Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Modal and Multi-Factor Branching Time Active Inference

Active inference is a state-of-the-art framework for modelling the brain that explains a wide range of mechanisms such as habit formation, dopaminergic discharge and curiosity. Recently, two versions of branching time active inference (BTAI) based on Monte-Carlo tree search have been developed to handle the exponential (space and time) complexity class that occurs when computing the prior over all possible policies up to the time horizon. However, those two versions of BTAI still suﬀer from an exponential complexity class w.r. t the number of observed and latent variables being modelled. In the present paper, we resolve this limitation by ﬁrst allowing the modelling of several observations, each of them having its own likelihood mapping. Similarly, we allow each latent state to have its own transition mapping. The inference algorithm then exploits the factorisation of the likelihood and transition mappings to accelerate the computation of the posterior. Those two optimisations were tested on the dSprites environment in which the metadata of the dSprites dataset was used as input to the model instead of the dSprites images. On this task, BTAI V MP (Champion et al., 2022b,a) was able to solve 96.9% of the task in 5.1 seconds, and BTAI BF (Champion et al., 2021a) was able to solve 98.6% of the task in 17.5 seconds. Our new approach ( BTAI 3 MF ) outperformed both of its predecessors by solving the task completly (100%) in only 2.559 seconds. Finally, BTAI 3 MF has been implemented in a ﬂexible and easy to use (python) package, and we developed a graphical user interface to enable the inspection of the model’s beliefs, planning process
Groups acting on hyperbolic spaces -- a survey

This is a (very subjective) survey paper for nonspecialists covering group actions on Gromov hyperbolic spaces. The ﬁrst section is about hyperbolic groups themselves, while the rest of the paper focuses on mapping class groups and Out ( F n ), and the way to understand their large scale geometry using their actions on various hyperbolic spaces constructed using projection complexes. This understanding for Out ( F n ) signiﬁcantly lags behind that of mapping class groups and the paper ends with a few open questions.
Discovering Salient Neurons in Deep NLP Models

While a lot of work has been done in understanding representations learned within deep NLP models and what knowledge they capture, little attention has been paid towards individual neurons. We present a technique called as Linguistic Correlation Analysis to extract salient neurons in the model, with respect to any extrinsic property - with the goal of understanding how such a knowledge is preserved within neurons. We carry out a fine-grained analysis to answer the following questions: (i) can we identify subsets of neurons in the network that capture specific linguistic properties? (ii) how localized or distributed neurons are across the network? iii) how redundantly is the information preserved? iv) how fine-tuning pre-trained models towards downstream NLP tasks, impacts the learned linguistic knowledge? iv) how do architectures vary in learning different linguistic properties? Our data-driven, quantitative analysis illuminates interesting findings: (i) we found small subsets of neurons that can predict different linguistic tasks, ii) with neurons capturing basic lexical information (such as suffixation) localized in lower most layers, iii) while those learning complex concepts (such as syntactic role) predominantly in middle and higher layers, iii) that salient linguistic neurons are relocated from higher to lower layers during transfer learning, as the network preserve the higher layers for task specific information, iv) we found interesting differences across pre-trained models, with respect to how linguistic information is preserved within, and v) we found that concept exhibit similar neuron distribution across different languages in the multilingual transformer models. Our code is publicly available as part of the NeuroX toolkit.
Adaptive Neural Network Stochastic-Filter-Based Controller for Attitude Tracking With Disturbance Rejection

