Miracle-dz/UniFAR-FTUs huggingface dataset:
Frontiers in Synaptic Neuroscience Synaptic Neuroscience

(1) Unlike in vitro, in vivo there are continuous barrages of ongoing presynaptic activity impinging on the dendritic tree that can generate postsynaptic spikes (Figures 1A,B) These spikes backpropa-gate throughout the neuron (Waters et al., 2003; Waters and Helmchen, 2004), potentially interacting with the vast number of dendritically located synapses as these synapses continue to receive barrages of excitatory inputs (Figure 1C 2). Under these conditions, the implications of STDP rules on individual synapses would be that a synapse active just prior to a spike event will increase in efficacy, whereas a synapse that is active just after the spike, will decrease its efficacy. The question arises, whether the mere association of presynaptic input and postsynaptic spiking activity would be enough to alter synaptic efficacy, and whether individual synapses in turn continuously scale up and down as inputs and backpro-pagating spikes constantly interact? Moreover, do spontaneously occurring spikes (Figure 1C 2) and stimulus-evoked spikes (Figure 1C 3) equally change synaptic weights as they interact with presynaptic input (Figure 1D)? When a spike is fired, whether it is spontaneous or evoked, how are active synaptic inputs that are driven by a stimulus separated from those that are due to the ongoing activity? One possible solution to these selectivity problems was originally proposed in relation to reward mediated learning (Miller, 1981; Wickens, 1990). These theoretical studies proposed that, in addition
Virtual Mixup Training for Unsupervised Domain Adaptation

We study the problem of unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain. Recently, the cluster assumption has been applied to unsupervised domain adaptation and achieved strong performance. One critical factor in successful training of the cluster assumption is to impose the locally-Lipschitz constraint to the model. Existing methods only impose the locally-Lipschitz constraint around the training points while miss the other areas, such as the points in-between training data. In this paper, we address this issue by encouraging the model to behave linearly in-between training points. We propose a new regularization method called Virtual Mixup Training (VMT), which is able to incorporate the locally-Lipschitz constraint to the areas in-between training data. Unlike the traditional mixup model, our method constructs the combination samples without using the label information, allowing it to apply to unsupervised domain adaptation. The proposed method is generic and can be combined with most existing models such as the recent state-of-the-art model called VADA. Extensive experiments demonstrate that VMT significantly improves the performance of VADA on six domain adaptation benchmark datasets. For the challenging task of adapting MNIST to SVHN, VMT can improve the accuracy of VADA by over 30\%. Code is available at \url{this https URL}.
Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs

This paper establishes that optimistic algorithms attain gap-dependent and non-asymptotic logarithmic regret for episodic MDPs. In contrast to prior work, our bounds do not suffer a dependence on diameter-like quantities or ergodicity, and smoothly interpolate between the gap dependent logarithmic-regret, and the $\widetilde{\mathcal{O}}(\sqrt{HSAT})$-minimax rate. The key technique in our analysis is a novel "clipped" regret decomposition which applies to a broad family of recent optimistic algorithms for episodic MDPs.
Kindling the Darkness: A Practical Low-light Image Enhancer

Images captured under low-light conditions often suffer from (partially) poor visibility. Besides unsatisfactory lightings, multiple types of degradations, such as noise and color distortion due to the limited quality of cameras, hide in the dark. In other words, solely turning up the brightness of dark regions will inevitably amplify hidden artifacts. This work builds a simple yet effective network for Kindling the Darkness (denoted as KinD), which, inspired by Retinex theory, decomposes images into two components. One component (illumination) is responsible for light adjustment, while the other (reflectance) for degradation removal. In such a way, the original space is decoupled into two smaller subspaces, expecting to be better regularized/learned. It is worth to note that our network is trained with paired images shot under different exposure conditions, instead of using any ground-truth reflectance and illumination information. Extensive experiments are conducted to demonstrate the efficacy of our design and its superiority over state-of-the-art alternatives. Our KinD is robust against severe visual defects, and user-friendly to arbitrarily adjust light levels. In addition, our model spends less than 50ms to process an image in VGA resolution on a 2080Ti GPU. All the above merits make our KinD attractive for practical use.
Functional renormalization group for frustrated magnets with nondiagonal spin interactions

