Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepSea: An efficient deep learning model for automated cell segmentation and tracking

: Time-lapse microscopy can directly capture the dynamics and heterogeneity of cellular processes at the single-cell level. Successful application of single-cell live microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. Recently, deep learning models have ushered in a new era in quantitative analysis of microscopy images. This work presents a versatile and trainable deep-learning-based software, termed DeepSea, that allows for both segmentation and tracking of single cells and their nuclei in sequences of phase-contrast live microscopy images. We show that DeepSea can quantify several cell biological features of mouse embryonic stem cells, such as cell division cycle, mitosis, cell morphology, and cell size, with high precision using phase-contrast images. Using DeepSea, we were able to show that despite their ultrafast cell division cycle, mouse embryonic stem cells exhibit cell size control in the G1 phase of the cell cycle.
Trident Pyramid Networks: The importance of processing at the feature pyramid level for better object detection

Feature pyramids have become ubiquitous in multi-scale computer vision tasks such as object detection. Based on their importance, we divide a computer vision network into three parts: a backbone (generating a feature pyramid), a core (reﬁning the feature pyramid) and a head (generating the ﬁnal output). Most existing networks operating on feature pyramids, named cores, are shallow and mostly focus on communication-based processing in the form of top-down and bottom-up operations. We present a new core architecture called Trident Pyramid Network (TPN), that allows for a deeper design and for a better balance between communication-based processing and self-processing. We show consistent improvements when using our TPN core on the COCO object detection benchmark, outperforming the popular BiFPN baseline by 1 . 5 AP. Additionally, we empirically show that it is more beneﬁcial to put additional computation into the TPN core, rather than into the backbone, by outperforming a ResNet-101+FPN base-line with our ResNet-50+TPN network by 1 . 7 AP, while operating under similar computation budgets. This emphasizes the importance of performing computation at the feature pyramid level in modern-day object detection systems. Code will be released.
Moduli of stable maps in genus one and logarithmic geometry, I

This is the first in a pair of papers developing a framework for the application of logarithmic structures in the study of singular curves of genus $1$. We construct a smooth and proper moduli space dominating the main component of Kontsevich's space of stable genus $1$ maps to projective space. A variation on this theme furnishes a modular interpretation for Vakil and Zinger's famous desingularization of the Kontsevich space of maps in genus $1$. Our methods also lead to smooth and proper moduli spaces of pointed genus $1$ quasimaps to projective space. Finally, we present an application to the log minimal model program for $\mathcal{M}_{1,n}$. We construct explicit factorizations of the rational maps among Smyth's modular compactifications of pointed elliptic curves.
UOD AND SEGMENTATION IN VIDEOS 3 UOD CRF Segmentation Appearance maps Object Models Segmented FramesInput Sequences Optical Flow

Unsupervised object discovery is the task of finding recurring objects over an unsorted set of images without any human supervision, which becomes more and more important as the amount of visual data grows exponentially. Existing approaches typically build on still images and rely on different prior knowledge to yield accurate results. In contrast, we propose a novel video-based approach, allowing also for exploiting motion information, which is a strong and physically valid indicator for foreground objects, thus, tremendously easing the task. In particular, we show how to integrate motion information in parallel with appearance cues into a common conditional random field formulation to automatically discover object categories from videos. In the experiments, we show that our system can successfully extract, group, and segment most foreground objects and is also able to discover stationary objects in the given videos. Furthermore, we demonstrate that the unsupervised learned appearance models also yield reasonable results for object detection on still images.
Direct Multi-view Multi-person 3D Pose Estimation (Supplementary Material)

We use PyTorch [9] to implement the proposed Multi-view Pose transformer (MvP) model. Our MvP model is trained on 8 Nvidia RTX 2080 Ti GPUs, with a batch size of 1 per GPU and a total batch size of 8. We use the Adam optimizer [7] with an initial learning rate of 1e-4 and decrease the learning rate by a factor of 0.1 at 20 epochs during training. The hyper-parameter λ for balancing confidence score and pose regression losses is set to 2.5. We use the image feature representations (256-d) from the de-convolution layer of the 2D pose estimator PoseResNet [11] for multi-view inputs. Additionally, we provide the code of MvP, including the implementation of model architecture, training and inference, in the folder of “. /mvp” for better understanding our method.
Probabilistic imaginary-time evolution by using forward and backward real-time evolution with a single ancilla: first-quantized eigensolver of quantum chemistry for ground states

