Miracle-dz/UniFAR-FTUs huggingface dataset:
S0033291722000897jrv 1222..1242

Major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia-spectrum disorders (SSD) are heterogeneous psychiatric disorders, which place significant burden on patient’s well-being and global health. Disruptions in the gut-microbiome may play a role in these psychiatric disorders. This review presents current data on composition of the human gastrointestinal microbiota, and its interaction mechanisms in the gut–brain axis in MDD, BD and SSD. Diversity metrics and microbial relative abundance differed across studies. More studies reported inconsistent findings (n = 7) or no differences (n = 8) than studies who reported lower α-diversity in these psychiatric disorders (n = 5). The most consistent findings across studies were higher relative abundances of the genera Streptococcus, Lactobacillus, and Eggerthella and lower relative abundance of the butyrate producing Faecalibacterium in patients with psychiatric disorders. All three increased genera were associated with higher symptom severity. Confounders, such as medication use and life style have not been accounted for. So far, the results of probiotics trials have been inconsistent. Most traditional and widely used probiotics (consisting of Bifidobacterium spp. and Lactobacillus spp.) are safe, however, they do not correct potential microbiota disbalances in these disorders. Findings on prebiotics and faecal microbiota transplantation (FMT) are too limited to draw definitive conclusions. Disease-specific pro/prebiotic treatment or even FMT could be auspicious interventions for prevention and therapy for psychiatric disorders and should be investigated in future trials.
Atomic cross-chain exchanges of shared assets

A core enabler for blockchain or DLT interoperability is the ability to atomically exchange assets held by mutually untrusting owners on different ledgers. This atomic swap problem has been well-studied, with the Hash Time Locked Contract (HTLC) emerging as a canonical solution. HTLC ensures atomicity of exchange, albeit with caveats for node failure and timeliness of claims. But a bigger limitation of HTLC is that it only applies to a model consisting of two adversarial parties having sole ownership of a single asset in each ledger. Realistic extensions of the model in which assets may be jointly owned by multiple parties, all of whose consents are required for exchanges, or where multiple assets must be exchanged for one, are susceptible to collusion attacks and hence cannot be handled by HTLC. In this paper, we generalize the model of asset exchanges across DLT networks and present a taxonomy of use cases, describe the threat model, and propose MPHTLC, an augmented HTLC protocol for atomic multi-owner-and-asset exchanges. We analyze the correctness, safety, and application scope of MPHTLC. As proof-of-concept, we show how MPHTLC primitives can be implemented in networks built on Hyperledger Fabric and Corda, and how MPHTLC can be implemented in the Hyperledger Labs Weaver framework by augmenting its existing HTLC protocol.
Decomposing NeRF for Editing via Feature Field Distillation

Emerging neural radiance fields (NeRF) are a promising scene representation for computer graphics, enabling high-quality 3D reconstruction and novel view synthesis from image observations. However, editing a scene represented by a NeRF is challenging, as the underlying connectionist representations such as MLPs or voxel grids are not object-centric or compositional. In particular, it has been difficult to selectively edit specific regions or objects. In this work, we tackle the problem of semantic scene decomposition of NeRFs to enable query-based local editing of the represented 3D scenes. We propose to distill the knowledge of off-the-shelf, self-supervised 2D image feature extractors such as CLIP-LSeg or DINO into a 3D feature field optimized in parallel to the radiance field. Given a user-specified query of various modalities such as text, an image patch, or a point-and-click selection, 3D feature fields semantically decompose 3D space without the need for re-training and enable us to semantically select and edit regions in the radiance field. Our experiments validate that the distilled feature fields (DFFs) can transfer recent progress in 2D vision and language foundation models to 3D scene representations, enabling convincing 3D segmentation and selective editing of emerging neural graphics representations.
Semantic Autoencoder and Its Potential Usage for Adversarial Attack

