Miracle-dz/UniFAR-FTUs huggingface dataset:
Intact synapse structure and function after combined knockout of PTPδ, PTPσ, and LAR

It has long been proposed that leukocyte common antigen-related receptor protein tyrosine phosphatases (LAR-RPTPs) are cell-adhesion proteins that control synapse assembly. Their synaptic nanoscale localization, however, is not established, and synapse fine structure after knockout of the three vertebrate LAR-RPTPs (PTPδ, PTPσ, and LAR) has not been tested. Here, superresolution microscopy reveals that PTPδ localizes to the synaptic cleft precisely apposed to postsynaptic scaffolds of excitatory and inhibitory synapses. We next assessed synapse structure in newly generated triple-conditional-knockout mice for PTPδ, PTPσ, and LAR, complementing a recent independent study of synapse function after LAR-RPTP ablation (Sclip and Südhof, 2020). While mild effects on synaptic vesicle clustering and active zone architecture were detected, synapse numbers and their overall structure were unaffected, membrane anchoring of the active zone persisted, and vesicle docking and release were normal. Hence, despite their localization at synaptic appositions, LAR-RPTPs are dispensable for presynapse structure and function.
On the geometric and Riemannian structure of the spaces of group equivariant non-expansive operators

Group equivariant non-expansive operators have been recently proposed as basic components in topological data analysis and deep learning. In this paper we study some geometric properties of the spaces of group equivariant operators and show how a space $\mathcal{F}$ of group equivariant non-expansive operators can be endowed with the structure of a Riemannian manifold, so making available the use of gradient descent methods for the minimization of cost functions on $\mathcal{F}$. As an application of this approach, we also describe a procedure to select a finite set of representative group equivariant non-expansive operators in the considered manifold.
Evolving beyond z=0: insights about the future of stars and the intergalactic medium

We present results from seven cosmological simulations that have been extended beyond the present era as far as redshift z ≈ −0.99 or $t\approx 96.0\, {\rm Gyr}$, using the Enzo simulation code. We adopt the calibrated star formation and feedback prescriptions from our previous work on reproducing the Milky Way with Enzo, with modifications to the simulation code, chemistry, and cooling library. We then consider the future behaviour of the halo mass function (HMF), the equation of state (EOS) of the IGM, and the cosmic star formation history (SFH). Consistent with previous work, we find a freeze out in the HMF at z ≈ −0.6 or $t\approx 28.1\, {\rm Gyr}$. The evolution of the EOS of the IGM presents an interesting case study of the cosmological coincidence problem, where there is a sharp decline in the IGM temperature immediately after z = 0. For the SFH, the simulations produce a peak and a subsequent decline into the future. However, we do find a turnaround in the SFH after z ≈ −0.98 or $t\approx 82.4\, {\rm Gyr}$ in some simulations, which we attribute to limitations of the criteria used for star formation. By integrating the SFH in time up to z ≈ −0.92 or $t\approx 55.1\, {\rm Gyr}$, the simulation with the best spatial resolution predicts an asymptotic total stellar mass that is very close to that obtained from extrapolating the fit of the observed SFR. Lastly, we investigate the future evolution of the partition of baryons within a Milky Way-sized galaxy, using both a zoom and a box simulation. Despite vastly different resolutions, these simulations predict individual haloes containing an equal fraction of baryons in stars and gas at the time of freeze out.
The JCMT BISTRO Survey: Revealing the Diverse Magnetic Field Morphologies in Taurus Dense Cores with Sensitive Submillimeter Polarimetry

We have obtained sensitive dust continuum polarization observations at 850 μm in the B213 region of Taurus using POL-2 on SCUBA-2 at the James Clerk Maxwell Telescope as part of the B-fields in STar-forming Region Observations (BISTRO) survey. These observations allow us to probe magnetic field (B-field) at high spatial resolution (∼2000 au or ∼0.01 pc at 140 pc) in two protostellar cores (K04166 and K04169) and one prestellar core (Miz-8b) that lie within the B213 filament. Using the Davis–Chandrasekhar–Fermi method, we estimate the B-field strengths in K04166, K04169, and Miz-8b to be 38 ± 14, 44 ± 16, and 12 ± 5 μG, respectively. These cores show distinct mean B-field orientations. The B-field in K04166 is well ordered and aligned parallel to the orientations of the core minor axis, outflows, core rotation axis, and large-scale uniform B-field, in accordance with magnetically regulated star formation via ambipolar diffusion taking place in K04166. The B-field in K04169 is found to be ordered but oriented nearly perpendicular to the core minor axis and large-scale B-field and not well correlated with other axes. In contrast, Miz-8b exhibits a disordered B-field that shows no preferred alignment with the core minor axis or large-scale field. We found that only one core, K04166, retains a memory of the large-scale uniform B-field. The other two cores, K04169 and Miz-8b, are decoupled from the large-scale field. Such a complex B-field configuration could be caused by gas inflow onto the filament, even in the presence of a substantial magnetic flux.
Information-Centric Networking in Wireless Environments: Security Risks and Challenges

