Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing Flipping Errors in Deep Neural Networks

Deep neural networks (DNNs) have been widely applied in various domains in artificial intelligence including computer vision and natural language processing. A DNN is typically trained for many epochs and then a validation dataset is used to select the DNN in an epoch (we simply call this epoch ``the last epoch") as the final model for making predictions on unseen samples, while it usually cannot achieve a perfect accuracy on unseen samples. An interesting question is ``how many test (unseen) samples that a DNN misclassifies in the last epoch were ever correctly classified by the DNN before the last epoch? ". In this paper, we empirically study this question and find on several benchmark datasets that the vast majority of the misclassified samples in the last epoch were ever classified correctly before the last epoch, which means that the predictions for these samples were flipped from ``correct" to ``wrong". Motivated by this observation, we propose to restrict the behavior changes of a DNN on the correctly-classified samples so that the correct local boundaries can be maintained and the flipping error on unseen samples can be largely reduced. Extensive experiments on different benchmark datasets with different modern network architectures demonstrate that the proposed flipping error reduction (FER) approach can substantially improve the generalization, the robustness, and the transferability of DNNs without introducing any additional network parameters or inference cost, only with a negligible training overhead.
On recurrence in zero-dimensional locally compact flow with compactly generated phase group

We deﬁne recurrence for a compactly generated para-topological group G acting continuously on a locally compact Hausdor ﬀ space X with dim X = 0, and then, show that if Gx is compact for all x ∈ X , the conditions (i) this dynamics is pointwise recurrent, (ii) X is a union of G -minimal sets, (iii) the G -orbit closure relation is closed in X × X , and (iv) X ∋ x 7→ Gx ∈ 2 X is continuous, are pairwise equivalent. Consequently, if this dynamics is pointwise product recurrent, then it is pointwise regularly almost periodic and equicontinuous; moreover, a distal, compact, and non-connected G -ﬂow has a non-trivial equicontinuous pointwise regularly almost periodic factor.
Interactive Portrait Harmonization

Current image harmonization methods consider the entire background as the guidance for harmonization. However, this may limit the capability for user to choose any specific object/person in the background to guide the harmonization. To enable flexible interaction between user and harmonization, we introduce interactive harmonization, a new setting where the harmonization is performed with respect to a selected \emph{region} in the reference image instead of the entire background. A new flexible framework that allows users to pick certain regions of the background image and use it to guide the harmonization is proposed. Inspired by professional portrait harmonization users, we also introduce a new luminance matching loss to optimally match the color/luminance conditions between the composite foreground and select reference region. This framework provides more control to the image harmonization pipeline achieving visually pleasing portrait edits. Furthermore, we also introduce a new dataset carefully curated for validating portrait harmonization. Extensive experiments on both synthetic and real-world datasets show that the proposed approach is efficient and robust compared to previous harmonization baselines, especially for portraits. Project Webpage at \href{https://jeya-maria-jose.github. io/IPH-web/}{https://jeya-maria-jose.github. io/IPH-web/}
Fixed-Budget Best-Arm Identification in Structured Bandits

Best-arm identification (BAI) in a fixed-budget setting is a bandit problem where the learning agent maximizes the probability of identifying the optimal (best) arm after a fixed number of observations. Most works on this topic study unstructured problems with a small number of arms, which limits their applicability. We propose a general tractable algorithm that incorporates the structure, by successively eliminating suboptimal arms based on their mean reward estimates from a joint generalization model. We analyze our algorithm in linear and generalized linear models (GLMs), and propose a practical implementation based on a G-optimal design. In linear models, our algorithm has competitive error guarantees to prior works and performs at least as well empirically. In GLMs, this is the first practical algorithm with analysis for fixed-budget BAI.
Accurate Parameter Estimation for Risk-aware Autonomous Systems