—Autoencoder can give rise to an appropriate latent representation of the input data [1, 2], however, the representation which is solely based on the intrinsic property of the input data, is usually inferior to express some semantic information. A typical case is the potential incapability of forming a clear boundary upon clustering of these representations. By encoding the latent representation that not only depends on the content of the input data, but also the semantic of the input data, such as label information, we propose an enhanced autoencoder architecture named semantic autoencoder. Experiments of representation distribution via t-SNE shows a clear distinction between these two types of encoders and confirm the supremacy of the semantic one, whilst the decoded samples of these two types of autoencoders exhibit faint dissimilarity either objectively or subjectively. Based on this observation, we consider adversarial attacks to learning algorithms that rely on the latent representation obtained via autoencoders. It turns out that latent contents of adversarial samples constructed from semantic encoder with deliberate wrong label information exhibit different distribution compared with that of the original input data, while both of these samples manifest very marginal difference. This new way of attack set up by our work is worthy of attention due to the necessity to secure the widespread deep learning applications [3, 4].
Decentralized Computation of Wasserstein Barycenter over Time-Varying Networks

Inspired by recent advances in distributed algorithms for approximating Wasserstein barycenters, we propose a novel distributed algorithm for this problem. The main novelty is that we consider time-varying computational networks, which are motivated by examples when only a subset of sensors can make an observation at each time step, and yet, the goal is to average signals (e.g., satellite pictures of some area) by approximating their barycenter. We prove non-asymptotic accelerated in the sense of Nesterov convergence rates and explicitly characterize their dependence on the parameters of the network and its dynamics. Our approach is based on our novel distributed non-smooth optimization method on time-varying networks, which may be of separate interest. In the experiments, we demonstrate the efﬁciency of the proposed algorithm.
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game

Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms have been proposed for offline RL in the previous literature, the minimax optimality has only been (nearly) established for tabular Markov decision processes (MDPs). In this paper, we focus on offline RL with linear function approximation and propose a new pessimism-based algorithm for offline linear MDP. At the core of our algorithm is the uncertainty decomposition via a reference function, which is new in the literature of offline RL under linear function approximation. Theoretical analysis demonstrates that our algorithm can match the performance lower bound up to logarithmic factors. We also extend our techniques to the two-player zero-sum Markov games (MGs), and establish a new performance lower bound for MGs, which tightens the existing result, and verifies the nearly minimax optimality of the proposed algorithm. To the best of our knowledge, these are the first computationally efficient and nearly minimax optimal algorithms for offline single-agent MDPs and MGs with linear function approximation.
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning

We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in a trajectory independently when providing feedback on agent behaviour. In this work, we remove this assumption, extending RM to capture temporal dependencies in human assessment of trajectories. We show how RM can be approached as a multiple instance learning (MIL) problem, where trajectories are treated as bags with return labels, and steps within the trajectories are instances with unseen reward labels. We go on to develop new MIL models that are able to capture the time dependencies in labelled trajectories. We demonstrate on a range of RL tasks that our novel MIL models can reconstruct reward functions to a high level of accuracy, and can be used to train high-performing agent policies.
Post-hoc Concept Bottleneck Models

Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts (``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances interpretability since it can be investigated to understand what concepts the model"sees"in an input and which of these concepts are deemed important. However, CBMs are restrictive in practice as they require dense concept annotations in the training data to learn the bottleneck. Moreover, CBMs often do not match the accuracy of an unrestricted neural network, reducing the incentive to deploy them in practice. In this work, we address these limitations of CBMs by introducing Post-hoc Concept Bottleneck models (PCBMs). We show that we can turn any neural network into a PCBM without sacrificing model performance while still retaining the interpretability benefits. When concept annotations are not available on the training data, we show that PCBM can transfer concepts from other datasets or from natural language descriptions of concepts via multimodal models. A key benefit of PCBM is that it enables users to quickly debug and update the model to reduce spurious correlations and improve generalization to new distributions. PCBM allows for global model edits, which can be more efficient than previous works on local interventions that fix a specific prediction. Through a model-editing user study, we show that editing PCBMs via concept-level feedback can provide significant performance gains without using data from the target domain or model retraining.
Matrix-free Monolithic Multigrid Methods for Stokes and Generalized Stokes Problems

We consider the widely used continuous $\mathcal{Q}_{k}$-$\mathcal{Q}_{k-1}$ quadrilateral or hexahedral Taylor-Hood elements for the finite element discretization of the Stokes and generalized Stokes systems in two and three spatial dimensions. For the fast solution of the corresponding symmetric, but indefinite system of finite element equations, we propose and analyze matrix-free monolithic geometric multigrid solvers that are based on appropriately scaled Chebyshev-Jacobi smoothers. The analysis is based on results by Sch\"oberl and Zulehner (2003). We present and discuss several numerical results for typical benchmark problems.
Human Mobility Disproportionately Extends PM2.5 Emission Exposure for Low Income Populations