This article proposes a real-time neural network (NN) stochastic filter-based controller on the Lie group of the special orthogonal group SO(3) as a novel approach to the attitude tracking problem. The introduced solution consists of two parts: a filter and a controller. First, an adaptive NN-based stochastic filter is proposed, which estimates attitude components and dynamics using measurements supplied by onboard sensors directly. The filter design accounts for measurement uncertainties inherent to the attitude dynamics, namely, unknown bias and noise corrupting angular velocity measurements. The closed-loop signals of the proposed NN-based stochastic filter have been shown to be semiglobally uniformly ultimately bounded (SGUUB). Second, a novel control law on SO(3) coupled with the proposed estimator is presented. The control law addresses unknown disturbances. In addition, the closed-loop signals of the proposed filter-based controller have been shown to be SGUUB. The proposed approach offers robust tracking performance by supplying the required control signal given data extracted from low-cost inertial measurement units. While the filter-based controller is presented in continuous form, the discrete implementation is also presented. In addition, the unit-quaternion form of the proposed approach is given. The effectiveness and robustness of the proposed filter-based controller are demonstrated using its discrete form and considering low sampling rate, high initialization error, high level of measurement uncertainties, and unknown disturbances.
Wideband Audio Waveform Evaluation Networks: Efficient, Accurate Estimation of Speech Qualities

Speech quality and speech intelligibility can vary dramatically across the wide range of currently available telecommunications systems, devices, and operating environments. This creates a strong demand for efficient real-time measurements of quality and intelligibility. Wideband Audio Waveform Evaluation Networks (WAWEnets) are convolutional neural networks (CNNs) that operate directly on wideband audio waveforms in order to produce evaluations of those waveforms. In the present work these evaluations give qualities of telecommunications speech (e.g., noisiness, intelligibility, overall speech quality). WAWEnets are no-reference networks because they do not require “reference” (original or undistorted) versions of the waveforms they evaluate. Our initial 2020 WAWEnet publication introduces four WAWEnets and each emulates the output of an established full-reference speech quality or intelligibility estimation algorithm. We have updated the WAWEnet architecture to be more efficient and effective. Here we present a single WAWEnet that closely tracks seven different quality and intelligibility values with per-segment correlations in the range of 0.92 to 0.96. We create a second network that additionally tracks four subjective speech quality dimensions. We offer a third network that focuses on just subjective quality scores and achieves a per-segment correlation of 0.97. The performance of our WAWEnet architecture compares favorably to models with orders-of-magnitude more parameters and computational complexity. This work has leveraged 334 hours of speech in 13 languages, more than two million full-reference target values, and more than 93,000 subjective mean opinion scores. We also interpret the operation of WAWEnets and identify the key to their operation using the language of signal processing: ReLUs strategically move spectral information from non-DC components into the DC component. The DC values of 96 output signals define a vector in a 96-D latent space, and this vector is then mapped to a quality or intelligibility value for the input waveform.
Understanding Benign Overfitting in Nested Meta Learning

Meta learning has demonstrated tremendous success in few-shot learning with limited supervised data. In those settings, the meta model is usually overparameterized. While the conventional statistical learning theory suggests that overparameterized models tend to overﬁt, empirical evidence reveals that overparameterized meta learning methods still work well – a phenomenon often called “benign overﬁtting. ” To understand this phenomenon, we focus on the meta learning settings with a challenging nested structure that we term the nested meta learning, and analyze its generalization performance under an overparameterized meta learning model. While our analysis uses the relatively tractable linear models, our theory contributes to understanding the delicate interplay among data heterogeneity, model adaptation and benign overﬁtting in nested meta learning tasks. We corroborate our theoretical claims through numerical simulations.
On single distribution lattice Boltzmann schemes for the approximation of Navier Stokes equations

In this contribution we study the formal ability of a multi-resolution-times lattice Boltzmann scheme to approximate isothermal and thermal compressible Navier Stokes equations with a single particle distribution. More precisely, we consider a total of 12 classical square lattice Boltzmann schemes with prescribed sets of conserved and nonconserved moments. The question is to determine the algebraic expressions of the equilibrium functions for the nonconserved moments and the relaxation parameters associated to each scheme. We compare the fluid equations and the result of the Taylor expansion method at second order accuracy for bidimensional examples with a maximum of 17 velocities and three-dimensional schemes with at most 33 velocities. In some cases, it is not possible to fit exactly the physicalmodel. For several examples, we adjust the Navier Stokes equations and propose nontrivial expressions for the equilibria.
p-Meta: Towards On-device Deep Model Adaptation