Information-Centric Networking (ICN) has emerged as a paradigm to cope with the lack of built-in security primitives and efficient mechanisms for content distribution of today's Internet. However, deploying ICN in a wireless environment poses a different set of challenges compared to a wired environment, especially when it comes to security. In this article, we present the security issues that may arise and the attacks that may occur from different points of view when ICN is deployed in wireless environments. The discussed attacks may target both applications and the ICN network itself by exploiting elements of the ICN architecture, such as content names and in-network content caches. Furthermore, we discuss potential solutions to the presented issues and countermeasures to the presented attacks. Finally, we identify future research opportunities and directions.
Functional Extensionality for Refinement Types

Refinement type checkers are a powerful way to reason about functional programs. For example, one can prove properties of a slow, specification implementation, porting the proofs to an optimized implementation that behaves the same. Without functional extensionality, proofs must relate functions that are fully applied. When data itself has a higher-order representation, fully applied proofs face serious impediments! When working with first-order data, fully applied proofs lead to noisome duplication when using higher-order functions. While dependent type theories are typically consistent with functional extensionality axioms, refinement type systems with semantic subtyping treat naive phrasings of functional extensionality inconsistently, leading to unsoundness. We demonstrate this unsoundness and develop a new approach to equality in Liquid Haskell: we define a propositional equality in a library we call PEq. Using PEq avoids the unsoundness while still proving useful equalities at higher types; we demonstrate its use in several case studies. We validate PEq by building a small model and developing its metatheory. Additionally, we prove metaproperties of PEq inside Liquid Haskell itself using an unnamed folklore technique, which we dub `classy induction'.
Convergence and Inequality in Research Globalization

The catch-up effect and the Matthew effect offer opposing characterizations of globalization: the former predicts an eventual convergence as the poor can grow faster than the rich due to free exchanges of complementary resources, while the latter, a deepening inequality between the rich and the poor. To understand these effects on the globalization of research, we conduct an in-depth study based on scholarly and patent publications covering STEM research from 218 countries/regions over the past four decades, covering more than 55 million scholarly articles and 1.7 billion citations. Unique to this investigation is the simultaneous examination of both the research output and its impact in the same data set, using a novel machine learning based measure, called saliency, to mitigate the intrinsic biases in quantifying the research impact. The results show that the two effects are in fact co-occurring: there are clear indications of convergence among the high income and upper middle income countries across the STEM fields, but a widening gap is developing that segregates the lower middle and low income regions from the higher income regions. Furthermore, the rate of convergence varies notably among the STEM sub-fields, with the highly strategic area of Artificial Intelligence (AI) sandwiched between fields such as Medicine and Materials Science that occupy the opposite ends of the spectrum. The data support the argument that a leading explanation of the Matthew effect, namely, the preferential attachment theory, can actually foster the catch-up effect when organizations from lower income countries forge substantial research collaborations with those already dominant. The data resoundingly show such collaborations benefit all parties involved, and a case of role reversal can be seen in the Materials Science field where the most advanced signs of convergence are observed.
ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

An important goal of medical imaging is to be able to precisely detect patterns of disease specific to individual scans; however, this is challenged in brain imaging by the degree of heterogeneity of shape and appearance. Traditional methods, based on image registration, historically fail to detect variable features of disease, as they utilise population-based analyses, suited primarily to studying group-average effects. In this paper we therefore take advantage of recent developments in generative deep learning to develop a method for simultaneous classification, or regression, and feature attribution (FA). Specifically, we explore the use of a VAE-GAN (variational autoencoder - general adversarial network) for translation called ICAM, to explicitly disentangle class relevant features, from background confounds, for improved interpretability and regression of neurological phenotypes. We validate our method on the tasks of Mini-Mental State Examination (MMSE) cognitive test score prediction for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, as well as brain age prediction, for both neurodevelopment and neurodegeneration, using the developing Human Connectome Project (dHCP) and UK Biobank datasets. We show that the generated FA maps can be used to explain outlier predictions and demonstrate that the inclusion of a regression module improves the disentanglement of the latent space. Our code is freely available on GitHub https://github.com/CherBass/ICAM.
Bulk Production Augmentation Towards Explainable Melanoma Diagnosis

