Miracle-dz/UniFAR-FTUs huggingface dataset:
The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions

Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institution variation, such as multiple scanner types. While simply training on the combined datasets is fraught with data privacy limitations, fine-tuning the model on subsequent institutions after training it on the original institution results in a decrease in performance on the original dataset, a phenomenon called catastrophic forgetting. In this paper, we investigate trade-off between model refinement and retention of previously learned knowledge and subsequently address catastrophic forgetting for the assessment of mammographic breast density. More specifically, we propose a simple yet effective approach, adapting Elastic weight consolidation (EWC) using the global batch normalization (BN) statistics of the original dataset. The results of this study provide guidance for the deployment of clinical deep learning models where continuous learning is needed for domain expansion.
Revealing PAK2's Function in the Cell Division through MKLP1's Interactome

Cell division-related proteins are essential for the normal development and differentiation of cells and may be related to the occurrence of cancer and the drug resistance mechanism of cancer cells. The mitotic kinesin-like protein 1 (MKLP1) is a kinesin protein that has been involved in the assembly of the midzone/midbody during mitosis and cytokinesis. In this study, we found that the tail domain of MKLP1 exhibited an autoinhibitory effect on its motor activity. Overexpression of the tail domain in HEK293 cells blocked cytokinesis and caused bi-/multinucleation. It is possible that protein binding to the MKLP1 tail relieves this autoinhibition and induces the motility of MKLP1. We used the GST pull-down assay followed by the LC-MS/MS analysis and identified 54 MKLP1 tail domain-specific binding proteins. Further, we confirmed the MS result by coimmunoprecipitation and FRET that a serine/threonine kinase, p21-activated kinase 2 (PAK2), binding to MKLP1. Endogenous PAK2 expression was found to be identical to that of MKLP1 in HEK293 cells during cytokinesis. Finally, functional studies indicated that when PAK2 expression was downregulated by siRNA, MKLP1 underwent a change in its localization away from the midbody, and cell cytokinesis was subsequently impeded. This study presents a novel regulatory mechanism that PAK2 promotes the activation of MKLP1 and contributes to complete cell cytokinesis.
RNA interference as an alternative preventive measure for avian influenza in poultry

RNA INTERFERENCE AS AN ALTERNATIVE PREVENTIVE MEASURE FOR AVIAN INFLUENZA IN POULTRY Avian influenza virus (AIV) is a viral pathogen that causes a wide range of disease in poultry, from subclinical to severe clinical illness and can often result in death. In 1878, AIV was first described as a disease affecting poultry. Nearly 80 years later this disease-causing agent was identified as influenza A virus and a member of the family Orthomyxoviridae. AIV was not considered a significant human pathogen until 1997, when high pathogenic AIV H5N1 emerged from the wildfowl reservoir and was directly transmitted from domestic poultry to humans. Despite a long history of outbreaks in animals, this incident propelled AIV into a globally recognized disease associated with socioeconomic and animal health consequences. Each AIV outbreak highlights ways to improve upon current control strategies and stimulates new ideas for developing novel approaches and technologies to better mitigate AIV outbreaks worldwide. AIV is a dynamic pathogen to study. Host range and adaptation, pathogenicity, pathology, molecular composition, and the epidemiology of AIV all make this virus particularly challenging to control in poultry. Vaccines against AIV are available but the protection they provide for poultry is limited, especially when administered at the onset or in the midst of an
Direct Classification of Emotional Intensity

In this paper, we present a model that can directly predict emotion intensity score from video inputs, instead of deriving from action units. Using a 3d DNN incorporated with dynamic emotion information, we train a model using videos of different people smiling that outputs an intensity score from 0-10. Each video is labeled framewise using a normalized action-unit based intensity score. Our model then employs an adaptive learning technique to improve performance when dealing with new subjects. Compared to other models, our model excels in generalization between different people as well as provides a new framework to directly classify emotional intensity.
Cycle-Consistent Generative Rendering for 2D-3D Modality Translation