Data collected by IoT devices are often private and have a large diversity across users. Therefore, learning requires pre-training a model with available representative data samples, deploying the pre-trained model on IoT devices, and adapting the deployed model on the device with local data. Such an on-device adaption for deep learning empowered applications demands data and memory efficiency. However, existing gradient-based meta learning schemes fail to support memory-efficient adaptation. To this end, we propose p-Meta, a new meta learning method that enforces structure-wise partial parameter updates while ensuring fast generalization to unseen tasks. Evaluations on few-shot image classification and reinforcement learning tasks show that p-Meta not only improves the accuracy but also substantially reduces the peak dynamic memory by a factor of 2.5 on average compared to state-of-the-art few-shot adaptation methods.
Compressing Features for Learning With Noisy Labels

Supervised learning can be viewed as distilling relevant information from input data into feature representations. This process becomes difficult when supervision is noisy as the distilled information might not be relevant. In fact, recent research shows that networks can easily overfit all labels including those that are corrupted, and hence can hardly generalize to clean datasets. In this article, we focus on the problem of learning with noisy labels and introduce compression inductive bias to network architectures to alleviate this overfitting problem. More precisely, we revisit one classical regularization named Dropout and its variant Nested Dropout. Dropout can serve as a compression constraint for its feature dropping mechanism, while Nested Dropout further learns ordered feature representations with respect to feature importance. Moreover, the trained models with compression regularization are further combined with co-teaching for performance boost. Theoretically, we conduct bias variance decomposition of the objective function under compression regularization. We analyze it for both single model and co-teaching. This decomposition provides three insights: 1) it shows that overfitting is indeed an issue in learning with noisy labels; 2) through an information bottleneck formulation, it explains why the proposed feature compression helps in combating label noise; and 3) it gives explanations on the performance boost brought by incorporating compression regularization into co-teaching. Experiments show that our simple approach can have comparable or even better performance than the state-of-the-art methods on benchmarks with real-world label noise including Clothing1M and ANIMAL-10N. Our implementation is available at https://yingyichen-cyy.github. io/CompressFeatNoisyLabels/.
Improving Policy Optimization with Generalist-Specialist Learning

Generalization in deep reinforcement learning over unseen environment variations usually requires policy learning over a large set of diverse training variations. We empirically observe that an agent trained on many variations (a generalist) tends to learn faster at the beginning, yet its performance plateaus at a less optimal level for a long time. In contrast, an agent trained only on a few variations (a specialist) can often achieve high returns under a limited computational budget. To have the best of both worlds, we propose a novel generalist-specialist training framework. Specifically, we first train a generalist on all environment variations; when it fails to improve, we launch a large population of specialists with weights cloned from the generalist, each trained to master a selected small subset of variations. We finally resume the training of the generalist with auxiliary rewards induced by demonstrations of all specialists. In particular, we investigate the timing to start specialist training and compare strategies to learn generalists with assistance from specialists. We show that this framework pushes the envelope of policy learning on several challenging and popular benchmarks including Procgen, Meta-World and ManiSkill.
State of Public Sector Risk Management implementation in Germany

While many studies assess risk management in a private sector context, public sector risk management and particularly with a focus on German municipalities is lagging behind. One main question in this regard is what factors drive the implementation of risk management in German municipalities. This article addresses that research gap by means of a structural equation model. The results evidence that the stage of risk management implementation is positively related to six factors and the implementation stage of risk management is positively related to the quality of risk management. The organisation size shows a moderating effect on the results.
Response of a generalized Langevin system to a multiplicative trichotomous noise

Motivated by subdiffusive motion of biomolecules observed in living cells, we explore the phenomenon of stochastic resonance in a system described by a generalized Langevin equation subjected to an external periodic force. The influence of fluctuations of environmental parameters on the system is modeled by a multiplicative three-level Markovian noise and by an internal Mittag-Leffler noise. Starting from a generalized Langevin equation, we derive an analytical expression for the first moment of the output signal, which exhibits a resonance-like nonmonotonic dependence on noise parameters. The conditions for the occurrence of stochastic resonance are found and illustrated by phase diagrams. The influences of the memory time of the internal noise and the flatness of multiplicative noise on the dynamics of the system are also discussed.
Beyond the two-infall model with Gaia DR3 α -element abundances I. Evidence for a recent gas infall in the solar vicinity