Although highly accurate automated diagnostic techniques for melanoma have been reported, the realization of a system capable of providing diagnostic evidence based on medical indices remains an open issue because of difficulties in obtaining reliable training data. In this paper, we propose bulk production augmentation (BPA) to generate high-quality, diverse pseudo-skin tumor images with the desired structural malignant features for additional training images from a limited number of labeled images. The proposed BPA acts as an effective data augmentation in constructing the feature detector for the atypical pigment network (APN), which is a key structure in melanoma diagnosis. Experiments show that training with images generated by our BPA largely boosts the APN detection performance by 20.0 percentage points in the area under the receiver operating characteristic curve, which is 11.5 to 13.7 points higher than that of conventional CycleGAN-based augmentations in AUC.
Dynamic Oﬄoading Loading Optimization in Time-Varying Mobile Edge Networks with Deep Reinforcement Learning Approach

Mobile edge computing (MEC) has been regarded as a promising wireless access architecture to alleviate the burden of performing computation for large content size tasks at resource limited mobile terminals. By allowing the mobile terminals to oﬄoad the computation tasks to MEC servers, the task processing delay can be signiﬁcantly decreased. As the resources at the MEC and user are limited, performing reasonable resources allocation optimization can improve the performance, especially for a multi-user oﬄoading system. In this study, with an aim to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals and time-varying wireless channels. To address the challenging dynamic joint optimization problem, we formulate it as a reinforcement learning (RL) problem. We design the computational oﬄoading policies to minimize the long-term average delay cost. Two deep RL strategies, that is, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to eﬃciently learn the computational oﬄoading policies adaptively. The proposed DQN strategy takes the MEC selection as a unique action while using convex
Style-based Point Generator with Adversarial Rendering for Point Cloud Completion

In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv to fully exploit the local structures as well as the global shape in point features. Secondly, we observe that the concatenation manner used by vanilla foldings limits its potential of generating a complex and faithful shape. Enlightened by the success of StyleGAN, we regard the shape feature as style code that modulates the normalization layers during the folding, which considerably enhances its capability. Thirdly, we realize that existing point supervisions, e.g., Chamfer Distance or Earth Mover’s Distance, cannot faithfully reflect the perceptual quality of the reconstructed points. To address this, we propose to project the completed points to depth maps with a differentiable renderer and apply adversarial training to advocate the perceptual realism under different viewpoints. Comprehensive experiments on ShapeNet and KITTI prove the effectiveness of our method, which achieves state-of-the-art quantitative performance while offering superior visual quality.
Studies on Pectolytic Enzymes of Molds

The clarification of apple juice has been studied using six pectolytic enzymes produced by Coniothyrium diplodiella, endo-PG (polygalacturonase) I, II and III, exo-PG and PE (pectinesterase) I and II. Each of these six enzymes had no effect on the clarification of apple juice when acted alone, whereas mixtures of any one of endo-PGs and PEs were all able to clarify the juice. Mixtures of exo-PG and either of PEs has no effect on the clarification. Clarifying activities of PG-PE mixtures were varied with the kind of endo-PG used in each mixture and not with the kind of PE. Clarifying activity of PG-PE mixture depended on either endo-PG or PE activities when the other was kept constant.Crude enzyme from the mold and a mixture of the four PGs and PE in the ratio of the crude enzyme had essentially identical effect on apple juice as well as on artificial pectin and pectic acid.
Coherent control and distinguishability of quantum channels via PBS-diagrams

We introduce a graphical language for coherent control of general quantum channels inspired by practical quantum optical setups involving polarising beam splitters (PBS). As standard completely positive trace preserving maps are known not to be appropriate to represent coherently controlled quantum channels, we propose to instead use purified channels, an extension of Stinespring's dilation. We characterise the observational equivalence of purified channels in various coherent-control contexts, paving the way towards a faithful representation of quantum channels under coherent control.
Inference-Based Deterministic Messaging For Multi-Agent Communication