For humans, visual understanding is inherently generative: given a 3D shape, we can postulate how it would look in the world; given a 2D image, we can infer the 3D structure that likely gave rise to it. We can thus translate between the 2D visual and 3D structural modalities of a given object. In the context of computer vision, this corresponds to a learnable module that serves two purposes: (i) generate a realistic rendering of a 3D object (shape-toimage translation) and (ii) infer a realistic 3D shape from an image (image-to-shape translation). In this paper, we learn such a module while being conscious of the difficulties in obtaining large paired 2D-3D datasets. By leveraging generative domain translation methods, we are able to define a learning algorithm that requires only weak supervision, with unpaired data. The resulting model is not only able to perform 3D shape, pose, and texture inference from 2D images, but can also generate novel textured 3D shapes and renders, similar to a graphics pipeline. More specifically, our method (i) infers an explicit 3D mesh representation, (ii) utilizes example shapes to regularize inference, (iii) requires only an image mask (no keypoints or camera extrinsics), and (iv) has generative capabilities. While prior work explores subsets of these properties, their combination is novel. We demonstrate the utility of our learned representation, as well as its performance on image generation and unpaired 3D shape inference tasks.
Towards Map-Based Validation of Semantic Segmentation Masks

Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness. We propose to validate machine learning models for self-driving vehicles not only with given ground truth labels, but also with additional a-priori knowledge. In particular, we suggest to validate the drivable area in semantic segmentation masks using given street map data. We present first results, which indicate that prediction errors can be uncovered by map-based validation.
The Effect Analysis of Carrier Gas Flow Rate on the Rapid Gas Chromatographic Separation System

Odor pollution did not only disturb the human normal life but also aroused the attention of environmental researchers and environmental protection departments. Therefore, the research on odor detecting method and instrument is important to theory and application. On this basis, the self-developed microfluidic chip capillary column is used in our odor detecting system. In this paper, lead the chip column into the chromatography separation system, with its small size, high efficiency, easy integration, and other characteristics to replace the original traditional column. The chip column was used in many gas experiments for several typical VOCs. At different carrier gas flow rates, the baseline value, toluene response, and toluene and methyl sulfide mixed gas separation were compared to verify the experiment to determine the optimal carrier gas flow rate in accordance with its response and separation degree. Under the premise of ensuring column efficiency as high as possible, it is determined that the optimal carrier gas flow rate is 6 ml/min. This paper shows the most proper carrier gas flow rate of our odor detecting system with the self-developed microfluidic chip capillary column.
Analyzing ‘Near Me’ Services: Potential for Exposure Bias in Location-based Retrieval

The proliferation of smartphones has led to the increased popularity of location-based search and recommendation systems. Online platforms like Google and Yelp allow location-based search in the form of a nearby feature to query for hotels or restaurants in the vicinity. Moreover, hotel booking platforms like Booking[dot]com, Expedia, or Trivago allow travelers searching for accommodations using either their desired location as a search query or near a particular landmark. Since the popularity of different locations in a city varies, certain locations may get more queries than other locations. Thus, the exposure received by different establishments at these locations may be very different from their intrinsic quality as captured in their ratings.Today, many small businesses (shops, hotels, or restaurants) rely on such online platforms for attracting customers. Thus, receiving less exposure than what is expected can be unfavorable for businesses. It could have a negative impact on their revenue and potentially lead to economic starvation or even shutdown. By gathering and analyzing data from three popular platforms, we observe that many top-rated hotels and restaurants get less exposure vis-a-vis their quality, which could be detrimental for them. Following a meritocratic notion, we define and quantify such exposure disparity due to location-based searches on these platforms. We attribute this exposure disparity mainly to two kinds of biases — Popularity Bias and Position Bias. Our experimental evaluation on multiple datasets reveals that although the platforms are doing well in delivering distance-based results, exposure disparity exists for individual businesses and needs to be reduced for business sustainability.
Pairing function of visual binary stars

An all-sky sample of 1227 visual binaries based on Washington Double Star catalogue is constructed to infer the IMF, mass ratio, and projected distance distribution with a dedicated population synthesis model. Parallaxes from Gaia DR2 and Hipparcos are used to verify the distance distribution. The model is validated on the single-star Tycho-2 sample and successfully reproduces the observed magnitudes and angular separations. The projected separation distribution follows $f(s)\sim s^{-1.2}$ in $10^2 - 2\cdot10^3$ AU range for 1--4.5 $m_{\odot}$ primary stars. Several algorithms are explored as pairing functions. Random pairing is confidently rejected. Primary-constrained (PCP) and split-core pairing (SCP), the scenarios adopting primary component's or total system's mass as fundamental, are considered. The preferred IMF slope is $\alpha \sim 2.8$ either way. A simple power-law mass ratio distribution is unlikely, but the introduction of a twin excess provides a favourable result. PCP with $f(q) \sim q^{-1}$ is preferred with a tiny twin fraction, models with $f(q) \sim q^{-1.5}$ are acceptable when a larger twin excess is allowed. SCP is similar to PCP when a larger slope of the power law is adopted: $f(q)\sim q^{\beta+0.7}$.
Tools for modelling and simulating the Smart Grid