Imaginary-time evolution (ITE) on a quantum computer is a promising formalism for obtaining the ground state of a quantum system. As a kind of it, the probabilistic ITE (PITE) takes advantage of measurements to implement the nonunitary operations. We propose a new approach of PITE which requires only a single ancillary qubit. Under a practical approximation, the circuit is constructed from the forward and backward real-time evolution (RTE) gates as black boxes, generated by the original many-qubit Hamiltonian. All the efficient unitary quantum algorithms for RTE proposed so far and those in the future can thus be transferred to ITE exactly as they are. Our approach can also be used for obtaining the Gibbs state at a finite temperature and the partition function. We apply the approach to several systems as illustrative examples to see its validity. We also discuss the application of our approach to quantum chemistry by focusing on the scaling of computational cost, leading to a novel framework denoted by first-quantized quantum eigensolver.
Time Blocks Decomposition of Multistage Stochastic Optimization Problems

Multistage stochastic optimization problems are, by essence, complex as their solutions are indexed both by stages and by uncertainties. Their large scale nature makes decomposition methods appealing, like dynamic programming which is a sequential decomposition using a state variable defined at all stages. In this paper, we introduce the notion of state reduction by time blocks, that is, at stages that are not necessarily all the original stages. Then, we prove a reduced dynamic programming equation. We position our result with respect to the most well-known mathematical frameworks for dynamic programming. We illustrate our contribution by showing its potential for applied problems with two time scales.
THE IMPLEMENTATION OF PROJECT-BASED LEARNING IN AN ESP CLASS TO IMPROVE READING AND SPEAKING SKILLS

Employing a descriptive qualitative method, the study aims at describing how the implementation of Project-Based Learning (PBL) has improved students’ reading and speaking skills in an English for Specific Purposes (ESP) class at Universitas Atma Jaya Yogyakarta. The contextual and real-life situations designed in PBL are expected to equip them better to practice the language and apply the content knowledge. The results of the questionnaires and interviews suggest that the students generally perceived PBL as an effective and creative method in learning. It has also helped them acquire better reading and speaking skills through the accomplishment of a variety of group projects. The improvements cover, among others, vocabulary, grammar as well as fluency and confidence in speaking. It is hoped that this research can provide benefits to researchers and FBE UAJY in general. The results of this study may provide meaningful inputs on the process of teaching and learning activities. Thus, ESP lecturers can find the most appropriate method to improve students’ English skills.
Huskie Commons Huskie Commons

Corporate social responsibility (CSR) has emerged as an effective way for firms to create favorable attitudes among consumers. Although prior research has addressed the direct influence of proactive and reactive CSR on consumer responses, this research hypothesized that consumers’ perceived organizational motives (i.e., attributions) will mediate this relationship. It was also hypothesized that the source of information and location of CSR initiative will affect the motives consumers assign to a firms’ engagement in the initiative. Two experiments were conducted to test these hypotheses. The results of Study 1 indicate that the nature of a CSR initiative influences consumer attribution effects and that these attributions act as mediators in helping to explain consumers’ responses to CSR. Study 2 suggests that the source of the CSR message moderates the effect of CSR on consumer attributions. The mediating influence of the attributions as well as the importance of information source suggests that proper communication of CSR can be a viable way to inculcate positive corporate associations and purchase intentions.
Laboratory Measurement of Falling Velocity of Individual Ice crystals

Measurements of falling velocity of individual ice crystals produced by dry ice seeding were made in a cold room. The falling velocity was measured by stroboscopic photographs of falling ice crystals and each crystal was replicated to observe the shape and size. The relationship between the falling velocity and the dimension of crystals was obtained for the plane and the columnar type of crystals. Moreover, the measured falling velocity was generally explained by the computation method which use the drag coefficient obtained from the model experiment.
Factors Associated with HIV Counseling and Testing among Males and Females in Ethiopia: Evidence from Ethiopian Demographic and Health Survey Data

Background: HIV counseling and testing is one of the key strategies in the HIV/AIDS prevention and control programs in Ethiopia. However utilization of this service is very low. The aim of the study was to assess and compare factors associated with HIV counseling and testing service utilization among Ethiopian males and females. Methods: The study utilized data from the Ethiopian Demographic and Health Survey (EDHS) 2011, which was a cross-sectional survey conducted on a nationally representative sample. Using cluster sampling, 14110 males aged 15-59 years and 16,834 females aged 15-49 were selected from all the 9 administrative regions and 2 administrative cities of Ethiopia. Descriptive analysis was done and chi-square test was used to test the association of each independent variable with the dependent variable. Multivariable logistic regression was used to identify potential factors associated with HIV testing service utilization. Results: Generally, 42.5% male and 41.9% female participants had ever been tested and counseled for HIV at least once. Education level, wealth index, area of residence, knowledge about HIV/AIDS and having no stigmatizing attitudes toward people living with HIV/AIDS were found to be strongly and positively associated with HIV testing service utilization in both male and female genders. HIV test rate was higher among younger men and women (aged ≤ 34 years), rich in income, urban residents, exposed to mass media and those of educated secondary and above. Conclusions: HIV testing service utilization among male and females in Ethiopia is low. HIV/AIDS-related stigma, HIV related knowledge, place of residence, educational level, marital status, wealth index, and media exposure were major factors affecting HIV testing service utilization among males and females in the country.
Magnetic Flux Cancellation as the Trigger Mechanism of Solar Coronal Jets