Analysis and synthesis of safety-critical autonomous systems are carried out using models which are often dynamic. Two central features of these dynamic systems are parameters and unmodeled dynamics. This paper addresses the use of a spectral lines-based approach for estimating parameters of the dynamic model of an autonomous system. Existing literature has treated all unmodeled components of the dynamic system as sub-Gaussian noise and proposed parameter estimation using Gaussian noise-based exogenous signals. In contrast, we allow the unmodeled part to have deterministic unmodeled dynamics, which are almost always present in physical systems, in addition to sub-Gaussian noise. In addition, we propose a deterministic construction of the exogenous signal in order to carry out parameter estimation. We introduce a new tool kit which employs the theory of spectral lines, retains the stochastic setting, and leads to non-asymptotic bounds on the parameter estimation error. Unlike the existing stochastic approach, these bounds are tunable through an optimal choice of the spectrum of the exogenous signal leading to accurate parameter estimation. We also show that this estimation is robust to unmodeled dynamics, a property that is not assured by the existing approach. Finally, we show that under ideal conditions with no unmodeled dynamics, the proposed approach can ensure a $\tilde{O}(\sqrt{T})$ regret, matching existing literature. Experiments are provided to support all theoretical derivations, which show that the spectral lines-based approach outperforms the Gaussian noise-based method when unmodeled dynamics are present, in terms of both parameter estimation error and Regret obtained using the parameter estimates with a Linear Quadratic Regulator in feedback.
Revisiting Design Choices in Offline Model Based Reinforcement Learning

Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collection. Significant progress has been made recently in offline model-based reinforcement learning, approaches which leverage a learned dynamics model. This typically involves constructing a probabilistic model, and using the model uncertainty to penalize rewards where there is insufficient data, solving for a pessimistic MDP that lower bounds the true MDP. Existing methods, however, exhibit a breakdown between theory and practice, whereby pessimistic return ought to be bounded by the total variation distance of the model from the true dynamics, but is instead implemented through a penalty based on estimated model uncertainty. This has spawned a variety of uncertainty heuristics, with little to no comparison between differing approaches. In this paper, we compare these heuristics, and design novel protocols to investigate their interaction with other hyperparameters, such as the number of models, or imaginary rollout horizon. Using these insights, we show that selecting these key hyperparameters using Bayesian Optimization produces superior configurations that are vastly different to those currently used in existing hand-tuned state-of-the-art methods, and result in drastically stronger performance.
Predicting Patch Correctness Based on the Similarity of Failing Test Cases

How do we know a generated patch is correct? This is a key challenging question that automated program repair (APR) systems struggle to address given the incompleteness of available test suites. Our intuition is that we can triage correct patches by checking whether each generated patch implements code changes (i.e., behavior) that are relevant to the bug it addresses. Such a bug is commonly specified by a failing test case. Towards predicting patch correctness in APR, we propose a novel yet simple hypothesis on how the link between the patch behavior and failing test specifications can be drawn: similar failing test cases should require similar patches. We then propose BATS, an unsupervised learning-based approach to predict patch correctness by checking patch Behavior Against failing Test Specification. BATS exploits deep representation learning models for code and patches: For a given failing test case, the yielded embedding is used to compute similarity metrics in the search for historical similar test cases to identify the associated applied patches, which are then used as a proxy for assessing the correctness of the APR-generated patches. Experimentally, we first validate our hypothesis by assessing whether ground-truth developer patches cluster together in the same way that their associated failing test cases are clustered. Then, after collecting a large dataset of 1,278 plausible patches (written by developers or generated by 32 APR tools), we use BATS to predict correct patches: BATS achieves AUC between 0.557 to 0.718 and recall between 0.562 and 0.854 in identifying correct patches. Our approach outperforms state-of-the-art techniques for identifying correct patches without the need for large labeled patch datasets—as is the case with machine learning-based approaches. While BATS is constrained by the availability of similar test cases, we show that it can still be complementary to existing approaches: When combined with a recent approach that relies on supervised learning, BATS improves the overall recall in detecting correct patches. We finally show that BATS is complementary to the state-of-the-art PATCH-SIM dynamic approach for identifying correct patches generated by APR tools.
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC

Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usually parametrized by a deep network, is highly multi-modal in the data space. This is a serious handicap for both theory and practice of EBMs. In this paper, we propose to learn an EBM with a flow-based model (or in general a latent variable model) serving as a backbone, so that the EBM is a correction or an exponential tilting of the flow-based model. We show that the model has a particularly simple form in the space of the latent variables of the backbone model, and MCMC sampling of the EBM in the latent space mixes well and traverses modes in the data space. This enables proper sampling and learning of EBMs.
GRPE: Relative Positional Encoding for Graph Transformer

