Miracle-dz/UniFAR-FTUs huggingface dataset:
subunits of NF-kappa B: a mechanism for cytoplasmic retention. I kappa B interacts with the nuclear localization sequences of the

NF-~B is an inducible transcription factor comprised of a 50-kD (pS0) and a 65-kD (p65) subunit. Induction of NF-KB activity, which is a critical event in many signal transduction pathways, involves release from a cytoplasmic inhibitory protein, IKB, followed by translocation of the active transcription factor complex into the nucleus. Earlier studies suggested that IKB targets the p65 subunit of NF-KB. However, we demonstrate by in vitro and in vivo methods that the recently cloned IKB/MAD-3 interacts with both the p50 and p65 subunits of NF-KB, as well as c-Rel. Furthermore, an alternatively spliced, dimerization-deficient transforming variant of p65 (p65A) interacts extremely weakly with IKB/MAD-3, suggesting that dimerization is important for interaction. We demonstrate that the conserved nuclear localization sequences (NLSs) of NF-KB and c-Rel are the targets for IKB/MAD-3 interaction. Indirect immunofluorescence experiments demonstrate that IKB/MAD-3 expression retains both p65 and p50 in the cytoplasm. Furthermore, and most important, a p65 that contains an SV40 large T antigen NLS in addition to its own NLS is no longer retained in the cytoplasm in the presence of IKB/MAD-3. We propose that IKB/MAD-3 masks the NLSs of NF-KB and c-Rel and that this constitutes the mechanism for cytoplasmic retention of these proteins.
Evaluation of left atrial function in asymptomatic dogs with chronic myxomatous mitral valve disease: two- dimensional feature-tracking echocardiography and Simpson’s monoplanar methods1

RESUMO. [Avaliação da função atrial esquerda em cães assintomáticos com degeneração mixomatosa crônica de valva mitral: métodos ecocardiográficos bidimensional feature tracking e monoplanar de Simpson]. O presente estudo avaliou o volume e a função atrial esquerda obtidos por meio da ecocardiografia bidimensional feature tracking ABSTRACT.Coelho M.R. , Muzzi R.A. L. , Dorneles E.M.S., Pereira G.G. , Cabral R.M. , Muzzi L.A. L. , Abreu C.B. & Oliveira L.E. D. 2020. Evaluation of left atrial function in asymptomatic dogs with chronic myxomatous mitral valve disease: two-dimensional feature-tracking echocardiography and Simpson’s monoplanar methods. Pesquisa Veterinária Brasileira 40(3):188-196. Departamento de Medicina Veterinária, Universidade Federal de Lavras, Av. Doutor Sylvio Menicucci 1001, Cx. Postal 3037, Lavras, MG 37200-000, Brazil. E-mail: marianacoelhorc@gmail.com The present study evaluated the volume and function of the left atrium by two-dimensional echocardiographic feature-tracking imaging (2D-FTI) and Simpson’s monoplanar modeling in dogs with asymptomatic degenerative mitral valve disease (DMVD). The study consisted of 80 dogs that were divided into the following three groups: Group 1, 21 dogs (A); Group 2, 30 dogs (B1) and Group 3, 29 dogs (B2). The variable strain (contraction phase) was significantly lower in Group 3 than in Group 1 (12.92±4.54 x 16.69±5.74, p=0.014), and significant differences in the contraction strain index (CSI) were observed between all of the groups that were evaluated (1 = 46.82±8.10, 2 = 39.88±8.03, 3 = 35.25±5.64, p<0.0001). The atrial diastolic volume index (AdVi) that was measured by 2D-FTI was significantly higher in Group 3 than in Group 1 (1.31±0.95 x 0.96±0.31, p=0.038), and the atrial cardiac index (ACI) was also higher in Group 3 than in Group 1 (102.38±80.18 x 78.19±33.38, p=0.030). Atrial function was assessed by Simpson’s monoplanar method, which demonstrated an increase in the left atrial systolic volume, while the contractile function decreased with an increasing disease severity (Group 1 0.21±0.06; Group 2 0.25±0.06; Group 3 0.32±0.08, p<0.0001). The intraobserver and interobserver assessments showed low to moderate variability; most of the values for the coefficient of variation for the variables that were analysed with each method were below 25%. Thus, DMVD was determined to cause an alteration in atrial function, especially in the contraction phase, and even in asymptomatic animals, and the methods of 2D-FTI echocardiography and Simpson’s monoplanar evaluation are sensitive and early methods for the detection of left atrial dysfunction.
Effect of fertilizer rate and water irrigation quality on the recovery of 15N-labeled fertilizer applied to Sudangrass