Context. The recent Gaia Data Release 3 (DR3) represents an unparalleled revolution in Galactic Archaeology, providing us with numerous radial velocities chemical abundances for million of stars, overcoming the spatial biases that ground-based spectroscopic surveys su ﬀ er from ground-based surveys. Aims. We present a new chemical evolution model for the Galactic disc components (high- and low- α sequence stars) designed to reproduce the new abundance ratios provided by the GSP-spec module for the Gaia DR3 and also constrained by i) stellar ages and ii) detailed star formation histories for both the thick and thin disc stars inferred from previous Gaia releases. Methods. Gaia DR3 highlighted the presence of young (massive) low- α disc stars which show evidence of a recent chemical impov- erishment in several elements. Additionally, previous Gaia releases have found evidence for narrow episodes of enhanced SF inferred in recent time. In order to reproduce these observables, we propose a new chemical evolution model in which the low- α sequence is generated by two distinct infall episodes. Hence, in this study we compare Gaia DR3 chemical abundances with the predictions of a three-infall chemical evolution model for the high- and low- α components. Results. The proposed three-infall chemical evolution model nicely reproduces the main features of the abundance ratio [X / Fe] versus [M / H] (X = Mg, Si, Ca, Ti, α ) of Gaia DR3 stars in di ﬀ erent age bins for the considered α elements. Moreover, the most recent gas infall - which started ∼ 2.7 Gyr ago - allows us to predict well the Gaia DR3 young population which has experienced a recent chemical impoverishment. Conclusions. We extended previous chemical evolution model designed to reproduce APOGEE and APOKASC data to predict new Gaia DR3 chemical abundances. To this aim we proposed a three-infall chemical evolution model in order to trace better both i) the young population in Gaia DR3 with evidence of chemical impoverishment and ii) the star formation history from previous Gaia releases.
n Chronic fluoxetine treatment increases daytime melatonin synthesis in the

mali.wong@anu.edu. au Abstract: Circadian rhythm disturbances can occur as part of the clinical symptoms of major depressive disorder and have been found to resolve with antidepressant therapy. The pineal gland is relevant to circadian rhythms as it secretes the hormone melatonin following activation of the cyclic adenosine monophosphate (cAMP) signaling cascade and of arylalkylamine N-acetyltransferase (AA-NAT), the rate-limiting enzyme for its synthesis. Cyclic AMP is synthesized by adenylate cyclases (AC) and degraded by phosphodiesterases (PDEs). Little is known about the contribution of the PDE system to antidepressant-induced alterations in pineal cAMP signaling and melatonin synthesis. In the present study we used enzyme immunoassay to measure plasma melatonin levels and pineal cAMP levels and as well as quantitative real-time polymerase chain reaction to measure pineal expression of PDE, AC, and AA-NAT genes in rats chronically treated with the prototypic antidepressant fluoxetine. We found elevated melatonin synthesis with increased pineal AA-NAT gene expression and daytime plasma melatonin levels and downregulated cAMP signaling with increased PDE and unchanged AC pineal gene expression, and decreased content of pineal cAMP. We conclude that chronic fluoxetine treatment increases daytime plasma melatonin and pineal AA-NAT gene expression despite downregulated pineal cAMP signaling in the gland
DCSIGN mediated internalisation of glycosylated extracellular vesicles from Schistosoma mansoni increases activation of monocytederived dendritic cells