Coronal jets are transient narrow features in the solar corona that originate from all regions of the solar disk: active regions, quiet Sun, and coronal holes. Recent studies indicate that at least some coronal jets in quiet regions and coronal holes are driven by the eruption of a minifilament following flux cancellation at a magnetic neutral line. We have tested the veracity of that view by examining 60 random jets in quiet regions and coronal holes using multithermal (304, 171, 193, and 211 Å) extreme ultraviolet images from the Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly and line-of-sight magnetograms from the SDO/Helioseismic and Magnetic Imager. By examining the structure and changes in the magnetic field before, during, and after jet onset, we found that 85% of these jets resulted from a minifilament eruption triggered by flux cancellation at the neutral line. The 60 jets have a mean base diameter of 8800 ± 3100 km and a mean duration of 9 ± 3.6 minutes. These observations confirm that minifilament eruption is the driver and magnetic flux cancellation is the primary trigger mechanism for most coronal hole and quiet region coronal jets.
Hochschild cohomology of multi-extension zero algebras

The main objective of this paper is to present a theory for computing the Hochschild cohomology of algebras built on a specific data, namely multi-extension algebras. The computation relies on cohomological functors evaluated on the data, and on the combinatorics of an ad hoc quiver. One-point extensions are occurrences of this theory, and Happel's long exact sequence is a particular case of the long exact sequence of cohomology that we obtain via the study of trajectories of the quiver. We introduce cohomology along paths, and we compute it under suitable Tor vanishing hypotheses. The cup product on Hochschild cohomology enables us to describe the connecting homomorphism of the long exact sequence. Multi-extension algebras built on the round trip quiver provide square matrix algebras which have two algebras on the diagonal and two bimodules on the corners. If the bimodules are projective, we show that a five-term exact sequences arises. If the bimodules are free of rank one, we provide a complete computation of the Hochschild cohomology. On the other hand, if the corner bimodules are projective without producing new cycles in the data, Hochschild cohomology is that of the product of the algebras on the diagonal for large enough degrees. The word \emph{algebra} always means an associative $k$-algebra over a field $k$; an algebra is not necessarily finite dimensional.
APMP key comparison for the measurement of air kerma for 60Co (APMP.RI(I)-K1.1)

The results are reported for an APMP.R(I)-K1.1 comparison that extends the regional comparison of standards for air kerma APMP.R(I)-K1 to several laboratories unable to participate earlier. The comparison was conducted with the goal of supporting the relevant calibration and measurement capabilities (CMCs) planned for publication by the participant laboratories. The comparison was conducted by the pilot laboratory, the Australian Radiation Protection and Nuclear Safety (ARPANSA), Australia, supported by the Institute of Nuclear Energy Research (INER), Taiwan, in a modified ring-shaped arrangement from September 2009 to November 2010, in parallel with an APMP.R(I)-K4 comparison being piloted by the INER. The laboratories that took part in the comparison were the ARPANSA, the Centre of Technology of Radiation Safety and Metrology (PTKMR-BATAN), Indonesia, the Division of Radiation and Medical Devices (DMSC), Thailand, the INER, the National Centre for Radiation Science (NCRS), New Zealand, the National Institute for Standards (NIS), Egypt and the National Metrology Institute of Japan (NMIJ/AIST), Japan. The two primary laboratories, ARPANSA and NMIJ, were chosen as the linking laboratories. Three ionization chambers were used as transfer instruments to be calibrated in terms of air kerma in 60Co radiotherapy beams. The comparison result is based on the ratio between the air kerma calibration coefficients (NK ) determined by the participants and the mean of the results of the linking laboratories. The mean comparison ratio was found to be within 0.5 % of the key comparison reference value KCRV. The largest deviation between any two comparison ratios for the three chambers in terms of air kerma was 2.0 %. An analysis of the participant uncertainty budgets enabled the calculation of degrees of equivalence (DoE) in terms of the deviations of the results and their associated uncertainties. As a result of this APMP comparison, the BIPM key comparison database (KCDB) should include three new entries since neither the PTKMR-BATAN nor the NCRS have yet been declared designated institutes and consequently their results cannot be entered. Main text To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm. org/. The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Theoretical results and applications related to dimension reduction

ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my advisor, Professor Xiaoming Huo for his great help and support during my graduate studies. His patience and insight make it a pleasure to work with him. Thanks are also due to other members of am also very grateful to my parents, Jiqiu Chen and Wenhui Liu, for their love and support over these four and a half years. Finally, I most cordially thank my husband, Jin Liu, who has been a tremendous source of encouragement in the past several years. Without him, the life in the U.S. would have been more tough and pale.
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world. We introduce the Kalman variational auto-encoder, a framework for unsupervised learning of sequential data that disentangles two latent representations: an object's representation, coming from a recognition model, and a latent state describing its dynamics. As a result, the evolution of the world can be imagined and missing data imputed, both without the need to generate high dimensional frames at each time step. The model is trained end-to-end on videos of a variety of simulated physical systems, and outperforms competing methods in generative and missing data imputation tasks.
Constraint satisfaction problems : backtrack search revisited

Many backtrack search algorithms has been designed over the last years to solve constraint satisfaction problems. Among them, Forward Checking (FC) and Maintaining Arc Consistency (MAC) algorithms are the most popular and studied algorithms. In This work, such algorithms are revisited and extensively compared giving rise to interesting characterization of their efficiency with respect to random instances. More precisely, we provide experimental evidence that FC outperforms MAC on hard CSPs with high graph density and low constraint tightness whereas MAC is better on hard CSPs with low density and high constraints tightness. This results show that on some CSPs maintaining full arc consistency during search might be time consuming. Then, we propose a new generic approach that maintain partial and parameterizable form of local consistency.
Under review as a conference paper at ICLR 2020 tional Random Field ( CRF ) optimization model for mesh segmentation

We address the problem of learning to discover 3D parts for objects in unseen categories. Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches. Formulated as a contextual bandit problem, we propose a learningbased iterative grouping framework which learns a grouping policy to progressively group small part proposals into bigger ones in a bottom-up fashion. At the core of our approach is to restrict the local context for extracting part-level features, which encourages the generalizability to unseen categories. On a recently proposed large-scale fine-grained 3D part dataset, PartNet, we demonstrate that our method can transfer knowledge of parts learned from 3 training categories to 21 unseen testing categories without seeing any annotated samples. Quantitative comparisons against four strong shape segmentation baselines shows that we achieve the state-of-the-art performance.
Clinical features and outcomes of hospitalized COVID-19 patients in a low burden region

Eleni Mylona (  mylonaelena@gmail.com ) 5th Department of Internal Medicine and Infectious Diseases, Evaggelismos Hospital, Athens, Greece Evangelia Margellou 1st Department of Internal Medicine, Evaggelismos Hospital, Athens, Greece Eleftheria Kranidioti 3d Department of Internal Medicine, Evaggelismos Hospital, Athens, Greece Vasilios Vlachakos 1st Department of Critical Care and Pulmonary Medicine, National and Kapodistrian University of Athens, Evaggelismos Hospital, Athens, Greece Vana Sypsa Department of Hygiene, Epidemiology & Medical Statistics, Medical School, National and Kapodistrian University of Athens, Athens, Greece Vissaria Sakka 3rd Department of Internal Medicine and Infectious Diseases, Red Cross Hospital, Athens, Greece, Evangelos Balis Department of Pulmonary Medicine, Evaggelismos Hospital, Athens, Greece Ioannis Kalomenidis 1st Department of Critical Care and Pulmonary Medicine, National and Kapodistrian University of Athens, Evaggelismos Hospital, Athens, Greece.
Insensitizing control for linear and semi-linear heat equations with partially unknown domain

We consider a semi-linear heat equation with Dirichlet boundary conditions and globally Lipschitz nonlinearity, posed on a bounded domain of ℝN (N ∈ ℕ*), assumed to be an unknown perturbation of a reference domain. We are interested in an insensitizing control problem, which consists in finding a distributed control such that some functional of the state is insensitive at the first order to the perturbations of the domain. Our first result consists of an approximate insensitization property on the semi-linear heat equation. It rests upon a linearization procedure together with the use of an appropriate fixed point theorem. For the linear case, an appropriate duality theory is developed, so that the problem can be seen as a consequence of well-known unique continuation theorems. Our second result is specific to the linear case. We show a property of exact insensitization for some families of deformation given by one or two parameters. Due to the nonlinearity of the intrinsic control problem, no duality theory is available, so that our proof relies on a geometrical approach and direct computations.