In the field of quantum magnetism, the advent of numerous spin-orbit assisted Mott insulating compounds, such as the family of Kitaev materials, has led to a growing interest in studying general spin models with non-diagonal interactions that do not retain the SU(2) invariance of the underlying spin degrees of freedom. However, the exchange frustration arising from these non-diagonal and often bond-directional interactions for two- and three-dimensional lattice geometries poses a serious challenge for numerical many-body simulation techniques. In this paper, we present an extended formulation of the pseudo-fermion functional renormalization group that is capable of capturing the physics of frustrated quantum magnets with generic (diagonal and off-diagonal) two-spin interaction terms. Based on a careful symmetry analysis of the underlying flow equations, we reveal that the computational complexity grows only moderately, as compared to models with only diagonal interaction terms. We apply the formalism to a kagome antiferromagnet which is augmented by general in-plane and out-of-plane Dzyaloshinskii-Moriya (DM) interactions, as argued to be present in the spin liquid candidate material herbertsmithite. We calculate the complete ground state phase diagram in the strength of in-plane and out-of-plane DM couplings, and discuss the extended stability of the spin liquid of the unperturbed kagome antiferromagnet in the presence of these couplings.
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition

Occlusion and pose variations, which can change facial appearance significantly, are two major obstacles for automatic Facial Expression Recognition (FER). Though automatic FER has made substantial progresses in the past few decades, occlusion-robust and pose-invariant issues of FER have received relatively less attention, especially in real-world scenarios. This paper addresses the real-world pose and occlusion robust FER problem in the following aspects. First, to stimulate the research of FER under real-world occlusions and variant poses, we annotate several in-the-wild FER datasets with pose and occlusion attributes for the community. Second, we propose a novel Region Attention Network (RAN), to adaptively capture the importance of facial regions for occlusion and pose variant FER. The RAN aggregates and embeds varied number of region features produced by a backbone convolutional neural network into a compact fixed-length representation. Last, inspired by the fact that facial expressions are mainly defined by facial action units, we propose a region biased loss to encourage high attention weights for the most important regions. We validate our RAN and region biased loss on both our built test datasets and four popular datasets: FERPlus, AffectNet, RAF-DB, and SFEW. Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose. Our method also achieves state-of-the-art results on FERPlus, AffectNet, RAF-DB, and SFEW. Code and the collected test data will be publicly available.
Open-source RANs in Practice: an Over-The-Air Deployment for 5G MEC

Edge computing that leverages cloud resources to the proximity of user devices is seen as the future infrastructure for distributed applications. However, developing and deploying edge applications, that rely on cellular networks, is burdensome. Such network infrastructures are often based on proprietary components, each with unique programming abstractions and interfaces. To facilitate straightforward deployment of edge applications, we introduce open-source software (OSS) based radio access network (RAN) on over-the-air (OTA) commercial spectrum with Development Operations (DevOps) capabilities. OSS allows software modifications and integrations of the system components, e.g., Evolved Packet Core (EPC) and edge hosts running applications, required for new data pipelines and optimizations not addressed in standardization. Such an OSS infrastructure enables further research and prototyping of novel end-user applications in an environment familiar to software engineers without telecommunications background. We evaluated the presented infrastructure with end-to-end (E2E) OTA testing, resulting in 7.5MB/s throughput and latency of 21ms, which shows that the presented infrastructure provides low latency for edge applications.
On the Inevitability of Online Echo Chambers

While social media make it easy to connect with and access information from anyone, they also facilitate basic influence and unfriending mechanisms that may lead to segregated and polarized clusters known as "echo chambers. " Here we study the conditions in which such echo chambers emerge by introducing a simple model of information sharing in online social networks with the two ingredients of influence and unfriending. Users can change both opinions and social connections based on the information to which they are exposed through sharing. Model dynamics demonstrate that even with minimal amounts of influence and unfriending, the social network rapidly devolves into segregated, homogeneous communities. These predictions are consistent with empirical data from Twitter. Although our findings suggest that echo chambers are somewhat inevitable given the mechanisms at play in online social media, they also provide insights into possible mitigation strategies.
How to design work related information for low-literate employees?