DCSIGN mediated internalisation of glycosylated extracellular vesicles from Schistosoma mansoni increases activation of monocyte derived dendritic cells. ABSTRACT Helminths like Schistosoma mansoni release excretory/secretory (E/S) products that modulate host immunity to enable infection. Extracellular vesicles (EVs) are among these E/S products, yet molecular mechanisms and functionality of S. mansoni EV interaction with host immune cells is unknown. Here we demonstrate that EVs released by S. mansoni schistosomula are internalised by human monocyte-derived dendritic cells (moDCs). Importantly, we show that this uptake was mainly mediated via DC-SIGN (CD209). Blocking DC-SIGN almost completely abrogated EV uptake, while blocking mannose receptor (MR, CD206) or dendritic cell immunoreceptor (DCIR, CLEC4A) had no effect on EV uptake. Mass spectrometric analysis of EV glycans revealed the presence of surface N-glycans with terminal Gal β 1-4(Fuc α 1-3)GlcNAc (LewisX) motifs, and a wide array of fucosylated lipid-linked glycans, including LewisX, a known ligand for DC-SIGN. EVs led to increased expression of costimulatory molecules CD86 and CD80 and regulatory surface marker PD-L1. Furthermore, schistosomula EVs increased expression of IL-12 and IL-10 by moDCs, which was partly dependent on the interaction with DC-SIGN. These results provide the first evidence that glycosylation of S. mansoni EVs facilitates the interaction with host immune cells and reveals a role for DC-SIGN and EV- associated glycoconjugates in parasite-induced immune modulation.
Cholinergic regulation of ghrelin and PYY release may be impaired in obesity

This is an uncopyedited electronic version of an article accepted for publication in Diabetes. The American Diabetes Association, publisher of Diabetes, is not responsible for any errors or omissions in this version of the manuscript or any version derived from it by third parties. The definitive publisher-authenticated version will be available in a future issue of Diabetes in print and online at http://diabetes.diabetesjournals. org. Diabetes Publish Ahead of Print, published online June 20, 2008
BROOD STOCK MANAGEMENT AND INDUCED BREEDING OF THAI PANGUS ( Pangasius hypophthalmus ) PRACTICED IN THE HATCHERIES OF JESSORE REGION, BANGLADESH Breeding Pangus Pangasius hypophthalmus ) Practiced in of

stock Management and Induced of ( the Abstract The study was conducted on brood stock management and induced breeding of Thai Pangus in a hatchery of Jessore region. Brood fishes were reared in the brood rearing pond by providing artificial diet for good health and full maturation. The water quality parameters; temperature, p H , dissolved oxygen and transparency were recorded from 25-30 0 c, 7-8.5, 4.5-7.1 ppm and 23- 31 cm in the brood rearing ponds respectively. The experiment was conducted on the induced breeding of Pangasius hypophthalmus in a hatchery by using pituitary gland (PG) hormone. The present study consisted of three treatments (T I , T 2 and T 3 ) with three replications of each. The eggs and sperms were obtained just by stripping and then fertilization was done by artificial insemination. Nine pairs of male and five pairs of female were selected for induced breeding and the average body wt. of the female and male were 4.47±0.55 kg and 3.65±0.44 kg respectively. To observe the effective dose for induced breeding, the females were injected at the rate of 8 (T I ), 10 (T 2 ) and 12 (T 3 ) mg PG/kg body wt. and correspondingly the males were administered a dose of 1 mg PG/kg body wt. in all treatments. The male and female ratio was maintained as 2:1 for each treatment. The fertilization rate, hatching rate, deformity rate and survival rate were determined. The fertilized eggs were hatched within 22-24 hours respectively. The water temperature was recorded between 27 to 28.5 0 C during the experiment period. Among the three treatments T 3 showed the best result in terms of fertilization rate (87%), hatching rate (82%), deformity of larvae (6%) and survival rate (80%). The present findings can be used in induced breeding of P. hypophthalmus for the development of hatchery propagation. The overall breeding performances of P. hypophthalmus were found to be satisfactory for the commercial production of this fish in Bangladesh. fertilization (87%), hatching (82%), deformity (6%) and survival (80 %) rates were found among the three treatments. The highest percentage of fertilization, hatching and survival rate obtained in the present experiment may be considered as satisfactory and the highest deformity was found in treatment T 1 . However; there is scope for further improvement in the process. The present findings can be used in induced breeding of P. hypophthalmus in hatcheries.