The Smart Grid (SG) is a Cyber-Physical System (CPS) considered a critical infrastructure divided into cyber (software) and physical (hardware) counterparts that complement each other. It is responsible for timely power provision wrapped by Information and Communication Technologies (ICT) for handling bi-directional energy flows in electric power grids. Enacting control and performance over the massive infrastructure of the SG requires convenient analysis methods. Modelling and simulation (M&S) is a performance evaluation technique used to study virtually any system by testing designs and artificially creating 'what-if' scenarios for system reasoning and advanced analysis. M&S preserves stressing the actual physical infrastructure and systems in production by addressing the problem in a computational perspective. Present work compiles a non-exhaustive list of tools for M&S of interest when tackling SG capabilities. Our contribution is to delineate available options for modellers when modelling power systems in combination with ICT. We also show the auxiliary tools and details of most relevant solutions pointing out major features and combinations over the years.
Iterative Self-Learning for Enhanced Back-Translation in Low Resource Neural Machine Translation

Many language pairs are low resource - the amount and/or quality of parallel data is not sufficient to train a neural machine translation (NMT) model which can reach an acceptable standard of accuracy. Many works have explored the use of the easier-to-get monolingual data to improve the performance of translation models in this category of languages - and even high resource languages. The most successful of such works is the back-translation - using the translations of the target language monolingual data to increase the amount of the training data. The quality of the backward model - trained on the available parallel data - has been shown to determine the performance of the back-translation approach. Many approaches have been explored to improve the performance of this model especially in low resource languages where the amount of parallel data is not sufficient to train an acceptable backward model. Among such works are the use of self-learning and the iterative back-translation. These methods were shown to perform better than the standard back-translation. This work presents the iterative self-training approach as an improvement over the self-learning approach to further enhance the performance of the backward model. Over several iterations, the synthetic data generated by the backward model is used to improve its performance through forward translation. Experiments have shown that the method outperforms both the standard back-translation and self-learning approach on IWSLT'14 English German low resource NMT. While the method also outperforms the iterative back-translation, though slightly, the number of models required to be trained is reduced exactly by the number of iterations.
Discovery of the Hidden State in Ionic Models Using a Domain-Specific Recurrent Neural Network

Ionic models, the set of ordinary differential equations (ODEs) describing the time evolution of the state of excitable cells, are the cornerstone of modeling in neuro- and cardiac electrophysiology. Modern ionic models can have tens of state variables and hundreds of tunable parameters. Fitting ionic models to experimental data, which usually covers only a limited subset of state variables, remains a challenging problem. In this paper, we describe a recurrent neural network architecture designed specifically to encode ionic models. The core of the model is a Gating Neural Network (GNN) layer, capturing the dynamics of classic (Hodgkin-Huxley) gating variables. The network is trained in two steps: first, it learns the theoretical model coded in a set of ODEs, and second, it is retrained on experimental data. The retrained network is interpretable, such that its results can be incorporated back into the model ODEs. We tested the GNN networks using simulated ventricular action potential signals and showed that it could deduce physiologically-feasible alterations of ionic currents. Such domain-specific neural networks can be employed in the exploratory phase of data assimilation before further fine-tuning using standard optimization techniques.
Do Offline Metrics Predict Online Performance in Recommender Systems?

Recommender systems operate in an inherently dynamical setting. Past recommendations influence future behavior, including which data points are observed and how user preferences change. However, experimenting in production systems with real user dynamics is often infeasible, and existing simulation-based approaches have limited scale. As a result, many state-of-the-art algorithms are designed to solve supervised learning problems, and progress is judged only by offline metrics. In this work we investigate the extent to which offline metrics predict online performance by evaluating eleven recommenders across six controlled simulated environments. We observe that offline metrics are correlated with online performance over a range of environments. However, improvements in offline metrics lead to diminishing returns in online performance. Furthermore, we observe that the ranking of recommenders varies depending on the amount of initial offline data available. We study the impact of adding exploration strategies, and observe that their effectiveness, when compared to greedy recommendation, is highly dependent on the recommendation algorithm. We provide the environments and recommenders described in this paper as Reclab: an extensible ready-to-use simulation framework at this https URL.
Multi-channel MR Reconstruction (MC-MRRec) Challenge - Comparing Accelerated MR Reconstruction Models and Assessing Their Genereralizability to Datasets Collected with Different Coils

