Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-cell-adhesive hydrogel promotes formation of human blastoids from primed human pluripotent stem cells

Artificial human blastoids are used to investigate early embryo development, pregnancy failures, and birth deficiencies, previously impossible without human embryos. Recent methods generating blastoids used human naive pluripotent stem cells, which are prone to genomic instability during in vitro culturing. Here, we introduce a simple, robust, and scalable method for generating human blastoids from more stable, primed human pluripotent stem cells (hPSC). Using a non-cell-adhesive hydrogel, hPSC aggregates formed an asymmetric blastoid structure with a cellular distribution similar to that of a human blastocyst, with in vitro implantation capability. Single-cell RNA-seq followed by cellular trajectory analysis revealed that hydrogel promoted differentiation to tri-lineage cells associated with a blastocyst. This model will allow studies on the underlying mechanisms of human pre- and postimplantation processes, consider elaborating on the potential implications of the model on assistive reproductive technology. One Sentence Summary No more than 125 characters and spaces.
Measuring Data Reconstruction Defenses in Collaborative Inference Systems

The collaborative inference systems are designed to speed up the prediction processes in edge-cloud scenarios, where the local devices and the cloud system work together to run a complex deep-learning model. However, those edge-cloud collaborative inference systems are vulnerable to emerging reconstruction attacks, where malicious cloud service providers are able to recover the edge-side users’ private data. To defend against such attacks, several defense countermeasures have been recently introduced. Unfortunately, little is known about the robustness of those defense countermeasures. In this paper, we take the ﬁrst step towards measuring the robustness of those state-of-the-art defenses with respect to reconstruction attacks. Speciﬁcally, we show that the latent privacy features are still retained in the obfus-cated representations. Motivated by such an observation, we design a technology called Sensitive Feature Distillation (SFD) to restore sensitive information from the protected feature representations. Our experiments show that SFD can break through defense mechanisms in model partitioning scenarios, demonstrating the in-adequacy of existing defense mechanisms as a privacy-preserving technique against reconstruction attacks. We hope our ﬁndings inspire further work in improving the robustness of defense mechanisms against reconstruction attacks for collaborative inference systems.
A Unifying Framework of Off-Policy General Value Function Evaluation

General Value Function (GVF) is a powerful tool to represent both the predictive and retrospective knowledge in reinforcement learning (RL). In practice, often multiple interrelated GVFs need to be evaluated jointly with pre-collected off-policy samples. In the literature, the gradient temporal difference (GTD) learning method has been adopted to evaluate GVFs in the off-policy setting, but such an approach may suffer from a large estimation error even if the function approximation class is sufficiently expressive. Moreover, none of the previous work have formally established the convergence guarantee to the ground truth GVFs under the function approximation settings. In this paper, we address both issues through the lens of a class of GVFs with causal filtering, which cover a wide range of RL applications such as reward variance, value gradient, cost in anomaly detection, stationary distribution gradient, etc. We propose a new algorithm called GenTD for off-policy GVFs evaluation and show that GenTD learns multiple interrelated multi-dimensional GVFs as efficiently as a single canonical scalar value function. We further show that unlike GTD, the learned GVFs by GenTD are guaranteed to converge to the ground truth GVFs as long as the function approximation power is sufficiently large. To our best knowledge, GenTD is the first off-policy GVF evaluation algorithm that has global optimality guarantee.
TREC: Transient Redundancy Elimination-based Convolution

The intensive computations in convolutional neural networks (CNNs) pose challenges for resource-constrained devices; eliminating redundant computations from convolution is essential. This paper gives a principled method to detect and avoid transient redundancy, a type of redundancy existing in input data or activation maps and hence changing across inferences. By introducing a new form of convolution (TREC), this new method makes transient redundancy detection and avoidance an inherent part of the CNN architecture, and the determination of the best configurations for redundancy elimination part of CNN backward propagation. We provide a rigorous proof of the robustness and convergence of TREC-equipped CNNs. TREC removes over 96% computations and achieves 3.51× average speedups on microcontrollers with minimal (about 0.7%) accuracy loss.
LIPS - Learning Industrial Physical Simulation benchmark suite

E Logistics & available materials 17 E.1 Datasets . 17 E.2 Getting started Notebooks . 17 E.3 Models . 18 E.4 Framework organisation . 18 E.5 Configuration Files . 19 E.6 LIPS usage . 22 E.6. 1 Generating data . 22 E.6. 2 Training an augmented simulator . 23 E.6. 3 Evaluation using Benchmark class . 24
Chronic THC vapor inhalation rescues hyperalgesia in rats with chronic inflammation and produces sex-specific alterations in midbrain neuronal activity

