Miracle-dz/UniFAR-FTUs huggingface dataset:
Composition of the Fecal Microbiota of Piglets at Various Growth Stages

Gastrointestinal (GI) microbiota play an important role in promoting growth in piglets. However, studies on microbiota composition at various growth stages are lacking. We measured body weights of Jinfen White and Mashen piglets every 7 days and collected their fecal samples by rectal swabbing at nine time points during suckling (1–28 days) and nursery (35–70 days) stages to gain insight into microbiota variability during piglet growth. The fecal microbiota were characterized via 16S rRNA gene sequencing to analyze the effects of microbial diversity on piglet growth and development preliminarily. The results showed that although the two breeds of piglets have similar body weights at birth, weaned Jinfen White piglets demonstrated a significantly greater body weight and daily weight gain than weaned Mashen piglets (P < 0.01). A total of 1,976 operational taxonomic units (OTUs) belonging to 27 phyla and 489 genera were uncovered, in which the highest numbers of OTUs belong to the phyla Firmicutes and Bacteroidetes. Lactobacillus, Bacteroides, and Prevotellaceae NK3B31 groups accounting for 12.4, 8.8, and 5.8% of OTUs, respectively, showed relatively high abundance at the genus level. Nine sampling time points were divided into three growth stages, namely, immediate postfarrowing (1 day old), suckling (7, 14, and 21 days old), and nursery (28, 35, 49, 63, and 70 days old), on the basis of the results of microbial diversity, principal coordinate, and co-occurrence network analyses. In addition, it identified 54 discriminative features in the microbiota between two breeds of piglets by LEfSe analysis, in which 17 genera enriched the microbiota community of Jinfen White piglets. Finally, abundances of 29 genera showed significant positive correlations with body weights and daily weight gain of piglets. Conversely, abundances of 12 genera demonstrated significant negative correlations with body weights of piglets. The results of our study will provide a theoretical basis for succession patterns in fecal microbiota of piglets and suggest the need for meticulous management of piglets in pig production.
Requirements of Postnatal proBDNF in the Hippocampus for Spatial Memory Consolidation and Neural Function

Mature brain-derived neurotrophic factor (BDNF) and its downstream signaling pathways have been implicated in regulating postnatal development and functioning of rodent brain. However, the biological role of its precursor pro-brain-derived neurotrophic factor (proBDNF) in the postnatal brain remains unknown. The expression of hippocampal proBDNF was blocked in postnatal weeks, and multiple behavioral tests, Western blot and morphological techniques, and neural recordings were employed to investigate how proBDNF played a role in spatial cognition in adults. The peak expression and its crucial effects were found in the fourth but not in the second or eighth postnatal week. Blocking proBDNF expression disrupted spatial memory consolidation rather than learning or memory retrieval. Structurally, blocking proBDNF led to the reduction in spine density and proportion of mature spines. Although blocking proBDNF did not affect N-methyl-D-aspartate (NMDA) receptor (NMDAR) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) subunits, the learning-induced phosphorylation of the GluN2B subunit level declined significantly. Functionally, paired-pulse facilitation, post-low-frequency stimulation (LFS) transiently enhanced depression, and GluN2B-dependent short-lasting long-term depression in the Schaffer collateral-CA1 pathway were weakened. The firing rate of pyramidal neurons was significantly suppressed around the target region during the memory test. Furthermore, the activation of GluN2B-mediated signaling could effectively facilitate neural function and mitigate memory impairment. The findings were consistent with the hypothesis that postnatal proBDNF played an essential role in synaptic and cognitive functions.
Identification of Snails and Schistosoma of Medical Importance via Convolutional Neural Networks: A Proof-of-Concept Application for Human Schistosomiasis

In recent decades, computer vision has proven remarkably effective in addressing diverse issues in public health, from determining the diagnosis, prognosis, and treatment of diseases in humans to predicting infectious disease outbreaks. Here, we investigate whether convolutional neural networks (CNNs) can also demonstrate effectiveness in classifying the environmental stages of parasites of public health importance and their invertebrate hosts. We used schistosomiasis as a reference model. Schistosomiasis is a debilitating parasitic disease transmitted to humans via snail intermediate hosts. The parasite affects more than 200 million people in tropical and subtropical regions. We trained our CNN, a feed-forward neural network, on a limited dataset of 5,500 images of snails and 5,100 images of cercariae obtained from schistosomiasis transmission sites in the Senegal River Basin, a region in western Africa that is hyper-endemic for the disease. The image set included both images of two snail genera that are relevant to schistosomiasis transmission – that is, Bulinus spp. and Biomphalaria pfeifferi – as well as snail images that are non-component hosts for human schistosomiasis. Cercariae shed from Bi. pfeifferi and Bulinus spp. snails were classified into 11 categories, of which only two, S. haematobium and S. mansoni, are major etiological agents of human schistosomiasis. The algorithms, trained on 80% of the snail and parasite dataset, achieved 99% and 91% accuracy for snail and parasite classification, respectively, when used on the hold-out validation dataset – a performance comparable to that of experienced parasitologists. The promising results of this proof-of-concept study suggests that this CNN model, and potentially similar replicable models, have the potential to support the classification of snails and parasite of medical importance. In remote field settings where machine learning algorithms can be deployed on cost-effective and widely used mobile devices, such as smartphones, these models can be a valuable complement to laboratory identification by trained technicians. Future efforts must be dedicated to increasing dataset sizes for model training and validation, as well as testing these algorithms in diverse transmission settings and geographies.
Householder Sketch for Accurate and Accelerated Least-Mean-Squares Solvers