Ambient exposure to fine particulate matters of diameters smaller than 2.5{\mu}m (PM2.5) has been identified as one critical cause for respiratory disease. Disparities in exposure to PM2.5 among income groups at individual residences are known to exist and are easy to calculate. Existing approaches for exposure assessment, however, do not capture the exposure implied by the dynamic mobility of city dwellers that accounts for a large proportion of the exposure outside homes. To overcome the challenge of gauging the exposure to PM2.5 for city dwellers, we analyzed billions of anonymized and privacy-enhanced location-based data generated by mobile phone users in Harris County, Texas, to characterize the mobility patterns of the populations and associated exposure. We introduce the metric for exposure extent based on the time people spent at places with the air pollutant and examine the disparities in mobility-based exposure across income groups. Our results show that PM2.5 emissions disproportionately expose low-income populations due to their mobility activities. People with higher-than-average income are exposed to lower levels of PM2.5 emissions. These disparities in mobility-based exposure are the result of frequent visits of low-income people to the industrial sectors of urban areas with high PM2.5 emissions, and the larger mobility scale of these people for life needs. The results inform about environmental justice and public health strategies, not only to reduce the overall PM2.5 exposure but also to mitigate the disproportional impacts on low-income populations. The findings also suggest that an integration of extensive fine-scale population mobility and pollution emissions data can unveil new insights into inequality in air pollution exposures at the urban scale.
n-type diamond synthesized with tert-butylphosphine for long spin coherence times of perfectly aligned NV centers

The longest spin coherence times for nitrogen-vacancy (NV) centers at room temperature have been achieved in phosphorus-doped n-type diamond. However, difficulty controlling impurity incorporation and the utilization of highly toxic phosphine gas in the chemical vapor deposition (CVD) technique pose problems for the growth of n-type diamond. In the present study, n-type diamond samples were synthesized by CVD using tert-butylphosphine, which is much less toxic than phosphine. The unintentional incorporation of nitrogen was found to be suppressed by incrementally increasing the gas flow rates of H2 and CH4. It was found that the spin coherence time ( T2) increased with decreasing the nitrogen concentration, which suggests that the nitrogen concentration limits the length of T2. In the sample with the lowest nitrogen concentration, T2 increased to 1.62 ± 0.10 ms. Optically detected magnetic resonance spectra indicated that all of the measured NV centers were aligned along the [111] direction. Hall measurements confirmed n-type conduction in three measured samples prepared under different growth conditions. The highest measured Hall mobility at room temperature was 422 cm2/(V s). This study provides appropriate CVD conditions for growing phosphorus-doped n-type diamond with perfectly aligned NV centers exhibiting long spin coherence times, which is important for the production of quantum diamond devices.
hmBERT: Historical Multilingual Language Models for Named Entity Recognition

Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable corpora, the historical text is usually scanned and Optical Character Recognition (OCR) needs to be performed. As a result, the historical corpora contain errors. Also, entities like location or organization can change over time, which poses another challenge. Overall, historical texts come with several peculiarities that differ greatly from modern texts and large labeled corpora for training a neural tagger are hardly available for this domain. In this work, we tackle NER for historical German, English, French, Swedish, and Finnish by training large historical language models. We circumvent the need for large amounts of labeled data by using unlabeled data for pretraining a language model. We propose hmBERT, a historical multilingual BERT-based language model, and release the model in several versions of different sizes. Furthermore, we evaluate the capability of hmBERT by solving downstream NER as part of this year's HIPE-2022 shared task and provide detailed analysis and insights. For the Multilingual Classical Commentary coarse-grained NER challenge, our tagger HISTeria outperforms the other teams' models for two out of three languages.
Bayesian Modeling of Marketing Attribution

In a multi-channel marketing world, the purchase decision journey encounters many interactions (e.g., email, mobile notifications, display advertising, social media, and so on). These impressions have direct (main effects), as well as interactive influence on the final decision of the customer. To maximize conversions, a marketer needs to understand how each of these marketing efforts individually and collectively affect the customer's final decision. This insight will help her optimize the advertising budget over interacting marketing channels. This problem of interpreting the influence of various marketing channels to the customer's decision process is called marketing attribution. We propose a Bayesian model of marketing attribution that captures established modes of action of advertisements, including the direct effect of the ad, decay of the ad effect, interaction between ads, and customer heterogeneity. Our model allows us to incorporate information from customer's features and provides usable error bounds for parameters of interest, like the ad effect or the half-life of an ad. We apply our model on a real-world dataset and evaluate its performance against alternatives in simulations.
Dynamics of cascaded resonant radiations in a dispersion-varying optical fiber

