translation problem, that is how to leverage multi-lingual data in order to learn translation directions uncovered by the data. Hence, we introduce our recently proposed iterative self-training method, that incrementally improves a multilingual NMT on a zero-shot direction by just relying on monolingual data. Our results on TED talks data show that multilingual NMT outperforms conventional bilingual NMT, that the transformer NMT outperforms recurrent NMT, and that zero-shot NMT outperforms conventional pivoting methods and even matches the performance of a fully-trained bilingual system.