In an effort to reduce reliance on opioids for the treatment of pain in the clinic, ongoing work is testing the utility of cannabinoid drugs as a potential alternative for treatment of chronic pain. We tested chronic THC vapor inhalation effects on thermal nociception and mechanical sensitivity, as well as midbrain (i.e., ventrolateral periaqueductal gray [vlPAG]) neuronal function, in adult male and female Wistar rats with chronic inflammatory pain (CFA treatment). We report that chronic THC vapor inhalation rescues both thermal hyperalgesia and mechanical hypersensitivity in males treated with CFA, but only thermal hyperalgesia in CFA females. Most of the anti-hyperalgesic effects of chronic THC vapor were still observable 24 hours after cessation of the last THC exposure. We also report that chronic THC vapor inhalation modulates intrinsic and synaptic properties of vlPAG neurons, including reductions in action potential firing rate and spontaneous inhibitory synaptic transmission in males, and that these effects occur specifically in neurons that respond to current input with a “delayed” firing phenotype. Treatment with CFA led to increased firing rate and increased sIPSC amplitude in vlPAG neurons of female rats, and chronic THC vapor rescued sIPSC amplitudes to control levels – these effects were specific to vlPAG neurons categorized as having an “onset” firing phenotype. Ongoing work is exploring sex-specific mechanisms (e.g., CB1 receptor) of THC vapor rescue effects in the vlPAG of rats treated with CFA, and further exploring the vlPAG cell types impacted by CFA treatment and chronic THC vapor inhalation. Significance Statement Many adults in the U.S. with pain self-medicate with THC and cannabis, and many humans use e-cigarette type devices filled with cannabis extracts to self-administer THC and other constituents of the marijuana plant. Until recently, most rodent studies of THC effects on brain and behavior have used injection procedures and male rats. Here, we tested the effect of chronic THC vapor inhalation on pain-related behaviors and midbrain neural circuit function in adult male and female Wistar rats. As predicted, chronic THC vapor inhalation rescued chronic inflammatory pain effects on behavior and midbrain neuronal function.
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support

We introduce Support Decomposition Variational Inference (SDVI), a new variational inference (VI) approach for probabilistic programs with stochastic support. Existing approaches to this problem rely on designing a single global variational guide on a variable-by-variable basis, while maintaining the stochastic control flow of the original program. SDVI instead breaks the program down into sub-programs with static support, before automatically building separate sub-guides for each. This decomposition significantly aids in the construction of suitable variational families, enabling, in turn, substantial improvements in inference performance.
HiPhase: Jointly phasing small and structural variants from HiFi sequencing

Background In diploid organisms, phasing is the problem of assigning the heterozygous variants to one of two haplotypes. Reads from PacBio HiFi sequencing provide long, accurate observations that can be used as the basis for both calling and phasing variants. HiFi reads also excel at calling larger classes of variation such as structural variants. However, current phasing tools typically only phase small variants, leaving larger structural variants unphased. Methods We developed HiPhase, a tool that jointly phases SNVs, indels, and structural variants. The main benefits of HiPhase are 1) dual mode allele assignment for detecting structural variants, 2) a novel application of the A*-algorithm to phasing, and 3) logic allowing phase blocks to span breaks caused by alignment issues around reference gaps and homozygous deletions. Results In our assessment, HiPhase produced an average phase block NG50 of 493 kb with 933 switchflip errors and fully phased 95.2% of genes, improving over the current state of the art. Additionally, HiPhase jointly phases SNVs, indels, and structural variants and includes innate multi-threading, statistics gathering, and concurrent phased alignment output generation. Availability https://github.com/PacificBiosciences/HiPhase
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits

While there has been extensive work on learning from ofﬂine data for contextual multi-armed bandit settings, existing methods typically assume there is no environment shift: that the learned policy will operate in the same environmental process as that of data collection. However, this assumption may limit the use of these methods for many practical situations where there may be distribution shifts. In this work we propose Factored Distributionally Robust Optimization (Factored-DRO) 1 , which is able to separately handle distribution shifts in the context distribution and shifts in the reward generating process. Prior work that either ignores potential shifts in the context, or considers them jointly, can lead to performance that is too conservative, especially under certain forms of reward feedback. Our Factored-DRO objective mitigates this by considering the shifts separately, and our proposed estimators are consistent and converge asymptotically. We also introduce a practical algorithm and demonstrate promising empirical results in environments based on real-world datasets, such as voting outcomes and scene classiﬁcation.
On Translation and Reconstruction Guarantees of the Cycle-Consistent Generative Adversarial Networks