Communication is essential for coordination among humans and animals. Therefore, with the introduction of intelligent agents into the world, agent-to-agent and agent-to-human communication becomes necessary. In this paper, we first study learning in matrix-based signaling games to empirically show that decentralized methods can converge to a suboptimal policy. We then propose a modification to the messaging policy, in which the sender deterministically chooses the best message that helps the receiver to infer the sender's observation. Using this modification, we see, empirically, that the agents converge to the optimal policy in nearly all the runs. We then apply this method to a partially observable gridworld environment which requires cooperation between two agents and show that, with appropriate approximation methods, the proposed sender modification can enhance existing decentralized training methods for more complex domains as well.
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images

General object grasping is an important yet unsolved problem in the field of robotics. Most of the current methods either generate grasp poses with few DoF that fail to cover most of the success grasps, or only take the unstable depth image or point cloud as input which may lead to poor results in some cases. In this paper, we propose RGBD-Grasp, a pipeline that solves this problem by decoupling 7-DoF grasp detection into two sub-tasks where RGB and depth information are processed separately. In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image. Consequently, a Fast Analytic Searching(FAS) module calculates the opening width and the distance of the gripper to the grasp point. By decoupling the grasp detection problem and introducing the stable RGB modality, our pipeline alleviates the requirement for the high-quality depth image and is robust to depth sensor noise. We achieve state-of-the-art results on GraspNet-1Billion dataset compared with several baselines. Real robot experiments on a UR5 robot with an Intel Realsense camera and a Robotiq two-finger gripper show high success rates for both single object scenes and cluttered scenes. Our code and trained model are available at graspnet.net.
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery

Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review of background theory and a survey of methods for structure discovery. We primarily focus on modern, continuous optimization methods, and provide reference to further resources such as benchmark datasets and software packages. Finally, we discuss the assumptive leap required to take us from structure to causality.
QoS-Driven Resource Optimization for Intelligent Fog Radio Access Network: A Dynamic Power Allocation Perspective

The fog radio access network (Fog-RAN) has been considered a promising wireless access architecture to help shorten the communication delay and relieve the large data delivery burden over the backhaul links. However, limited by conventional inflexible communication design, Fog-RAN cannot be used in some complex communication scenarios. In this study, we focus on investigating a more intelligent Fog-RAN to assist the communication in a high-speed railway environment. Due to the train’s continuously moving, the communication should be designed intelligently to adapt to channel variation. Specifically, we dynamically optimize the power allocation in the remote radio heads (RRHs) to minimize the total network power cost considering multiple quality-of-service (QoS) requirements and channel variation. The impact of caching on the power allocation is considered. The dynamic power optimization is analyzed to obtain a closed-form solution in certain cases. The inherent tradeoff among the total network cost, delay and delivery content size is further discussed. To evaluate the performance of the proposed dynamic power allocation, we present an invariant power allocation counterpart as a performance comparison benchmark. The result of our simulation reveals that dynamic power allocation can significantly outperform the invariant power allocation scheme, especially with a random caching strategy or limited caching resources at the RRHs.
An Iterative Contextualization Algorithm with Second-Order Attention

Combining the representations of the words that make up a sentence into a cohesive whole is difficult, since it needs to account for the order of words, and to establish how the words present relate to each other. The solution we propose consists in iteratively adjusting the context. Our algorithm starts with a presumably erroneous value of the context, and adjusts this value with respect to the tokens at hand. In order to achieve this, representations of words are built combining their symbolic embedding with a positional encoding into single vectors. The algorithm then iteratively weighs and aggregates these vectors using our novel second-order attention mechanism. Our models report strong results in several well-known text classification tasks.
Multiple imputation with missing data indicators

Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can generalize the sequential regression multiple imputation imputation procedure to handle missingness not at random in the setting where missingness may depend on other variables that are also missing but not on the missing variable itself, conditioning on fully observed variables. We provide algebraic justification for several generalizations of standard sequential regression multiple imputation using Taylor series and other approximations of the target imputation distribution under missingness not at random. Resulting regression model approximations include indicators for missingness, interactions, or other functions of the missingness not at random missingness model and observed data. In a simulation study, we demonstrate that the proposed sequential regression multiple imputation modifications result in reduced bias in the final analysis compared to standard sequential regression multiple imputation, with an approximation strategy involving inclusion of an offset in the imputation model performing the best overall. The method is illustrated in a breast cancer study, where the goal is to estimate the prevalence of a specific genetic pathogenic variant.