We propose a novel positional encoding for learning graph on Transformer architecture. Existing approaches either linearize a graph to encode absolute position in the sequence of nodes, or encode relative position with another node using bias terms. The former loses preciseness of relative position from linearization, while the latter loses a tight integration of node-edge and node-topology interaction. To overcome the weakness of the previous approaches, our method encodes a graph without linearization and considers both node-topology and node-edge interaction. We name our method Graph Relative Positional Encoding dedicated to graph representation learning. Experiments conducted on various graph datasets show that the proposed method outperforms previous approaches significantly. Our code is publicly available at https://github.com/lenscloth/GRPE.
Molecular architecture of the TasA biofilm scaffold in Bacillus subtilis

Many bacteria in nature exist in multicellular communities termed biofilms. Cells within biofilms are embedded in a primarily self-secreted extracellular polymeric matrix that provides rigidity to the biofilm and protects cells from chemical and mechanical stresses. In the Gram-positive model biofilm-forming bacterium Bacillus subtilis, TasA is the major protein component of the biofilm matrix, where it has been reported to form functional amyloid fibres contributing to biofilm structure and stability. The structure of TasA fibres, however, and how fibres scaffold the biofilm at the molecular level, is not known. Here, we present electron cryomicroscopy structures of TasA fibres, which show that rather than forming amyloid fibrils, TasA monomers assemble into filaments through donor strand complementation, with each subunit donating a β-strand to complete the fold of the next subunit along the filament. Combining electron cryotomography, atomic force microscopy, and mutational studies, we show how TasA filaments congregate in three dimensions to form abundant fibre bundles that are essential for B. subtilis biofilm formation. This study explains the previously observed biochemical properties of TasA and shows, for the first time, how a bacterial extracellular globular protein can assemble from monomers into β-sheet-rich fibres, and how such fibres assemble into bundles in biofilms. We establish a hierarchical, atomic-level assembly mechanism of biofilm scaffolding that provides a structural framework for understanding bacterial biofilm formation.
An end-to-end deep learning method for rotamer-free protein side-chain packing

Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to resolve this problem, but their accuracy is still unsatisfactory. To address this, we present AttnPacker, an end-to-end, SE(3)-equivariant deep graph transformer architecture for the direct prediction of side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone geometry to simultaneously compute all amino acid side-chain atom coordinates without delegating to a rotamer library, or performing expensive conformational search or sampling steps. Tested on the CASP13 and CASP14 native and non-native protein backbones, AttnPacker predicts side-chain conformations with RMSD significantly lower than the best side-chain packing methods (SCWRL4, FASPR, Rosetta Packer, and DLPacker), and achieves even greater improvements on surface residues. In addition to RMSD, our method also achieves top performance in side-chain dihedral prediction across both data sets.
The asymmetric ABA response on both sides of root tip is important for tomato root hydrotropism by mediating proton efflux and cell elongation

Hydrotropism is an important adaptation of plant roots to the uneven distribution of water, and the current research on hydrotropism is mainly focused on Arabidopsis thaliana. We examined hydrotropism in tomato (Solanum lycopersicum) primary roots. We used RNA sequencing to detect the gene expression on both sides (dry and wet side) of root tips (5 mm from the root cap junction) by splitting root tips longitudinally into two halves. We found that hydrostimulation induced the asymmetric cell elongation between the dry side (lower water potential) and wet side of root tips (higher water potential). ABA biosynthesis gene ABA4 was induced on the dry side as compared to the wet side of root tips. Chemical inhibitors that block ABA biosynthesis can disrupt hydrotropism, and ABA biosynthesis mutant not showed significantly reduced hydrotropism. Furthermore, asymmetric H+ efflux was found in wild-type but not in root tips of ABA biosynthesis mutant not after hydrostimulation. Our results suggest that the asymmetric ABA response on both sides of root tip mediate asymmetric H+ efflux, and then drive the asymmetric cell elongation, which allows the root to bend towards the wet side to take up more water. Graphical abstract Involvement of ABA-mediated asymmetric H+ efflux in root hydrotropism. Compared to the wet side of root tip (higher water potential), the dry side (lower water potential) induces the expression of ABA biosynthesis gene ABA4, thus enhancing proton efflux to promoting cell elongation on the dry side. Because H+ efflux and cell elongation on the dry side of the root tip are higher than that on the wet side, the asymmetric growth of cells on both sides allows the root to bend towards the wet side for taking up more water. In Brief The asymmetric ABA response on both sides of root tip (dry side and wet side) sequently mediates root asymmetric H+ efflux, and then drives the asymmetric cell elongation on both sides of root tip, which allows the root to bend towards the wet side for absorbing more water. Highlights Hydrotropic bending requires asymmetric cell elongation on the root two sides. Asymmetric expression of ABA biosynthesis gene ABA4 is required for root hydrotropism. The H+ efflux on the dry side of the root is increased. ABA-associated asymmetric H+ efflux driven root hydrotropic bending.
Virgin Birth: A genetic basis for facultative parthenogenesis