Least-Mean-Squares (LMS) solvers comprise a class of fundamental optimization problems such as linear regression, and regularized regressions such as Ridge, LASSO, and Elastic-Net. Data summarization techniques for big data generate summaries called coresets and sketches to speed up model learning under streaming and distributed settings. For example, (Maalouf et al., 2019) design a fast and accurate Caratheodory set on input data to boost the performance of existing LMS solvers. In retrospect, we explore classical Householder transformation as a candidate for sketching and accurately solving LMS problems. We find it to be a simpler, memory-efficient, and faster alternative that always existed to the above strong baseline. We also present a scalable algorithm based on the construction of distributed Householder sketches to solve LMS problem across multiple worker nodes. We perform thorough empirical analysis with large synthetic and real datasets to evaluate the performance of Householder sketch and compare with (Maalouf et al., 2019). Our results show Householder sketch speeds up existing LMS solvers in the scikit-learn library up to 100x400x. Also, it is 10x-100x faster than the above baseline with similar numerical stability. The distributed algorithm demonstrates linear scalability with a near-negligible communication overhead.
Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond

Selective labels are a common feature of high-stakes decision-making applications, referring to the lack of observed outcomes under one of the possible decisions. This paper studies the learning of decision policies in the face of selective labels, in an online setting that balances learning costs against future utility. In the homogeneous case in which individuals’ features are disregarded, the optimal decision policy is shown to be a threshold policy. The threshold becomes more stringent as more labels are collected; the rate at which this occurs is characterized. In the case of features drawn from a ﬁnite domain, the optimal policy consists of multiple homogeneous policies in parallel. For the general inﬁnite-domain case, the homogeneous policy is extended by using a probabilistic classiﬁer and bootstrapping to provide its inputs. In experiments on synthetic and real data, the proposed policies achieve consistently superior utility with no parameter tuning in the ﬁnite-domain case and lower parameter sensitivity in the general case.
A Novel Method to Solve Neural Knapsack Problems

0-1 knapsack is of fundamental importance across many ﬁelds. In this paper, we present a game-theoretic method to solve 0-1 knapsack problems (KPs) where the number of items (products) is large and the values of items are not predetermined but decided by an external value assignment function (e.g., neural network in our case) during the optimization process. While existing papers are interested in predicting solutions with neural networks for classical KPs whose objec-tive functions are mostly linear functions, we are interested in solving KPs whose objective functions are neural networks. In other words, we choose a subset of items that maximizes the sum of the values predicted by neural networks. Its key challenge is how to optimize the neural network-based non-linear KP objective with a budget constraint. Our solution is inspired by game-theoretic approaches in deep learning, e.g., generative adversarial networks. After formally deﬁning our two-player game, we develop an adaptive gradient ascent method to solve it. In our experiments, our method successfully solves two neural network-based non-linear KPs and conventional linear KPs with 1 million items.
Web-based videoconferencing for teaching online: Continuance intention to use in the post-COVID-19 period

Web-based videoconferencing has gained a great momentum worldwide, with extremely high adoption rates during the COVID -19 pandemic. The current study aims to investigate the use of web-based videoconferencing for teaching in the post-COVID-19 landscape. The study proposes and evaluates a model to predict continuance intention to use videoconferencing systems, from the perspective of University teachers. The proposed model combines constructs from the Technology Acceptance Model (TAM) and the Expectancy Confirmation Model (ECM). Sixty-six academic staff members filled out a survey questionnaire about their attitudes towards continuing using videoconferencing systems for teaching in the post-Emergency Remote Teaching (ERT) period. Partial Least Squares (PLS) was used to test the measurement and the structural model. The model explains and predicts 73% of the total variance in continuance intention to use. User satisfaction with web-based videoconferencing and perceived usefulness are the top two strong predictors. Implications for school administrators and instructional designers are discussed.
Neural Feature Matching in Implicit 3D Representations

