Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical LTL-Task MDPs for Multi-Agent Coordination through Auctioning and Learning

Given a temporal behavior speciﬁcation and a team of agents available for execution in a stochastic environment, it is still an open problem how to efﬁciently decompose and allocate the speciﬁcation to the agents while coordinating their actions accordingly and while considering long-term performance under uncertain external events. Our proposed framework works towards this goal by constructing a so-called hierachical LTL-Task MDP automatically by formally decomposing a given temporal logic goal speciﬁcation into a set of smaller MDP planning problems. In order to efﬁciently ﬁnd a multi-agent policy in this generated LTL-Task MDP, we combine methods from planning under uncertainty and auction-based task allocation with techniques from reinforcement learning. A particular challenge is to consider uncertainty in the environment, which might require signiﬁcant additional effort to satisfy the given speciﬁcation. This is addressed here by a formalism that allows the agents to consider preparation of such possible future reactions instead of updating the set of tasks only after observing an event.
The perfecting of the octopus. Animal

: Cephalopods split away from the phylogenetic tree about half a billion years ago, and octopus evolution has been accelerated by an extremely low survival rate. This helps explain why this unusual animal presents qualities found in no other. It has a radially organized nervous system with a processing centre for each of its eight tentacles. Yet, although this might suggest that each tentacle has its own centre of consciousness, it remains just one animal, with one mouth to feed, and one life to lose, and it behaves as if it is centrally controlled. Its capacity for a range of intelligent and often cognitive behaviour suggests that it must have its own version of sentience.
The Importance of Human Resource Management in the Hotel industry in Sierra Leone

Human Resources are the most valuable and unique assets of an organization. The successful management of an organization’s human resources is an exciting dynamic and challenging task, especially at a time when the world has become a global village and economies are in a state of flux The purpose of this research is to examine the roles and functions of human resource management in the hotel Industry. It also detailed with the methods used together with all relevant information about the importance of human resource management in the hotel sector and how the collected data was analyzed with the use of simple percentages tables to illustrate the research findings. Finally, are the summary, conclusion and references made by the researcher based on the findings.
Kinetic Studies of the Lipid-activated Pyruvate Oxidase Flavoprotein of Escherichia coli*

Pyruvate oxidase is a flavoprotein dehydrogenase isolated from Escherichia coli which catalyzes the oxidative decarboxylation of pyruvate to acetate and CO,. In vivo, the enzyme can bind to the bacterial membrane and reduce ubiquinone-8, feeding electrons into the respiratory chain. The purified enzyme has been shown previously $0 bind to phospholipids and detergents and, upon doing so, is activated. The turnover with ferricyanide as an electron acceptor increases 20to 30-fold upon lipid binding. In this work, initial velocity and stop-flow kinetics are used to investigate the activation of this enzyme. It is shown that the unactivated form of the enzyme is markedly hysteretic. Progress curves at low substrate concentrations show an initial acceleration in enzyme turnover. This is consistent with the results of stop-flow experiments. Rates obtained for either the reduction of the unactivated flavoprotein by pyruvate or its reoxidation by ferricyanide in single turnover experiments are much slower than the rates predicted by observed turnover in initial velocity studies, in some cases by more than 2 orders of magnitude. The data are b st explained by the slow interconversion between two forms of the enzyme, one with low turnover and one which rapidly turns over. As isolated, the enzyme is highly unreactive, as revealed by the stop-flow experiments. During turnover, even in the absence of lipid activators, some of the enzyme converts to the rapid-turnover form. This slow interconversion is shown by kinetic simulation to preclude a steady state from being established. Lipid activators appear to shift the equilibrium to favor the rapidturnover form of the enzyme. Once the enzyme is “locked” into an activated conformation, the hysteresis is no longer observed, and the stop-flow results are in agreement with data obtained from initial velocity experiments. Activation appears to result in both increased rates of electron transfer into and out of the flavin.
Augmented Precision Square Roots and 2-D Norms, and Discussion on Correctly Rounding sqrt(x^2+y^2)

Define an "augmented precision" algorithm as an algorithm that returns, in precision-p floating-point arithmetic, its result as the unevaluated sum of two floating-point numbers, with a relative error of the order of 2^(-2p). Assuming an FMA instruction is available, we perform a tight error analysis of an augmented precision algorithm for the square root, and introduce two slightly different augmented precision algorithms for the 2D-norm sqrt(x^2+y^2). Then we give tight lower bounds on the minimum distance (in ulps) between sqrt(x^2+y^2) and a midpoint when sqrt(x^2+y^2) is not itself a midpoint. This allows us to determine cases when our algorithms make it possible to return correctly-rounded 2D-norms.
Clinical evaluation of ZTE attenuation correction for brain FDG-PET/MR imaging—comparison with atlas attenuation correction

1, Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland 2, Department of Radiology, Nippon Medical School, Tokyo, Japan 3, Institute of Pharmacology & Toxicology, University of Zurich, Zurich, Switzerland 4, PMOD Technologies Ltd., Zürich, Switzerland 5, Department of Neuroradiology, University Hospital Zurich, Zurich, Switzerland 6, Department of Medical Radiology, Division of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland 7, GE Healthcare Waukesha, WI, USA
Depth Data Analysis for Fall Detection

Falls are a leading cause for accidental deaths among persons aged 65 or older. Fall victims require immediate assistance in order to minimize morbidity and mortality rates. However, statistically every other fall victim is unable to get back up without help. The aim of fall detection is to ensure fast assistance by automatically informing caretakers in case of falls. Different methods have been proposed for this purpose, but there are still unresolved issues that limit the applicability of fall detection systems in practice. This thesis introduces a new fall detection system using current depth-sensing technology, with the aim of addressing these limitations. This sensor technology has several advantages over alternatives; it is unobtrusive, preserves the privacy of subjects, and works independently of lighting conditions, allowing for continuous monitoring. The generated data enables powerful scene analysis and, consequently, reliable fall detection. The applicability of this technology is verified by means of a quantitative analysis, in order to assess its limitations and data quality. In contrast to existing methods, the proposed system emphasizes person detection and tracking, which allows for robust temporal analysis and thus improves fall detection performance. To this end, this work also proposes a new motion detection algorithm optimized for depth data and fall detection. It is shown that this algorithm performs better than the state of the art on this type of data. Furthermore, this work contributes to the research field of tracking by introducing effective means for tracking multiple persons. The proposed system is easy to set up, plug and play, and can run on inexpensive low-end hardware, promoting broad acceptance. This work shows that reliable fall detection in depth data is possible, as verified on a comprehensive fall database. At the time of writing, the system is tested under practical conditions in four countries, with promising first results.
ACTIVATION OF NICOTINAMIDE ADENINE DINUCLEOTIDE PHOSPHATE (NADPH) OXIDASE (NOX) BY TYPE 1 RECEPTORS FOR ANGIOTENSIN II (ATII)

The multiple pathogenic effects of excessive ATII activity are mediated primarily by its interaction with the angiotensin Type 1 receptor (AT1R), a seven-pass receptor coupled to various heterotrimeric G proteins. Stimulation of this receptor, in turn, promotes activation of Nox complexes via several independent pathways that interact in a complementary fashion; the mechanisms involved are somewhat tissue specific, and vary depending on the isoforms of Nox expressed in a given tissue [1-3]. Key downstream effectors of this activation are protein kinase C (PKC) and Rac1, which promote the plasma membrane assemblage of Nox1and Nox2-dependent forms of Nox. In cells expressing Nox2, PKC phosphorylates p47phox, promoting its translocation to the plasma membrane, whereas Angiotensin antagonist drugs as “source antioxidants” – Downregulation of nicotinamide adenine dinucleotide phosphate oxidase activation mediates many of their protective benefi ts, independent of hypertension control
Learning Sound Representations Using Triplet-loss

Sound representations play an important role in audio information retrieval. Good representations effectively reduce the data size of audio recordings while keeping the acoustic and/or semantic characteristics. Recent studies have demonstrated that machine learning approaches which rely on large sound collections successfully learn such representations. However, these approaches come with a few drawbacks such as the complexity of building a taxonomy, the cost of sound annotation, and the difficulty to utilize multi-labelled annotation. In this work, we aim to address the issues by a similarity learning framework. We employ triplet loss in which our sound representations are optimized directly using a distance measure. This allows for more effective use of user-made and multilabelled sound descriptions from online sound collections and removes the need for a taxonomy. Furthermore, by adjusting the triplet loss with a natural language processing technique, we investigate the benefit of word representations of sound descriptions to make our sound representations semantically rich. Experimental results revealed that the sound representations we learned in similarity learning achieved the performance almost equal to the state-of-the-art model in a clustering task, even when we used a limited amount of training data. On the other hand, we also found a few drawbacks of our learning strategy such as the poor performance in a discriminative task. Our code for this project is available on GitHub: https://github.com/kokimame/ OpenFSE An additional tool to visualize audio representations with audio-playing feature is available on GitHub: https://github.com/kokimame/tensoba
A design and its practical application to the pseudo two-step successive-approximation register analog-to-digital converter with the active charge transfer technique