The task of unpaired image-to-image translation has witnessed a revolution with the introduction of the cycle-consistency loss to Generative Adversarial Networks (GANs). Numerous variants, with Cycle-Consistent Adversarial Network (Cy-cleGAN) at their forefront, have shown remarkable empirical performance. The involvement of two unalike data spaces and the existence of multiple solution maps between them are some of the facets that make such architectures unique. In this study, we investigate the statistical properties of such unpaired data translator networks between distinct spaces, bearing the additional responsibility of cycle-consistency. In a density estimation setup, we derive sharp non-asymptotic bounds on the translation errors under suitably characterized models. This, in turn, points out sufficient regularity conditions that maps must obey to carry out successful translations. We further show that cycle-consistency is achieved as a consequence of the data being successfully generated in each space based on observations from the other. In a first-of-its-kind attempt, we also provide deterministic bounds on the cumulative reconstruction error. In the process, we establish tolerable upper bounds on the discrepancy responsible for ill-posedness in such networks.
Development of tools to detect and identify strains belonging to the Pseudomonas syringae species complex responsible for vein clearing of zucchini

Vein clearing of zucchini (VCZ) is a seed-borne bacterial disease that affects young plants of Cucurbita pepo subsp. pepo. VCZ agents are distributed into four phylogenetic clusters within the clades 2a and 2ba of the phylogroup 2 of Pseudomonas syringae species complex. Strains belonging to clades 2b and 2d are sometimes isolated from zucchini seeds but have not been associated with VCZ epidemics. Development of tools able to identify VCZ agents is important to better control the disease. Primers were designed to implement a seven-gene MLSA scheme on a collection of strains isolated from zucchini seeds. A clear predominance of strains with a host range on cucurbits limited to the genus Cucurbita (cluster 2ba-A) and the presence of VCZ strains in a fifth cluster (2ba-C) were evidenced. PCR tests were designed to characterize VCZ clusters and a multiplex qPCR test was proposed to distinguish strains having narrow cucurbit host range, associated to the presence of avrRpt2 and sylC, from broad host range strains associated to the presence of hopZ5 and sylC. Additional qPCR tests targeting clade 2b and 2d were also designed to gain insights on P. syringae strains that could be isolated from cucurbits. Specificity of these tools was evaluated in silico on the whole NCBI database and in vitro on a strain collection, showing a 100% inclusivity, except for the test dedicated to clade-2b strains, and an exclusivity ranging from to 96.7% to 100%. These different tools are intended to serve phylogenetic studies, epidemiological monitoring and seed testing.
Byzantine-tolerant federated Gaussian process regression for streaming data

In this paper, we consider Byzantine-tolerant federated learning for streaming data using Gaussian process regression (GPR). In particular, a cloud and a group of agents aim to collaboratively learn a latent function where some agents are subject to Byzantine attacks. We develop a Byzantine-tolerant federated GPR algorithm, which includes three modules: agent-based local GPR, cloud-based aggregated GPR and agent-based fused GPR. We derive the upper bounds on the prediction error between the mean from the cloud-based aggregated GPR and the target function provided that Byzantine agents are less than one quarter of all the agents. We also characterize the lower and upper bounds of the predictive variance. Experiments on a synthetic dataset and two real-world datasets are conducted to evaluate the proposed algorithm.
Depth is More Powerful than Width with Prediction Concatenation in Deep Forest

Random Forest (RF) is an ensemble learning algorithm proposed by Breiman [1] that constructs a large number of randomized decision trees individually and aggregates their predictions by naive averaging. Zhou and Feng [2] further propose Deep Forest (DF) algorithm with multi-layer feature transformation, which significantly outperforms random forest in various application fields. The prediction concatenation (PreConc) operation is crucial for the multi-layer feature transformation in deep forest, though little has been known about its theoretical property. In this paper, we analyze the influence of Preconc on the consistency of deep forest. Especially when the individual tree is inconsistent (as in practice, the individual tree is often set to be fully grown, i.e., there is only one sample at each leaf node), we find that the convergence rate of two-layer DF w.r. t. the number of trees M can reach O (1 /M 2 ) under some mild conditions, while the convergence rate of RF is O (1 /M ) . Therefore, with the help of PreConc, DF with deeper layer will be more powerful than the shallower layer. Experiments confirm theoretical advantages.
Structures of 9-1-1 DNA checkpoint clamp loading at gaps from start to finish and ramification to biology