A relatively high percentage of the workforce has lower literacy levels, which means that these employees have problems understanding written text. We have designed a brochure for a specific occupational group: landscape workers, people who work in urban and rural green spaces. This group bears a high risk of tick bites and, therefore, of contracting Lyme disease. The text and lay-out of the brochure are adapted according to the European Standards for making information easy to read and to understand. Four different versions of this brochure are designed and tested to investigate the effects of adding pictograms and a motivational agent to the adapted text. It was expected that these additions would be beneficial for low-literate landscape workers. The results of the study show that the participants appreciated the brochure, that they comprehended the brochure quite well and that they intended to perform the described protective measures. However, no positive effects of adding pictograms or a motivational agent are found. So, it seems to be more important to adapt information according to existing guidelines for easy to read text than to try to help low-literate readers by adding extra information.
GAN-Based Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system, which aims to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in one base station on sharing the same bandwidth. Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying demands and the allocated bandwidth as the environment \emph{state} and \emph{action}, respectively. In order to obtain better quality of experience (QoE) satisfaction ratio and spectrum efficiency (SE), we propose generative adversarial network (GAN) based deep distributional Q network (GAN- DDQN) to learn the distribution of state-action values. Furthermore, we estimate the distributions by approximating a full quantile function, which can make the training error more controllable. In order to protect the stability of GAN-DDQN's training process from the widely-spanning utility values, we also put forward a reward-clipping mechanism. Finally, we verify the performance of the proposed GAN-DDQN algorithm through extensive simulations.
Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network

The Legal Judgment Prediction (LJP) is to determine judgment results based on the fact descriptions of the cases. LJP usually consists of multiple subtasks, such as applicable law articles prediction, charges prediction, and the term of the penalty prediction. These multiple subtasks have topological dependencies, the results of which affect and verify each other. However, existing methods use dependencies of results among multiple subtasks inefficiently. Moreover, for cases with similar descriptions but different penalties, current methods cannot predict accurately because the word collocation information is ignored. In this paper, we propose a Multi-Perspective Bi-Feedback Network with the Word Collocation Attention mechanism based on the topology structure among subtasks. Specifically, we design a multi-perspective forward prediction and backward verification framework to utilize result dependencies among multiple subtasks effectively. To distinguish cases with similar descriptions but different penalties, we integrate word collocations features of fact descriptions into the network via an attention mechanism. The experimental results show our model achieves significant improvements over baselines on all prediction tasks.
ModGen: Theorem Proving by Model Generation

ModGen (Model Generation) is a complete theorem prover for first order logic with finite Herbrand domains. ModGen takes first order formulas as input, and generates models of the input formulas. ModGen consists of two major modules: a module for transforming the input formulas into propositional clauses, and a module to find models of the propositional clauses. The first module can be used by other researchers so that the SAT problems can be easily represented, stored and communicated. An important issue in the design of ModGen is to ensure that transformed propositional clauses are satisfiable iff the original formulas are. The second module can be easily replaced by any advanced SAT problem solver. ModGen is easy to use and very efficient. Many problems which are hard for general resolution theorem provers are found easy for ModGen.
Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach

Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in HAR. Although these methods are fast and easy for implementation, they still have some limitations due to poor performance in a number of situations. In this paper, we propose a novel method based on the ensemble learning to boost the performance of these machine learning methods for HAR.
Developing understanding of the idea of communities of learners

The idea of a community of learners is based on the premise that learning occurs as people participate in shared endeavors with others, with all playing active but often asymmetrical roles in sociocultural activity. This contrasts with models of learning that are based on one‐sided notions of learning— either that it occurs through transmission of knowledge from experts or acquisition of knowledge by novices, with the learner or the others (respectively) in a passive role. In this paper, I develop the distinction between the community of learners and one‐sided approaches from the perspective of a theory of learning as participation, and use two lines of research to illustrate the transitions in perspective necessary to understand the idea of communities of learners. One line of research examines differing models of teaching and learning employed by caregivers and toddlers from Guatemalan Mayan and middle‐class European‐American families; the other line of research involves a study of how middle‐class pare.
Topological analyses in APP/PS1 mice reveal that astrocytes do not migrate to amyloid-β plaques

Significance We sought to identify forces shaping the interaction between astrocytes and amyloid-β plaques by performing a spatial analysis, using tools from statistical physics, of 3D images from living transgenic mice. We discovered that astrocytes are repelled by other astrocytes and by plaques. The implications are (i) that a tight balance of repulsive factors maintains the highly territorial astrocyte organization and (ii) that, contrary to a belief widely held by Alzheimer’s researchers, astrocytes do not break this order, migrate to plaques, and phagocytose them. We conclude that astrocytes do sense something within the plaque microenvironment, as shown by the dramatic up-regulation of their key molecule, GFAP, but there is little change in position. These small positional changes are directed away from plaques. Although the clustering of GFAP immunopositive astrocytes around amyloid-β plaques in Alzheimer’s disease has led to the widespread assumption that plaques attract astrocytes, recent studies suggest that astrocytes stay put in injury. Here we reexamine astrocyte migration to plaques, using quantitative spatial analysis and computer modeling to investigate the topology of astrocytes in 3D images obtained by two-photon microscopy of living APP/PS1 mice and WT littermates. In WT mice, cortical astrocyte topology fits a model in which a liquid of hard spheres exclude each other in a confined space. Plaques do not disturb this arrangement except at very large plaque loads, but, locally, cause subtle outward shifts of the astrocytes located in three tiers around plaques. These data suggest that astrocytes respond to plaque-induced neuropil injury primarily by changing phenotype, and hence function, rather than location.
The transcription factor RUNX2 regulates receptor tyrosine kinase expression in melanoma