We use a dispersion-varying optical ﬁber to experimentally explore the dynamics of resonant radiations emitted from Raman-shifting solitons in the vicinity of the second zero-dispersion. The evolving zero-dispersion wavelength with ﬁber length allows to observe unprecedented processes such as a cascade of resonant radiations, the emission of multiple resonant radiations from a single soliton and the generation of a 500 nm continuum exclusively composed of resonant radiations. All experiments are interpreted using numerical simulations of the generalized nonlinear Schrödinger equation which are in excellent agreement.
Preparation, Characterization and Photocatalytic Property of Eu-doped TiO 2 Hollow Microspheres

: Pure TiO 2 and Eu-doped TiO 2 hollow microspheres were prepared by Sol-Gel method and characterized by techniques such as XRD, SEM, TEM, HRTEM, BET and XPS. Photocatalytic degradation of methylene blue (MB) in aqueous solution was used as a probe reaction to evaluate their photocatalytic activity. The experimental results show that the dosage of TBOT has a great effect on the morphology of TiO 2 hollow microspheres. When the dosage of TBOT is 1.5 mL, the obtained hollow microspheres disperse well and show clear structure. XRD pattern indicates that TiO 2 hollow microspheres calcined at 400 present anatase structure ℃ and Eu-doped ones can restrain the anatase phase from converting to rutile phase. The experimental results suggest that Eu-doped can obviously improve the photocatalytic activity of TiO 2 hollow microsphere and when the doping of Eu is 0.7%, the photocatalytic degradation reaches the best because of its highest surface area and smallest crystallize size and pore diameter.
Rethinking the Internet of Things: A Scalable Approach to Connecting Everything

Over the next decade, most devices connected to the Internet will not be used by people in the familiar way that personal computers, tablets and smart phones are. Billions of interconnected devices will be monitoring the environment, transportation systems, factories, farms, forests, utilities, soil and weather conditions, oceans and resources. Many of these sensors and actuators will be networked into autonomous sets, with much of the information being exchanged machine-to-machine directly and without human involvement. Machine-to-machine communications are typically terse. Most sensors and actuators will report or act upon small pieces of information - chirps. Burdening these devices with current network protocol stacks is inefficient, unnecessary and unduly increases their cost of ownership. This must change. The architecture of the Internet of Things must evolve now by incorporating simpler protocols toward at the edges of the network, or remain forever inefficient. Rethinking the Internet of Things describes reasons why we must rethink current approaches to the Internet of Things. Appropriate architectures that will coexist with existing networking protocols are described in detail. An architecture comprised of integrator functions, propagator nodes, and end devices, along with their interactions, is explored. What youll learn Discusses the difference between the "normal" Internet and the Internet of Things. Describes a new architecture and its components in the "chirp" context. Explains the shortcomings of IP for IoT. Describes the anatomy of the IoT. Describes how to build a suitable network to maximize the amazing potential of the IoT. Who this book is for Thought leaders, executives, architectural, standards and development leaders in the evolving IoT industry. Corporations and organizations whose commercial products could be adapted simply to be functioning devices on the IOT while saving billions of dollars in unnecessary costs or proprietary designs. Those who wish to capitalize on technology change and those interested in the Internet, its capabilities and the need to improve it.
Epistemological Disjunctivism and Introspective Indiscriminability

According to Duncan Pritchard ’ s Philosophical Issues , 21 (1), 434 – 455, (2011, 2012, 2015)version of epistemological disjunctivism, in paradigm cases of perceptual knowledge, one ’ s knowledge that p is grounded in one ’ s seeing that p , and one can, by reflection alone, come to know that they see that p . In this paper, I argue that the epistemic conception of introspective indiscriminability is incompatible with epistemological disjunctivism, so understood. This has the consequence that theories of the nature of sensory experience which accept the epistemic conception of introspective indiscriminability — such as phenomenal character disjunctivism and certain forms of naïve realism — are inconsistent with epistemological disjunctivism, so understood. I then argue that proponents of epistemological disjunctivism face a formidable challenge explaining in what sense, if any, one can have purely reflective knowledge of their factive rational support.