Recent structural studies show the Rad24-RFC loads the 9-1-1 checkpoint clamp onto a recessed 5′ end by binding the 5′ DNA on Rad24 at an external surface site and threading the 3′ ssDNA into the well-established internal chamber and into 9-1-1. We find here that Rad24-RFC loads 9-1-1 onto DNA gaps in preference to a recessed 5′ DNA end, thus presumably leaving 9-1-1 on a 3′ ss/ds DNA after Rad24-RFC ejects from the 5′ gap end and may explain reports of 9-1-1 directly functioning in DNA repair with various TLS polymerases, in addition to signaling the ATR kinase. To gain a deeper understanding of 9-1-1 loading at gaps we report high-resolution structures of Rad24-RFC during loading of 9-1-1 onto 10-nt and 5-nt gapped DNAs. At a 10-nt gap we captured five Rad24-RFC–9-1-1 loading intermediates in which the 9-1-1 DNA entry gate varies from fully open to fully closed around DNA using ATPγS, supporting the emerging view that ATP hydrolysis is not needed for clamp opening/closing, but instead for dissociation of the loader from the clamp encircling DNA. The structure of Rad24-RFC–9-1-1 at a 5-nt gap shows a 180° axially rotated 3′-dsDNA which orients the template strand to bridge the 3′- and 5′- junctions with a minimum 5-nt ssDNA. The structures reveal a unique loop on Rad24 that limits the length of dsDNA in the inner chamber, and inability to melt DNA ends unlike RFC, thereby explaining Rad24-RFC’s preference for a preexisting ssDNA gap and suggesting a direct role in gap repair in addition to its checkpoint role.
Adaptation Accelerating Sampling-based Bayesian Inference in Attractor Neural Networks

The brain performs probabilistic Bayesian inference to interpret the external world. The sampling-based view assumes that the brain represents the stimulus posterior distribution via samples of stochastic neuronal responses. Although the idea of sampling-based inference is appealing, it faces a critical challenge of whether stochastic sampling is fast enough to match the rapid computation of the brain. In this study, we explore how latent feature sampling can be accelerated in neural circuits. Specifically, we consider a canonical neural circuit model called continuous attractor neural networks (CANNs) and investigate how sampling-based inference of latent continuous variables is accelerated in CANNs. Intriguingly, we find that by including noisy adaptation in the neuronal dynamics, the CANN is able to speed up the sampling process significantly. We theoretically derive that the CANN with noisy adaptation implements the efficient sampling method called Hamiltonian dynamics with friction, where noisy adaption effectively plays the role of momentum. We theoretically analyze the sampling performances of the network and derive the condition when the acceleration has the maximum effect. Simulation results validate our theoretical analyses. We further extend the model to coupled CANNs and demonstrate that noisy adaptation accelerates the sampling of the posterior distribution of multivariate stimuli. We hope that this study enhances our understanding of how Bayesian inference is realized in the brain
Learning dynamics of deep linear networks with multiple pathways

Not only have deep networks become standard in machine learning, they are increasingly of interest in neuroscience as models of cortical computation that capture relationships between structural and functional properties. In addition they are a useful target of theoretical research into the properties of network computation. Deep networks typically have a serial or approximately serial organization across layers, and this is often mirrored in models that purport to represent computation in mammalian brains. There are, however, multiple examples of parallel pathways in mammalian brains. In some cases, such as the mouse, the entire visual system appears arranged in a largely parallel, rather than serial fashion. While these pathways may be formed by differing cost functions that drive different computations, here we present a new mathematical analysis of learning dynamics in networks that have parallel computational pathways driven by the same cost function. We use the approximation of deep linear networks with large hidden layer sizes to show that, as the depth of the parallel pathways increases, different features of the training set (defined by the singular values of the input-output correlation) will typically concentrate in one of the pathways. This result is derived analytically and demonstrated with numerical simulation with both linear and non-linear networks. Thus, rather than sharing stimulus and task features across multiple pathways, parallel network architectures learn to produce sharply diversified representations with specialized and specific pathways, a mechanism which may hold important consequences for codes in both biological and artificial systems.
Recommender Forest for Efficient Retrieval

Recommender systems (RS) have to select the top-n items from a massive item set. For the sake of efficient recommendation, RS usually represents users and items as latent embeddings and relies on approximate nearest neighbor search (ANNs) to retrieve the recommendation results. Despite the reduction of running time, the representation learning is independent of ANNs index construction; thus, the two operations can be incompatible, which results in a potential loss of recommendation accuracy. To overcome the above problem, we propose the Recommender Forest (a.k.a., RecForest), which jointly learns latent embedding and index for an efficient and high-fidelity recommendation. RecForest consists of multiple K-ary trees, each of which is a partition of the item set via hierarchical balanced clustering such that each item is uniquely represented by a path from the root to a leaf. Given such a data structure, an encoder-decoder-based routing network is developed: it first encodes user information into user representation; then, leveraging a transformer-based decoder, it identifies the top-n items via beam search. Compared with the existing methods, RecForest brings in the following advantages: 1) the false partition of the near-boundary items can be effectively alleviated by the use of multiple trees; 2) the routing operation becomes much more accurate thanks to the powerful transformer decoder; 3) the branch parameters are shared across different tree levels, making the index to be extremely memory-efficient. The experimental studies are performed on six popular recommendation datasets: with a significantly simplified training cost, RecForest outperforms competitive baseline approaches in terms of both recommendation accuracy and efficiency. The code is available at https://github.com/wuchao-li/RecForest .