– Wastewaters are increasingly used for irrigation of cropping systems in Tunisia. However, to develop environmentally sound practices the contribution of wastewater to crop N nutrition needs to be clarified, especially in cropping systems already receiving mineral fertilizers. For a better understanding of the interaction between fertilizer N and N originating from wastewater, experiments using 15 N were conducted. 15 N-labeled fertilizer was applied at different rates (0, 60, 100 and 140 kg N·ha –1 ) and with different water irrigation qualities (tap water or treated wastewater) to sorghum grown in lysimeters during 1998 and 1999. Recovery of 15 N-labeled fertilizer in the above-ground crop at final harvest in treated wastewater irrigation was higher at the lowest rate of fertilizer application (54%), with the amount recovered in the crop increasing as the rate of 15 N-labeled fertilizer application increased up to the rate of 100 kg N·ha –1 . Nevertheless, in spite of this increase in 15 N-labeled fertilizer in the crop, total plant N uptake did not differ between rates. Treated wastewater irrigation had no negative effect on the recovery of 15 N-labeled fertilizer. About 62 and 55% of 15 N-labeled fertilizer was removed by Sudangrass in either tap water or treated wastewater. Neither fertilizer N rate nor water quality had an effect on the 15 N-labeled fertilizer remaining in the soil at final harvest. On average 20% in the wastewater treatment (19–24%) and 30% in the tap water treatment (26–31%) of the 15 N fertilizer applied were in the 0–60 cm layer of soil at final harvest in 1998 and 1999, respectively, and mostly present in the 0–20 cm layer. The proportion of applied 15 N-labeled fertilizer remaining in the soil at final harvest increased with increasing N rates. About 60, 69 and 72% of 15 N left in the soil at final harvest was in the surface 0–20 cm layer. Residual 15 N was greatly higher in soil following the first harvest than after the final harvest, with the greatest value (38%) measured at the lowest rate of 15 N-labeled fertilizer (30 kg N·ha –1 ). Losses of 15 N-labeled fertilizer increased with application rate, but were unaffected by water quality irrigation. Approximately 13% of the applied 15 N fertilizer was lost following the application of 100 kg N·ha –1 with either treated wastewater or tap water irrigation.
ReLSO: A Transformer-based Model for Latent Space Optimization and Generation of Proteins

The development of powerful natural language models have increased the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution, and next-generation sequencing have allowed for the accumulation of large amounts of labeled fitness data. Leveraging these two trends, we introduce Regularized Latent Space Optimization (ReLSO), a deep transformer-based autoencoder which features a highly structured latent space that is trained to jointly generate sequences as well as predict fitness. Through regularized prediction heads, ReLSO introduces a powerful protein sequence encoder and novel approach for efficient fitness landscape traversal. Using ReLSO, we explicitly model the sequence-function landscape of large labeled datasets and generate new molecules by optimizing within the latent space using gradient-based methods. We evaluate this approach on several publicly-available protein datasets, including variant sets of anti-ranibizumab and GFP. We observe a greater sequence optimization efficiency (increase in fitness per optimization step) by ReLSO compared to other approaches, where ReLSO more robustly generates high-fitness sequences. Furthermore, the attention-based relationships learned by the jointly-trained ReLSO models provides a potential avenue towards sequence-level fitness attribution information.
Stability of multiclass spatial birth-and-death processes with wireless-type interactions

This paper studies multiclass spatial birth-and-death (SBD) processes on a compact region of the Euclidean plane R 2 modeling wireless interactions as seen in telecommunication networks: the arrival rate of users is constant and the departure rate is inversely proportional to a shot noise created by interfering users in the network. The novelty of this work lies in the addition of service diﬀerentiation, inspired by bandwidth partitioning introduced in the latest generation of wireless networks. In this setup, users choose a ﬁxed number of transmission channels, and only interfere with transmissions on the channels they use. We restrict our study to symmetric networks, where users transmitting on the same number of bands have the same stochastic properties in the network. We deﬁne a general mathematical framework using stochastic geometry and queuing theory tools to study this category of processes and we prove the existence of a critical arrival rate above which the system is always unstable. We then introduce a discretization of the dynamics to reduce the study of stability to which of a queuing network in a countable state space. We then ﬁnd a closed form the critical arrival rate using ﬂuid limits arguments. In a second part, we deﬁne two heuristics to estimate the steady-state densities of users in the network. The ﬁrst one relies on a Poisson approximation of the steady-states processes and allows us to deﬁne a heuristic for the critical arrival rate of the network. The second heuristic uses a cavity approximation and a second-order approximation to obtain a better heuristic for the steady-state user density.
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting

We present a new method that includes three key components of distributed optimization and federated learning: variance reduction of stochastic gradients, partial participation, and compressed communication. We prove that the new method has optimal oracle complexity and state-of-the-art communication complexity in the partial participation setting. Regardless of the communication compression feature, our method successfully combines variance reduction and partial participation: we get the optimal oracle complexity, never need the participation of all nodes, and do not require the bounded gradients (dissimilarity) assumption.
VC Theoretical Explanation of Double Descent