Sexual reproduction evolved 1-2 billion years ago and underlies the biodiversity of our planet. Nevertheless, devolution of sexual into asexual reproduction can occur across all phyla of the animal kingdom. The genetic basis for how parthenogenesis can arise is completely unknown. To understand the mechanism and benefits of parthenogenesis, we have sequenced the genome of the facultative parthenogen, Drosophila mercatorum, and compared its organisation and expression pattern during parthenogenetic or sexual reproduction. We identified three genes, desat2, Myc, and polo in parthenogenetic D. mercatorum that when mis-regulated in a non-parthenogenetic species, D. melanogaster, enable facultative parthenogenetic reproduction. This simple genetic switch leads us to propose that sporadic facultative parthenogenesis could evolve as an ‘escape route’ preserving the genetic lineage in the face of sexual isolation.
Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection

Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target molecule of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. With the increasing amount of such high-throughput experimental data available, machine learning techniques have become increasingly popular for molecular datasets analysis. Here, we show that Restricted Boltzmann Machines (RBMs), a two-layer neural network architecture, can successfully be trained on sequence ensembles from SELEX experiments for thrombin aptamers, and used to estimate the fitness of the sequences obtained through the experimental protocol. As a direct consequence, we show that trained RBMs can be exploited to classify as well as generate novel molecules. To confirm our findings, we experimentally verify the generated sequences from RBM.
Tension anisotropy drives phenotypic transitions of cells via two-way cell-ECM feedback

Mechanical factors such as stress in the extracellular environment are known to affect phenotypic commitment of cells. However, the stress fields experienced by cells in tissues are multiaxial, and the ways that cells integrate this multiaxial information are largely unknown. Here, we report that the anisotropy of these stress fields is a critical factor triggering phenotypic transition in fibroblast cells, outweighing the previously reported role of stress amplitude. Using a combined experimental and computational approach, we discovered a self-reinforcing mechanism in which cellular protrusions interact with collagen fibers to develop tension anisotropy, which in turn stabilizes protrusions and amplifies their contractile forces. Disruption of this self-reinforcing process, either by reducing tension anisotropy or by inhibiting contractile protrusions, prevented phenotypic conversion of fibroblasts to contractile myofibroblasts.
Physiological trait networks enhance understanding of crop growth and water use in contrasting environments

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential (“risky” stomatal regulation), high xylem hydraulic conductivity, and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance, and low stomatal conductance at low leaf water potential (“conservative” stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits and their interactions (i.e., networks), has potential to improve crop growth predictions in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance. Summary statement Our process-based model uncovered two beneficial but contrasting trait networks for maize which can be understood by their integrated effect on water use/conservation. Modification of multiple, physiologically aligned, traits were required to bring about meaningful improvements in NPP and yield.
Molecular docking between human TMPRSS2 and the serine protease Kunitz-type inhibitor rBmTI-A

SARS-CoV-2 entrance into host cells is dependent of ACE2 receptor and viral protein S initiation by serine protease TMPRSS2. Cleavage of coronavirus protein S at the junctions Arg685/Ser686 and Arg815/Ser816 leads to the production of the S1/S2 and S2’ fragments needed for the fusion of viral and cell membranes. Studying and identifying serine protease inhibitors is an important step towards the development of candidate drugs to prevent SARS-CoV-2 infection. It has already been stablished that camostat mesylate, a serine protease inhibitor, is capable of blocking TMPRSS2 activity and prevent SARS-CoV-2 entrance into host cells. In this work, the interaction between the two domains of Kunitz-type serine protease inhibitor rBmTI-A and TMPRSS2 was studied through molecular docking. rBmTI-A domain 2 (P1 site Leu84) had the best complex results with predicted binding affinity of -12 Kcal.mol-1 and predicted dissociation constant at 25°C of 1.6 nM. The results suggest that rBmTI-A is capable of binding TMPRSS2 cleavage site at the junction Arg815/Ser816 using essentially the same residues that camostat mesylate.