Recently, neural implicit functions have achieved impressive results for encoding 3D shapes. Conditioning on low-dimensional latent codes generalises a single implicit function to learn shared representation space for a variety of shapes, with the advantage of smooth interpolation. While the beneﬁts from the global latent space do not correspond to explicit points at local level, we propose to track the continuous point trajectory by matching implicit features with the latent code interpo-lating between shapes, from which we corroborate the hierarchical functionality of the deep implicit functions, where early layers map the latent code to ﬁtting the coarse shape structure, and deeper layers further reﬁne the shape details. Further-more, the structured representation space of implicit functions enables to apply feature matching for shape deformation, with the beneﬁts to handle topology and semantics inconsistency, such as from an armchair to a chair with no arms, without explicit ﬂow functions or manual annotations.
SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks

In this paper, we propose a conceptually simple but very effective attention module for Convolu-tional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial-wise attention modules, our module instead infers 3-D attention weights for the feature map in a layer without adding parameters to the original networks. Speciﬁcally, we base on some well-known neuro-science theories and propose to optimize an energy function to ﬁnd the importance of each neuron. We further derive a fast closed-form solution for the energy function, and show that the solution can be implemented in less than ten lines of code. Another advantage of the module is that most of the operators are selected based on the solution to the deﬁned energy function, avoiding too many efforts for structure tuning. Quantitative evaluations on various visual tasks demonstrate that the proposed module is ﬂexible and effective to improve the representation ability of many ConvNets. Our code is available at Pytorch-SimAM.
Dynamic Balancing for Model Selection in Bandits and RL

We propose a framework for model selection by combining base algorithms in stochastic bandits and reinforcement learning. We require a candidate regret bound for each base algorithm that may or may not hold. We select base algorithms to play in each round using a “balancing condition” on the candidate regret bounds. Our approach simultaneously recovers previous worst-case regret bounds, while also obtaining much smaller regret in natural scenarios when some base learners signiﬁcantly exceed their candidate bounds. Our framework is relevant in many settings, including linear bandits and MDPs with nested function classes, linear bandits with unknown misspeciﬁ-cation, and tuning conﬁdence parameters of algorithms such as LinUCB. Moreover, unlike recent efforts in model selection for linear stochastic bandits, our approach can be extended to consider adversarial rather than stochastic contexts.
AutoAttend: Automated Attention Representation Search

Self-attention mechanisms have been widely adopted in many machine learning areas, including Natural Language Processing (NLP) and Graph Representation Learning (GRL), etc. However, existing works heavily rely on hand-crafted design to obtain customized attention mechanisms. In this paper, we automate Key , Query and Value representation design, which is one of the most important steps to obtain effective self-attentions. We propose an automated self-attention representation model, AutoAttend , which can automatically search powerful attention representations for downstream tasks leveraging Neural Architecture Search (NAS). In particular, we design a tailored search space for attention representation automation, which is ﬂexible to produce effective attention representation designs. Based on the design prior obtained from attention representations in previous works, we further regularize our search space to reduce the space complexity without the loss of expressivity. More-over, we propose a novel context-aware parameter sharing mechanism considering special characteristics of each sub-architecture to provide more accurate architecture estimations when conducting parameter sharing in our tailored search space. Experiments show the superiority of our proposed AutoAttend model over previous state-of-the-arts on eight text classiﬁcation tasks in NLP and four node classiﬁcation tasks in GRL.
Accurate Post Training Quantization With Small Calibration Sets

Lately, post-training quantization methods have gained considerable attention, as they are simple to use, and require only a small unlabeled calibration set. This small dataset cannot be used to ﬁne-tune the model without signiﬁcant over-ﬁtting. Instead, these methods only use the calibration set to set the activations’ dynamic ranges. However, such methods always resulted in signiﬁcant accuracy degradation, when used below 8-bits (except on small datasets). Here we aim to break the 8-bit barrier. To this end, we minimize the quantization errors of each layer or block separately by optimizing its parameters over the calibration set. We empirically demonstrate that this approach is: (1) much less susceptible to over-ﬁtting than the standard ﬁne-tuning approaches, and can be used even on a very small calibration set; and (2) more powerful than previous methods, which only set the activations’ dynamic ranges. We suggest two ﬂavors for our method, parallel and sequential aim for a ﬁxed and ﬂexible bit-width allocation. For the latter, we demonstrate how to optimally allocate the bit-widths for each layer, while constraining accuracy degradation or model compression by proposing a novel integer programming formulation. Finally, we suggest model global statistics tuning, to correct biases introduced during quantization. Together, these methods yield state-of-the-art results for both vision and text models. For instance, on ResNet50, we obtain less than 1% accuracy degradation — with 4-bit weights and activations in all layers, but ﬁrst and last. The suggested methods are two orders of magnitude faster than the traditional Quan-tize Aware Training approach used for lower than 8-bit quantization. We open-sourced our code https://github.com/papers-submission/CalibTIP .
Combining Optimization Methods Using an Adaptive Meta Optimizer