There has been growing interest in generalization performance of large multilayer neural networks that can be trained to achieve zero training error, while generalizing well on test data. This regime is known as 'second descent' and it appears to contradict the conventional view that optimal model complexity should reflect an optimal balance between underfitting and overfitting, i.e., the bias-variance trade-off. This paper presents a VC-theoretical analysis of double descent and shows that it can be fully explained by classical VC-generalization bounds. We illustrate an application of analytic VC-bounds for modeling double descent for classification, using empirical results for several learning methods, such as SVM, Least Squares, and Multilayer Perceptron classifiers. In addition, we discuss several reasons for the misinterpretation of VC-theoretical results in Deep Learning community.
On the Potential of Sequential and Nonsequential Regression Models for Sentinel-1-Based Biomass Prediction in Tanzanian Miombo Forests

This study derives regression models for aboveground biomass (AGB) estimation in miombo woodlands of Tanzania that utilize the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restricts their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions based on airborne laser scanning (ALS) data as a surrogate response variable for SAR data. This dramatically increases the available training data and opens for flexible regression models that capture fine-scale AGB dynamics. This becomes a sequential modeling approach, where the first regression stage has linked in situ data to ALS data and produced the AGB prediction map; we perform the subsequent stage, where this map is related to Sentinel-1 data. We develop a traditional, parametric regression model and alternative nonparametric models for this stage. The latter uses a conditional generative adversarial network (cGAN) to translate Sentinel-1 images into ALS-based AGB prediction maps. The convolution filters in the neural networks make them contextual. We compare the sequential models to traditional, nonsequential regression models, all trained on limited AGB ground reference data. Results show that our newly proposed nonsequential Sentinel-1-based regression model performs better quantitatively than the sequential models, but achieves less sensitivity to fine-scale AGB dynamics. The contextual cGAN-based sequential models best reproduce the distribution of ALS-based AGB predictions. They also reach a lower RMSE against in situ AGB data than the parametric sequential model, indicating a potential for further development.
Hierarchically Constrained Adaptive Ad Exposure in Feeds

A contemporary feed application usually provides blended results of organic items and sponsored items~(ads) to users. Conventionally, ads are exposed at fixed positions. Such a fixed ad exposure strategy is inefficient due to ignoring users' personalized preferences towards ads. To this end,adaptive ad exposure is becoming an appealing strategy to boost the overall performance of the feed. However, existing approaches to implement the adaptive ad exposure strategy suffer from several limitations: 1) they usually fall into sub-optimal solutions because of only focusing on request-level optimization without consideration of the application-level performance and constraints, 2) they neglect the necessity of keeping the game-theoretical properties of ad auctions, and 3) they can hardly be deployed in large-scale applications due to high computational complexity. In this paper, we focus on the application-level performance optimization under hierarchical constraints in feeds and formulate adaptive ad exposure as a Dynamic Knapsack Problem. We propose Hierarchically Constrained Adaptive Ad Exposure~(HCA2E) that possesses the desirable game-theoretical properties, computational efficiency, and performance robustness. Comprehensive offline and online experiments on a leading e-commerce application demonstrate the performance superiority of HCA2E.
EMS: Efficient and Effective Massively Multilingual Sentence Representation Learning

Massively multilingual sentence representation models, e.g., LASER, SBERT-distill, and LaBSE, help significantly improve cross-lingual downstream tasks. However, multiple training procedures, the use of a large amount of data, or inefficient model architectures result in heavy computation to train a new model according to our preferred languages and domains. To resolve this issue, we introduce efficient and effective massively multilingual sentence representation learning (EMS), using cross-lingual sentence reconstruction (XTR) and sentence-level contrastive learning as training objectives. Compared with related studies, the proposed model can be efficiently trained using significantly fewer parallel sentences and GPU computation resources without depending on large-scale pre-trained models. Empirical results show that the proposed model significantly yields better or comparable results with regard to bi-text mining, zero-shot cross-lingual genre classification, and sentiment classification. Ablative analyses demonstrate the effectiveness of each component of the proposed model. We release the codes for model training and the EMS pre-trained model, which supports 62 languages (https://github.com/Mao-KU/EMS).
Variational inference via Wasserstein gradient flows

Along with Markov chain Monte Carlo (MCMC) methods, variational inference (VI) has emerged as a central computational approach to large-scale Bayesian inference. Rather than sampling from the true posterior $\pi$, VI aims at producing a simple but effective approximation $\hat \pi$ to $\pi$ for which summary statistics are easy to compute. However, unlike the well-studied MCMC methodology, algorithmic guarantees for VI are still relatively less well-understood. In this work, we propose principled methods for VI, in which $\hat \pi$ is taken to be a Gaussian or a mixture of Gaussians, which rest upon the theory of gradient flows on the Bures--Wasserstein space of Gaussian measures. Akin to MCMC, it comes with strong theoretical guarantees when $\pi$ is log-concave.
Exact Feature Collisions in Neural Networks