Receptor tyrosine kinases-based autocrine loops largely contribute to activate the MAPK and PI3K/AKT pathways in melanoma. However, the molecular mechanisms involved in generating these autocrine loops are still largely unknown. In the present study, we examine the role of the transcription factor RUNX2 in the regulation of receptor tyrosine kinase (RTK) expression in melanoma. We have demonstrated that RUNX2-deficient melanoma cells display a significant decrease in three receptor tyrosine kinases, EGFR, IGF-1R and PDGFRβ. In addition, we found co-expression of RUNX2 and another RTK, AXL, in both melanoma cells and melanoma patient samples. We observed a decrease in phosphoAKT2 (S474) and phosphoAKT (T308) levels when RUNX2 knock down resulted in significant RTK down regulation. Finally, we showed a dramatic up regulation of RUNX2 expression with concomitant up-regulation of EGFR, IGF-1R and AXL in melanoma cells resistant to the BRAF V600E inhibitor PLX4720. Taken together, our results strongly suggest that RUNX2 might be a key player in RTK-based autocrine loops and a mediator of resistance to BRAF V600E inhibitors involving RTK up regulation in melanoma.
Adaptive controller for linear system with input delay and output disturbance

We present a new stabilization approach for a linear plant with input delay, parametric uncertainties, and an unknown harmonic disturbance affected the input and the output. To solve this problem, we combine the well-known predictor feedback approach with the state observer and adaptive scheme that identifies the frequency of the disturbance. Compared to the existing approaches, the structure of our adaptive scheme is the simplest and the results apply to plants that are unstable, non-minimum phase, and have an arbitrary relative degree.
Crossing Large Gaps: A Simulation Study of Stick Insect Behavior

The motor behavior of stick insects climbing over large gaps has already been the subject of many experimental studies. The searching movement of the legs after stepping into the gap is analyzed in the first part of this study. Based on these and earlier biological results, a simulation study is carried out using WALKNET, a neural network model of the stick insect walking system. Five new modules are implemented into the model in the form of a unit assembly system. The performance of the enhanced model is evaluated in a series of tests designed based on previous biological experiments. The simu lation study shows that with only four new modules (for searching movements, velocity control, a load effect and short steps), the performance of the model in a simulated gap crossing situation can be improved beyond the performance of the biological system under comparable conditions.
Towards provably secure trust and reputation systems in e-marketplaces

In this paper, we present a framework for evaluating the security of trust and reputation systems for electronic marketplaces populated with buying and selling agents. Our proposed framework offers a method for researchers to understand the security of their systems, and to provide precise guarantees of the degree of provable security that these systems offer. We demonstrate the viability of our proposed framework by presenting a specific monetary-based trust system known as Trunits, along with an analysis that shows that Trunits provides a guaranteed level of security for buyers.
MdMYB46 could enhance salt and osmotic stress tolerance in apple by directly activating stress‐responsive signals

Summary To expand the cultivation area of apple (Malus×domestica Borkh.) and select resistant varieties by genetic engineering, it is necessary to clarify the mechanism of salt and osmotic stress tolerance in apple. The MdMYB46 transcription factor was identified, and the stress treatment test of MdMYB46‐overexpressing and MdMYB46‐RNAi apple lines indicated that MdMYB46 could enhance the salt and osmotic stress tolerance in apple. In transgenic Arabidopsis and apple, MdMYB46 promoted the biosynthesis of secondary cell wall and deposition of lignin by directly binding to the promoter of lignin biosynthesis‐related genes. To explore whether MdMYB46 could coordinate stress signal transduction pathways to cooperate with the formation of secondary walls to enhance the stress tolerance of plants, MdABRE1A, MdDREB2A and dehydration‐responsive genes MdRD22 and MdRD29A were screened out for their positive correlation with osmotic stress, salt stress and the transcriptional level of MdMYB46. The further verification test demonstrated that MdMYB46 could activate their transcription by directly binding to the promoters of these genes. The above results indicate that MdMYB46 could enhance the salt and osmotic stress tolerance in apple not only by activating secondary cell wall biosynthesis pathways, but also by directly activating stress‐responsive signals.