The 2020 Multi-channel Magnetic Resonance Reconstruction (MC-MRRec) Challenge had two primary goals: 1) compare different MR image reconstruction models on a large dataset and 2) assess the generalizability of these models to datasets acquired with a different number of receiver coils (i.e., multiple channels). The challenge had two tracks: Track 01 focused on assessing models trained and tested with 12-channel data. Track 02 focused on assessing models trained with 12-channel data and tested on both 12-channel and 32-channel data. While the challenge is ongoing, here we describe the first edition of the challenge and summarise submissions received prior to 5 September 2020. Track 01 had five baseline models and received four independent submissions. Track 02 had two baseline models and received two independent submissions. This manuscript provides relevant comparative information on the current state-of-the-art of MR reconstruction and highlights the challenges of obtaining generalizable models that are required prior to clinical adoption. Both challenge tracks remain open and will provide an objective performance assessment for future submissions. Subsequent editions of the challenge are proposed to investigate new concepts and strategies, such as the integration of potentially available longitudinal information during the MR reconstruction process. An outline of the proposed second edition of the challenge is presented in this manuscript.
SamWalker++: Recommendation With Informative Sampling Strategy

Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Existing methods address this challenge by treating all the un-observed data as negative (dislike) but downweight the confidence of these data. However, this treatment causes two problems: (1) Confidence weights of the unobserved data are usually assigned manually, which lack flexibility and may create empirical bias on evaluating user's preference. (2) To handle massive volume of the unobserved feedback data, most of the existing methods rely on stochastic inference and data sampling strategies. However, since a user is only aware of a very small fraction of items in a large dataset, it is difficult for existing samplers to select informative training instances in which the user really dislikes the item rather than does not know it. To address the above two problems, we propose two novel recommendation methods SamWalker and SamWalker++ that support both adaptive confidence assignment and efficient model learning. SamWalker models data confidence with a social network-aware function, which can adaptively specify different weights to different data according to users’ social contexts. However, the social network information may not be available in many recommender systems, which hinders application of SamWalker. Thus, we further propose SamWalker++, which does not require any side information and models data confidence with a constructed pseudo-social network. In the pseudo-social network, similar users are connected with specific item nodes or community nodes. This way, the inference of one's data confidence can benefit from the knowledge from other similar users. We also develop fast random-walk-based sampling strategies for our SamWalker and SamWalker++ to adaptively draw informative training instances, which can speed up gradient estimation and reduce sampling variance. Extensive experiments on five real-world datasets demonstrate the superiority of the proposed SamWalker and SamWalker++.
Morphologically Aware Word-Level Translation

We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way. Our model exploits the basic linguistic intuition that the lexeme is the key lexical unit of meaning, while inflectional morphology provides additional syntactic information. This approach leads to substantial performance improvements—19% average improvement in accuracy across 6 language pairs over the state of the art in the supervised setting and 16% in the weakly supervised setting. As another contribution, we highlight issues associated with modern BLI that stem from ignoring inflectional morphology, and propose three suggestions for improving the task.
A tunable magneto-acoustic oscillator with low phase noise

A frequency-tunable low phase noise magneto-acoustic resonator is developed on the base of a parallel-plate straight-edge bilayer consisting of a yttrium-iron garnet (YIG) layer grown on a substrate of a gallium-gadolinium garnet(GGG). When a YIG/GGG sample forms an ideal parallel plate, it supports a series of high-quality-factor acoustic modes standing along the plate thickness. Due to the magnetostriction of the YIG layer the ferromagnetic resonance (FMR) mode of the YIG layer can strongly interact with the acoustic thickness modes of the YIG/GGG structure, when the modes' frequencies match. A particular acoustic thickness mode used for the resonance excitations of the hybrid magneto-acoustic oscillations in a YIG/GGG bilayer is chosen by the YIG layer FMR frequency, which can be tuned by the variation of the external bias magnetic field. A composite magneto-acoustic oscillator, which includes an FMR-based resonance pre-selector, is developed to guarantee satisfaction of the Barkhausen criteria for a single-acoustic-mode oscillation regime. The developed low phase noise composite magneto-acoustic oscillator can be tuned from 0.84 GHz to 1 GHz with an increment of about 4.8 MHz (frequency distance between the adjacent acoustic thickness modes in a YIG/GGG parallel plate), and demonstrates the phase noise of -116 dBc/Hz at the offset frequency of 10 kHz.