Abstract A pseudo two-step successive-approximation register (SAR) analog-to-digital converter (ADC) that uses an active charge transfer technique instead of directly amplifying the residue signal is proposed to achieve the advantages of lower capacitive loading and less design effort. The proof-of-concept prototype was designed using standard 0.18-μm 1P6M complementary metal-oxide-semiconductor technology to verify the functionality of the proposed technique. Due to the simple structure and low capacitive loading features, the proposed SAR ADC is easily combined with other circuitry to extend the practical applications, including battery capability measurement, replacement of the galvanometer for the Wheatstone bridge, and voltage measurement by combining the designed ADC with a field-programmable gate array device.
Orbital Semilattices

Orbital semilattices are introduced as bounded semilattices that are, in addition, equipped with an outer multiplication (a semigroup action) and diagonals (a concept borrowed from cylindric algebra), where each semilattice element has a certain domain. An example of an orbital semilattice is a table algebra, where the domain is the schema, the diagonals are diagonal relations, and outer multiplication encodes renaming, projection and column duplication. It is shown that each orbital semilattice can be represented by a subalgebra of such a table algebra.
JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text

In this paper we describe a system sub-mitted to the INLG 2022 Generation Challenge (GenChal) on Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text. We implement a Bi-LSTM-based neural network model to predict the Average rating score and Disagreement score of the synthetic Hinglish dataset. In our models, we used word embeddings for English and Hindi data, and one hot encodings for Hinglish data. We achieved a F1 score of 0.11, and mean squared error of 6.0 in the average rating score prediction task. In the task of Disagreement score prediction, we achieve a F1 score of 0.18, and mean squared error of 5.0.
Towards Differentiable Rendering for Sidescan Sonar Imagery

Recent advances in differentiable rendering, which allow calculating the gradients of 2D pixel values with respect to 3D object models, can be applied to estimation of the model parameters by gradient-based optimization with only 2D supervision. It is easy to incorporate deep neural networks into such an optimization pipeline, allowing the leveraging of deep learning techniques. This also largely reduces the requirement for collecting and annotating 3D data, which is very difficult for applications, for example when constructing geometry from 2D sensors. In this work, we propose a differentiable renderer for sidescan sonar imagery. We further demonstrate its ability to solve the inverse problem of directly reconstructing a 3D seafloor mesh from only 2D sidescan sonar data.
Automatic Prosody Annotation with Pre-Trained Text-Speech Model

Prosodic boundary plays an important role in text-to-speech synthesis (TTS) in terms of naturalness and readability. How-ever, the acquisition of prosodic boundary labels relies on manual annotation, which is costly and time-consuming. In this paper, we propose to automatically extract prosodic boundary labels from text-audio data via a neural text-speech model with pre-trained audio encoders. This model is pre-trained on text and speech data separately and jointly ﬁne-tuned on TTS data in a triplet format: { speech, text, prosody } . The experimental results on both automatic evaluation and human evaluation demonstrate that: 1) the proposed text-speech prosody annotation framework signiﬁcantly outperforms text-only baselines; 2) the quality of automatic prosodic boundary annotations is comparable to human annotations; 3) TTS systems trained with model-annotated boundaries are slightly better than systems that use manual ones. Code is released 1 .
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency

Visual domain adaptation (DA) seeks to transfer trained models to unseen, unlabeled domains across distribution shift, but approaches typically focus on adapting convolutional neural network architectures initialized with supervised ImageNet representations. In this work, we shift focus to adapting modern architectures for object recognition -- the increasingly popular Vision Transformer (ViT) -- and modern pretraining based on self-supervised learning (SSL). Inspired by the design of recent SSL approaches based on learning from partial image inputs generated via masking or cropping -- either by learning to predict the missing pixels, or learning representational invariances to such augmentations -- we propose PACMAC, a simple two-stage adaptation algorithm for self-supervised ViTs. PACMAC first performs in-domain SSL on pooled source and target data to learn task-discriminative features, and then probes the model's predictive consistency across a set of partial target inputs generated via a novel attention-conditioned masking strategy, to identify reliable candidates for self-training. Our simple approach leads to consistent performance gains over competing methods that use ViTs and self-supervised initializations on standard object recognition benchmarks. Code available at https://github.com/virajprabhu/PACMAC
Approximating optimization problems in graphs with locational uncertainty

Many combinatorial optimization problems can be formulated as the search for a subgraph that satisﬁes certain properties and minimizes the total weight. We assume here that the vertices correspond to points in a metric space and can take any position in given uncertainty sets. Then, the cost function to be minimized is the sum of the distances for the worst positions of the vertices in their uncertainty sets. We propose two types of polynomial-time approximation algorithms. The ﬁrst one relies on solving a deterministic counterpart of the problem where the uncertain distances are replaced with maximum pairwise distances. We study in details the resulting approximation ratio, which depends on the structure of the feasible subgraphs and whether the metric space is Ptolemaic or not. The second algorithm is a fully-polynomial time approximation scheme for the special case of s − t paths.
A super class-AB adaptive biasing amplifier in 65-nm CMOS technology

ABSTRACT This paper presents a class-AB amplifier with enhanced transconductance and slew rate using adaptive biasing technique and new drivers. The adaptive biasing configuration provides a tail current linked to the input signal in order to increase the slew rate during slewing phase. In addition, the new structure benefits from new drivers to enhance the transconductance which are supported by adaptive biasing configuration. These enhancement techniques lead to the input and output class-AB operations for fast settling applications. The proposed structure is simulated in 65-nm complementary metal-oxide semiconductortechnology which shows a 16.2-dB enhancement in the DC gain and a 72% reduction in the settling time compared to the conventional structure.
Rank the triplets: A ranking-based multiple instance learning framework for detecting HPV infection in head and neck cancers using routine H&E images

The aetiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens such as alcohol, tobacco and infection with human papillomavirus (HPV). As the HPV infection inﬂuences the prognosis, treatment and survival of patients with HNSCC, it is important to determine the HPV status of these tumours. In this paper, we propose a novel triplet-ranking loss function and a multiple instance learning pipeline for HPV status prediction. This achieves a new state-of-the-art performance in HPV detection using only the routine H&E stained WSIs on two HNSCC cohorts. Furthermore, a comprehensive tumour microenvironment proﬁling was performed, which characterised the unique patterns between HPV+/- HNSCC from genomic, immunology and cellular perspectives. Positive correlations of the proposed score with diﬀerent subtypes of T cells (e.g. T cells follicular helper, CD8+ T cells), and negative correlations with macrophages and connective cells (e.g. ﬁbroblast) were identiﬁed, which is in line with clinical ﬁndings. Unique gene expression proﬁles were also identiﬁed with respect to HPV infection status, and is in line with existing ﬁndings.
A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting

Flood disasters cause enormous social and economic losses. However, both traditional physical models and learning-based flood forecasting models require massive historical flood data to train the model parameters. When come to some new site that does not have sufficient historical data, the model performance will drop dramatically due to overfitting. This technical report presents a Flood Domain Adaptation Network (FloodDAN), a baseline of applying Unsupervised Domain Adaptation (UDA) to the flood forecasting problem. Specifically, training of FloodDAN includes two stages: in the first stage, we train a rainfall encoder and a prediction head to learn general transferable hydrological knowledge on large-scale source domain data; in the second stage, we transfer the knowledge in the pretrained encoder into the rainfall encoder of target domain through adversarial domain alignment. During inference, we utilize the target domain rainfall encoder trained in the second stage and the prediction head trained in the first stage to get flood forecasting predictions. Experimental results on Tunxi and Changhua flood dataset show that FloodDAN can perform flood forecasting effectively with zero target domain supervision. The performance of the FloodDAN is on par with supervised models that uses 450-500 hours of supervision.
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings

Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain. However, in a typical experiment, many factors corrupt neural recordings from individual electrodes, including electrical noise, movement artifacts, and faulty manufacturing. Currently, common practice is to discard these corrupted recordings, reducing already limited data that is difﬁcult to collect. To address this challenge, we propose Deep Neural Imputation (DNI) , a framework to recover missing values from electrodes by learning from data collected across spatial locations, days, and participants. We explore our framework with a linear nearest-neighbor approach and two deep generative autoencoders, demonstrating DNI’s ﬂexibility. One deep autoencoder models participants in-dividually, while the other extends this architecture to model many participants jointly. We evaluate our models across 12 human participants implanted with multielectrode intracranial electrocorticography arrays; participants had no explicit task and behaved naturally across hundreds of recording hours. We show that DNI recovers not only time series but also frequency content, and further establish DNI’s practical value by recovering signiﬁcant performance on a scientiﬁcally-relevant downstream neural decoding task.
A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling

The widespread adoption of EVs is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over time. For this reason, monitoring the battery state of charge and state of health during the EV lifetime is a very relevant problem. This work proposes the structure of a battery digital twin designed to reflect battery dynamics at the run time accurately. To ensure a high degree of correctness concerning non-linear phenomena, the digital twin relies on data-driven models trained on traces of battery evolution over time: a state of health model, repeatedly executed to estimate the degradation of maximum battery capacity, and a state of charge model, retrained periodically to reflect the impact of aging. The proposed digital twin structure will be exemplified on a public dataset to motivate its adoption and prove its effectiveness, with a high degree of accuracy and inference and retraining times compatible with onboard execution.