Predictions made by deep neural networks were shown to be highly sensitive to small changes made in the input space where such maliciously crafted data points containing small perturbations are being referred to as adversarial examples. On the other hand, recent research suggests that the same networks can also be extremely insensitive to changes of large magnitude, where predictions of two largely different data points can be mapped to approximately the same output. In such cases, features of two data points are said to approximately collide, thus leading to the largely similar predictions. Our results improve and extend the work of Li et al.(2019), laying out theoretical grounds for the data points that have colluding features from the perspective of weights of neural networks, revealing that neural networks not only suffer from features that approximately collide but also suffer from features that exactly collide. We identify the necessary conditions for the existence of such scenarios, hereby investigating a large number of DNNs that have been used to solve various computer vision problems. Furthermore, we propose the Null-space search, a numerical approach that does not rely on heuristics, to create data points with colliding features for any input and for any task, including, but not limited to, classification, localization, and segmentation.
CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation. Its application to video generation is still facing many challenges: The potential huge computation cost makes the training from scratch unaffordable; The scarcity and weak relevance of text-video datasets hinder the model understanding complex movement semantics. In this work, we present 9B-parameter transformer CogVideo, trained by inheriting a pretrained text-to-image model, CogView2. We also propose multi-frame-rate hierarchical training strategy to better align text and video clips. As (probably) the first open-source large-scale pretrained text-to-video model, CogVideo outperforms all publicly available models at a large margin in machine and human evaluations.
Provable General Function Class Representation Learning in Multitask Bandits and MDPs

While multitask representation learning has become a popular approach in reinforcement learning (RL) to boost the sample efficiency, the theoretical understanding of why and how it works is still limited. Most previous analytical works could only assume that the representation function is already known to the agent or from linear function class, since analyzing general function class representation encounters non-trivial technical obstacles such as generalization guarantee, formulation of confidence bound in abstract function space, etc. However, linear-case analysis heavily relies on the particularity of linear function class, while real-world practice usually adopts general non-linear representation functions like neural networks. This significantly reduces its applicability. In this work, we extend the analysis to general function class representations. Specifically, we consider an agent playing $M$ contextual bandits (or MDPs) concurrently and extracting a shared representation function $\phi$ from a specific function class $\Phi$ using our proposed Generalized Functional Upper Confidence Bound algorithm (GFUCB). We theoretically validate the benefit of multitask representation learning within general function class for bandits and linear MDP for the first time. Lastly, we conduct experiments to demonstrate the effectiveness of our algorithm with neural net representation.
Evaluating Robustness to Dataset Shift via Parametric Robustness Sets

We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance. These shifts are defined via parametric changes in the causal mechanisms of observed variables, where constraints on parameters yield a"robustness set"of plausible distributions and a corresponding worst-case loss over the set. While the loss under an individual parametric shift can be estimated via reweighting techniques such as importance sampling, the resulting worst-case optimization problem is non-convex, and the estimate may suffer from large variance. For small shifts, however, we can construct a local second-order approximation to the loss under shift and cast the problem of finding a worst-case shift as a particular non-convex quadratic optimization problem, for which efficient algorithms are available. We demonstrate that this second-order approximation can be estimated directly for shifts in conditional exponential family models, and we bound the approximation error. We apply our approach to a computer vision task (classifying gender from images), revealing sensitivity to shifts in non-causal attributes.
Normalized Latent Measure Factor Models

We propose a methodology for modeling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalized random measures, we consider a prior distribution for a collection of discrete random measures where each measure is a linear combination of a set of latent measures, interpretable as characteristic traits shared by different distributions, with positive random weights. The model is non-identified and a method for post-processing posterior samples to achieve identified inference is developed. This uses Riemannian optimization to solve a non-trivial optimization problem over a Lie group of matrices. The effectiveness of our approach is validated on simulated data and in two applications to two real-world data sets: school student test scores and personal incomes in California. Our approach leads to interesting insights for populations and easily interpretable posterior inference
A Reduction to Binary Approach for Debiasing Multiclass Datasets

We propose a novel reduction-to-binary (R2B) approach that enforces demographic parity for multiclass classification with non-binary sensitive attributes via a reduction to a sequence of binary debiasing tasks. We prove that R2B satisfies optimality and bias guarantees and demonstrate empirically that it can lead to an improvement over two baselines: (1) treating multiclass problems as multi-label by debiasing labels independently and (2) transforming the features instead of the labels. Surprisingly, we also demonstrate that independent label debiasing yields competitive results in most (but not all) settings. We validate these conclusions on synthetic and real-world datasets from social science, computer vision, and healthcare.