Optimization methods are of great importance for the efficient training of neural networks. There are many articles in the literature that propose particular variants of existing optimizers. In our article, we propose the use of the combination of two very different optimizers that, when used simultaneously, can exceed the performance of the single optimizers in very different problems. We propose a new optimizer called ATMO (AdapTive Meta Optimizers), which integrates two different optimizers simultaneously weighing the contributions of both. Rather than trying to improve each single one, we leverage both at the same time, as a meta-optimizer, by taking the best of both. We have conducted several experiments on the classification of images and text documents, using various types of deep neural models, and we have demonstrated through experiments that the proposed ATMO produces better performance than the single optimizers.
Deep Coherent Exploration for Continuous Control

In policy search methods for reinforcement learning (RL), exploration is often performed by injecting noise either in action space at each step independently or in parameter space over each full trajectory. In prior work, it has been shown that with linear policies, a more balanced trade-off between these two exploration strategies is beneﬁcial. However, that method did not scale to policies using deep neural networks. In this paper, we introduce deep coherent exploration, a general and scalable exploration framework for deep RL algorithms for continuous control, that generalizes step-based and trajectory-based exploration. This framework models the last layer parameters of the policy network as latent variables and uses a recursive inference step within the policy update to handle these latent variables in a scalable manner. We ﬁnd that deep coherent exploration improves the speed and stability of learning of A2C, PPO, and SAC on several continuous control tasks.
A Framework for Private Matrix Analysis in Sliding Window Model

We perform a rigorous study of private matrix analysis when only the last W updates to matrices are considered useful for analysis. We show the existing framework in the non-private setting is not robust to noise required for privacy. We then propose a framework robust to noise and use it to give ﬁrst efﬁcient o ( W ) space differentially private algorithms for spectral approximation, principal component analysis (PCA), multi-response linear regression, sparse PCA, and non-negative PCA. Prior to our work, no such result was known for sparse and non-negative differentially private PCA even in the static data setting. We also give a lower bound to demonstrate the cost of privacy.
Incentivizing Compliance with Algorithmic Instruments

Randomized experiments can be susceptible to selection bias due to potential non-compliance by the participants. While much of the existing work has studied compliance as a static behavior, we propose a game-theoretic model to study compliance as dynamic behavior that may change over time. In rounds, a social planner interacts with a sequence of heterogeneous agents who arrive with their unobserved private type that determines both their prior preferences across the actions (e.g., control and treatment) and their baseline rewards without taking any treatment. The planner provides each agent with a randomized recommendation that may alter their beliefs and their action selection. We develop a novel recommendation mechanism that views the planner’s recommendation as a form of instrumental variable (IV) that only affects an agents’ action selection, but not the observed rewards. We construct such IVs by carefully mapping the history –the interactions between the planner and the previous agents– to a random recommendation. Even though the initial agents may be completely non-compliant, our mechanism can incentivize compliance over time, thereby enabling the estimation of the treatment effect of each treatment, and minimizing the cumulative regret of the planner whose goal is to identify the optimal treatment.
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models

Deep generative models (dgms) seem a natural fit for detecting out-of-distribution (ood) inputs, but such models have been shown to assign higher probabilities or densities to ood images than images from the training distribution. In this work, we explain why this behavior should be attributed to model misestimation. We first prove that no method can guarantee performance beyond random chance without assumptions on which out-distributions are relevant. We then interrogate the typical set hypothesis, the claim that relevant out-distributions can lie in high likelihood regions of the data distribution, and that ood detection should be defined based on the data distribution's typical set. We highlight the consequences implied by assuming support overlap between in- and out-distributions, as well as the arbitrariness of the typical set for ood detection. Our results suggest that estimation error is a more plausible explanation than the misalignment between likelihood-based ood detection and out-distributions of interest, and we illustrate how even minimal estimation error can lead to ood detection failures, yielding implications for future work in deep generative modeling and ood detection.
Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling

Our work focuses on the interaction of two key challenges in deep learning: achieving model compactness and sparsity simultaneously with adversarial robustness . Robustness aware network pruning methods showed recent success in this domain. Nevertheless, no effective method existed for robust end-to-end sparse training. Motivating question: How can we enable learning with state-of-the-art robust training objectives by end-to-end sparse training under strict connectivity constraints? Optimizing the network with a negative log-posterior loss which combines a sparsity prior with the robust training objective . We update both the connectivity conﬁguration and the weights such that we are sampling network parameters from the posterior via stochastic gradient Langevin dynamics. Incorporating the sparsity prior by a weight re-parametrization trick: