Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Planning for Symbiotic Autonomy in Domestic Robots

—Having robots interact with humans in domestic environments, while completing household tasks, requires a whole new degree of autonomy and reasoning. Typically, these kind of environments may push the robot’s capabilities (i.e. robot has to pick an object on an unreachable location). Likewise, some actions performed by the robot agent have a higher success rate due to its domain knowledge. Cooperation between humans and robots can overcome these issues, resulting in a higher amount of possible tasks for the robot. This is an example of Symbiotic Autonomy. In this scenario, at some point the robot has the option to do some action by himself or to ask for help in case it’s better for both agents - in the short or long-term. The proposed approach uses a planning framework based on probabilistic logic programming, the HYPE planner, which gathers at every step, observations from the environment, generating a grounded Markov Decision Process problem from the described domain and deciding the action he should take in order to maximize its performance on this environment. Furthermore, this architecture is benchmarked on a simulation environment from generated observations as well as in a house with a robot and real human agents.
How economical are Bounds on Inverted Index Summarization for Calculating Hadoop Channel?

We develop a novel technique for resizable Hadoop cluster’s lower bounds, the template matching rectangular array of inverted Index summarization expressions . Specifically, fix an arbitrary hybrid kernel function (cid:1) ∶ {0,1} (cid:8) → {0,1} and let (cid:10) (cid:11) be the rectangular array of inverted Index summarization expressions whose columns are each an application of (cid:1) to some subset of the variables (cid:12) (cid:13) ,(cid:12) (cid:14) ,…,(cid:12) (cid:16) (cid:17) . We prove that (cid:10) (cid:11) has bounded-capacity resizable Hadoop cluster’s complexity Ω ((cid:19)) , where (cid:19) is the approximate degree of (cid:1) . This finding remains valid in the MapReduce programming model, regardless of prior measurement. In particular, it gives a new and simple proof of lower bounds for robustness and other symmetric conjunctive predicates. We further characterize the discrepancy, approximate PageRank, and approximate trace distance norm of (cid:10) (cid:11) in terms of well-studied analytic properties of (cid:1) , broadly generalizing several findings on small-bias resizable Hadoop cluster and agnostic inference. The method of this paper has also enabled important progress in multi-cloud resizable Hadoop cluster’s complexity.
Investigating students' journey through a computer science program using exam data: three new approaches

A computing student will over the first three years of their studies complete approximately 20 exams and even more attempts due to failures and retakes. Details about all exam attempts are stored in a national database called Common Student System (Felles studentsystem, FS). Although access to FS, in general, is restricted, anonymized data about exam attempts can be provided for research and is a potential goldmine of data that, if used right might be a useful tool for educators and teachers. In this study, we explore this data in an attempt to conceptualize three new approaches to assessing student performance. Firstly, we relate students' final grade point average (GPA) to their performance in all courses in the first two years. Additionally, we propose a new indicator of student performance called "struggle factor," which is calculated using the number of exam attempts. Lastly, we investigate how students perform in different course subjects and types. Both the proposed use of FS data and the new approaches to performance indicators are relevant for educators wanting to understand the educational design of a study program and the students’ journey.
Information Aggregation in Networks

This thesis studies information aggregation in networks in which agents’ utilities depend on an unknown event. Agents initially receive a noisy signal about the event and take actions repeatedly while observing the actions of their neighbors in the network at each round. Such settings characterize many distributed systems such as sensor networks for intrusion detection and routing systems for internet trafﬁc. The thesis formally deﬁnes notions of convergence in action and in knowledge for agents in general networks, including rules of behavior for a general class. Our theoretical results show that (1) agents converge in action and in knowledge for a general class of decision making rules and for all network structures; (2) all networks converge to playing the same action regardless of the network structure; and (3) for particular network conﬁgurations, agents can converge to the correct action when using a well deﬁned class of myopic decision rules. These results are supported by a new empirical test-bed that allows researchers to simulate agent behavior over time for different types of networks and behavior rules. These results have implications for network designers in showing
Incorporating Attention in World Models for Improved Dynamics Modeling

Recurrent world models have recently been shown to perform well on reinforcement learning tasks, where spatio-temporal representations are learned in an unsupervised manner in simulated game environments. This work is an attempt to improve the world model by incorporating a content-based attention mechanism in the dynamics modeling module which is used to simulate a training environment. The proposed attention mechanism uses the dynamics from the recurrent neural network (RNN) with the encoded visual information to create a focused spatial representation of the input. Further, a gating network is used during training of the visual encoding module which helps in predicting descriptive factorized latent feature vectors, which tend to generate more realistic states when combined with attention in an unsupervised manner. The efﬁcacy of the approach is demonstrated through experiments in VizDoom game environment.
Spintronic Lateral Flow Biochip Platform (Extended Abstract)

In recent years, the application of lateral flow immunoassays has seen a huge growth on the diagnostic industry. It is considered to be a highly promising class of point-of-care (POC) diagnostic tests and represents a multibillion-dollar market on its own. This development was motivated by the combination of unique properties of lateral flow, which include its simplicity, relative accuracy, fast results, low price, and user friendliness. This technology has been used not only in the academic context but also to support a wide range of commercial applications including glucose tests, pregnancy tests, monitoring and testing of cardiac panels in emergency conditions, infectious disease screening (including Flu and HIV), cancer marker screening, and drugs abuse testing. The work presented in the thesis aims at exploring a new approach for combining lateral flow test with magnetoresistive sensors, with particular focus on: i) designing more compact test platforms that are easier to use and handle; and ii) to further push the detection limit of existing approaches by lowering the mechanical and thermal drift produced by the movement of the lateral-flow strip. In order to achieve the platform with the properties indicated above, it was necessary to combine several components in a single orchestrated architecture. To pave the way for the development of such a platform this work: i) presents a comprehensive study on spin valve sensors; ii) provides an experimental evaluation of the applicability of two magnetic nanoparticles to achieve magnetic labelling employing a permanent magnet for the magnetization method; iii) presents experimental characterization of several nitrocellulose membranes usually employed in lateral flow tests; and finally, iv) proposes an initial design of a physical platform that allows one to demonstrate a way to combine all required elements of the envisioned platform. Experimental measurement using a locking amplifier in combination with a prototype platform that was designed to serve as proof-of-concept for the applicability of the envisioned platform have shown that this approach is very promising, and that the developed platform was able to effectively detect magnetic nanoparticles that were inside a nitrocellulose membrane.
18F-FDG-PET in cancer (hyperPET): feasibility of a new imaging concept using a clinical PET/MRI scanner

In this paper we demonstrate, for the first time, the feasibility of a new imaging concept combined hyperpolarized 13C-pyruvate magnetic resonance spectroscopic imaging (MRSI) and 18F-FDG-PET imaging. This procedure was performed in a clinical PET/MRI scanner with a canine cancer patient. We have named this concept hyper PET. Intravenous injection of the hyperpolarized 13C-pyruvate results in an increase of 13C-lactate, 13C-alanine and 13CCO2 ( C-HCO3) resonance peaks relative to the tissue, disease and the metabolic state probed. Accordingly, with dynamic nuclear polarization (DNP) and use of 13C-pyruvate it is now possible to directly study the Warburg Effect through the rate of conversion of 13C-pyruvate to 13C-lactate. In this study, we combined it with 18F-FDG-PET that studies uptake of glucose in the cells. A canine cancer patient with a histology verified local recurrence of a liposarcoma on the right forepaw was imaged using a combined PET/MR clinical scanner. PET was performed as a single-bed, 10 min acquisition, 107 min post injection of 310 MBq 18F-FDG. 13C-chemical shift imaging (CSI) was performed just after FDG-PET and 30 s post injection of 23 mL hyperpolarized 13C-pyruvate. Peak heights of 13C-pyruvate and 13Clactate were quantified using a general linear model. Anatomic 1H-MRI included axial and coronal T1 vibe, coronal T2-tse and axial T1-tse with fat saturation following gadolinium injection. In the tumor we found clearly increased 13C-lactate production, which also corresponded to high 18F-FDG uptake on PET. This is in agreement with the fact that glycolysis and production of lactate are increased in tumor cells compared to normal cells. Yet, most interestingly, also in the muscle of the forepaw of the dog high 18F-FDG uptake was observed. This was due to activity in these muscles prior to anesthesia, which was not accompanied by a similarly high 13C-lactate production. Accordingly, this clearly demonstrates how the Warburg Effect directly can be demonstrated by hyperpolarized 13C-pyruvate MRSI. This was not possible with 18F-FDG-PET imaging due to inability to discriminate between causes of increased glucose uptake. We propose that this new concept of simultaneous hyperpolarized 13C-pyruvate MRSI and PET may be highly valuable for image-based non-invasive phenotyping of tumors. This methods may be useful for treatment planning and therapy monitoring.
An Enhanced Hybrid Routing and Clustering Technique for Wireless Sensor Network

Wireless Sensor Networks (WSN) have extensively deployed in a wide range of applications. However, WSN still faces several limitations in processing capabilities, memory, and power supply of sensor nodes. It is required to extend the lifetime of WSN. Mainly this is achieved by routing protocols choosing the best transmission path in-network with desired power conservation.This cause is developing a generic protocol framework for WSNa big challenge. This work proposed a new routing technique, described as Hybrid Routing-Clustering (HRC) model. This new approach takes advantage of clustering and routing procedures defined in K-Mean clustering and AODV routing, which constituted of three phases. This development aims to achieve enhanced power conservation rate in consequence network lifetime. An extensive evaluation methodology utilized to measure the performance of the proposed model in simulated scenarios.The results categorized in terms of the average amount of packet received and power conservation rate. The Hybrid Routing-Clustering (HRC) model was determined, showed enhanced results regarding both parameters. In the end, they are comparing these results with well-known routing and well-known clustering algorithms.
Genome Editing: Methods and Application in Plant Pathology

Genetic engineering can accelerate the advancement of improved crops and animals. Firstly genetically modified (GM) crops were popularized in 1996. From that point forward the cultivated area has expanded 100 overlays with 28 nations growing these crops. About 2000 examinations have been distributed assessing the wellbeing of GM crops; thus far International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 05 (2019) Journal homepage: http://www.ijcmas. com
Optimal Deterministic Coresets for Ridge Regression

We consider the ridge regression problem, for which we are given an n × d matrix A of examples and a corresponding n × d (cid:48) matrix B of labels, as well as a ridge parameter λ ≥ 0, and would like to output an X (cid:48) ∈ R d × d (cid:48) for which (cid:107)
Simulation and Visualization of Articulatory Trajectories Estimated from Speech Signals

This paper presents recent research onthe estimationofarticulatoryspeech parameters froman acoustic speech signal. The approach is based on a model of speech generation whose parameters are estimated to duplicate an arbitrary speech signal input. It is shown how perceptual articulatory codebooks and limited dynamic programming can improve the efﬁciency of the initial estimation. An extension of this voice mimic system to unvoiced sounds is also described and demonstrated by synthesizing short utterances of speech such as “she saw a ﬁre”. This research aims to advance fundamental understanding of human speech generation and coalesces the problem of speech synthesis, speech recognition, and low bit-rate speech coding into a compact parametric framework.
CVD graphene and graphene for sensing application

Owing to its novel electrical properties [1] and large surface to volume ratio, graphene is regarded as excellent candidate for ultrasensitive biological (chemical) detection and diagnosis devices. Before reaching such ambitious biological applications of graphene, its response to various electrolyte compositions, where the biomolecules site in, has to be clarified first of all. In fact, large variations in sensitivities have been reported for graphene-based ion sensors. For example, contradicted pH sensitivities ranging from a low value of 12mV/pH to a supra-Nernst value of 99mV/pH (Nernst value, 60mV/pH at room temperature), were reported in previous literature. The present work [1] serves to resolve this discrepancy. We start with either exfoliated graphene or chemical vapor deposition (CVD) graphene [2] grown on 25 um thick copper foils. The CVD graphene allows for the transfer of high-quality graphene with lateral scale of many centimeters on arbitrary substrates. Further characterizations indicate that we achieved predominant uniform, monolayer graphene with high mobility ~3000cm/Vs. As shown in Fig. 1, liquid-gated graphene field-effect transistors (GFETs) with reliable performance are developed. It is revealed that ideal defect-free graphene should be inert to electrolyte composition changes in solution, whereas a defective one responses to electrolyte composition. This suggests that graphene cannot sense the chemical potential of protons or ions (the proton or KCl concentration), but rather senses the electrostatic potential of the solution [3]. This finding sheds light on the large variety of pH or ion-induced gate shifts that have been published for GFETs in the recent literature. As a next step to target graphene-based (bio-) chemical sensing platform, non-covalent functionalization of graphene has to be introduced [4]. References [1] K. S. Novoselov, et al, “Field Effect in Atomically Thin Carbon Films”, Science, 306 (2004) 666. [2] X. S. Li, et al, “Large-Area Synthesis of High-Quality and Uniform Graphene Films on Copper Foils”, Science, 324 (2009) 1312. [3] W. Y. Fu, et al, “Graphene Transistors Are Insensitive to pH Changes in Solution”, Nano Lett. , 11 (2011) 3597. [4] W. Y. Fu, et al, “High Mobility Graphene Ion-Sensitive Field-Effect Transistors by Noncovalent Functionalization”, to be submitted.
by Abstract Interpretation of Functional Correctness Properties of Neural Networks

Contexte et atouts du poste Machine learning techniques, notably deep learning [3], are enjoying tremendous success. They are applied to an ever-growing set of applications (image classification, speech recognition, prediction, to name a few). They are now also being considered for embedded critical applications, including medical devices, autonomous driving, or avionics. Such applications require a high level of insurance that the system behaves reliably and as expected.
Multiple-beam X-ray interferometry for phase-contrast microtomography

The ®rst successful operation of an X-ray interferometer under conditions of non-planar three-beam diffraction for phase-contrast X-ray microtomography is reported. Intrinsic phase differences of the re ̄ections used cancel from the threedimensional phase image of the specimen. With simultaneous hkl and hk l re ̄ections of a synchrotron radiation beam in a side-by-side geometry, the size of the usable ®eld of view is doubled and the investigated specimen volume is increased by a factor of four. As an example, the reconstructed slice of a mouse kidney is shown in phase contrast at 71 keV. Optimized choices of three-beam re ̄ections and matching interferometer geometries useful for applications are presented.
Impact of Auxiliary Loss Functions on Dialogue Generation Using Mutual Information

Dialogue generation involves teaching a program to generate natural conversation. Assuming there are two participants, it requires developing a program that can converse with a human being or another program, and do so coherently and ﬂu-ently. This paper presents the development of a dialog generating program, popularly called a chatbot, that learns from a corpus of conversations, using a basic sequence to sequence (Seq2Seq) model with a variety of auxiliary loss functions. Auxiliary loss functions are similar to loss functions used during training, but are instead used during generation and do not have to be differentiable. The auxiliary loss functions developed for this chatbot are variants of mutual information between the utterances of one speaker and those of the other, because the objective is to couple these utterances tightly. We demonstrate that using different forms of mutual information leads to developments of chatbots of varying quality. The research shows that when these different chatbots chat with themselves, it is not a sufﬁcient replacement for a human.
MESO-γ SCALE VORTEX WITH HEAVY PRECIPITATION IN THE MEIYU FRONT DURING GAME/HUBEX 1998 IOP

During June and July 1998, GEWEX Asian Monsoon Experiment (GAME) / Huaihe River Basin Experiment (HUBEX) curried out in Anhui province, China. This experiment aimed to gain an understanding of the energy / water circulation and mesoscale precipitation process around the Meiyu front (Zhao and Takeda, 1998). Most of the precipitation during the Intensive Observation Period (IOP) of GAME/HUBEX was concentrated from June 29 to July 3, 1998 and consisted of tow event. The first event was associated with the generation of a meso-α scale low on the Meiyu front. and the second was associated with a lingering of the Meiyu front, which was aligned from west to east (Maesaka and Uyeda, 1999). In the second event (2 July 1998), we observed band shaped precipitation system which paralleled latitude and this system had a heavy precipitation in the south edge of the band. Furthermore, many meso-γ vortexes were detected by Doppler radar in the heavy precipitation region. This system brought about 100 mm rainfall was observed in the north part of Anfui province. In this paper, we focus on the second precipitation system with meso-γ vortex. So we analyze the structure of the precipitation system by using dual Doppler radar analysis and we compare the structure with water vapor budget analysis by upper air sounding data. To diag-
Grouping and Testing Methods with Clustering Algorithms

—There are many tools, and in some cases code, available to developers on Android platforms. However, ﬁnding the most desirable code and more speciﬁcally the most efﬁcient code can be difﬁcult. This paper presents the framework for a technique, EffMethod (Efﬁcient Method), by which a developer could quickly ﬁnd and incorporate existing efﬁcient code into their project. EffMethod utilizes a modiﬁed version of the k-means clustering algorithm to identify similar methods and outputs those methods for testing. After a battery of tests to determine efﬁciency, the developer is provided with a much smaller amount of methods to choose from.
Evaluation of Haemato-biochemical and Oxidative Stress Parameters in Dogs Affected with Gastroenteritis

Gastroenteritis which is a common illness affecting all species of animals is much more severe in monogastric animals especially in dogs. The patho-physiology of gastroenteritis is quite diverse depending upon the specific etiology. The causes of gastroenteritis may be dietary, viral, bacterial, rickettsial, parasitic and miscellaneous. Gastroenteritis is characterized by varied clinical signs such as diarrhoea, vomition with or without blood, inappetance, lethargy, fever, anaemia and dehydration (Banja et al., 2002). These days oxidative stress is getting a lot of attention for various research studies as it plays very crucial role in the pathogenesis of different diseases. The oxidative stress occurs due to imbalance of production of reactive oxygen/nitrogen species or neutralizing antioxidant enzymes. Excessive production of free radicals and their metabolites, called International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 2 (2020) Journal homepage: http://www.ijcmas. com
Mission Possible: Guaranteeing Reactive Missions for Complex Robots

If a human has a complex task in mind, how can he or she realize a controller for a given robot platform guaranteeing the success of the task? If the characteristics of the robot prevent it from achieving the task, can we inform the human where help is needed? Many approaches exist for synthesizing reactive controllers for high-level tasks (see, for example [8], [11]). Current techniques require certain assumptions of the system’s behavior with respect to the discretized environment. It is either assumed that the system can always make transitions to neighboring regions ([8]), or that the partitioning of the workspace can be adapted to the system, provided it falls within a certain class of systems ([11]). For this reason, automatic synthesis of correct-byconstruction controllers for specific robot platforms remains an open challenge. While multi-layered synthesis for online motion planning [2], [9] and automatically-constructed navigation functions [7] enable synthesis for certain classes of dynamics, these approaches cannot both provide apriori guarantees at the synthesis time and automate the controller synthesis step for a wide variety of nonlinear systems. To enable reactive synthesis for nonlinear systems, we propose a formal approach using the framework pictured in Fig. 1. The approach takes as input a discrete abstraction of a robot, a map of its workspace, and a formal mission specification, and returns (if possible) a high-level controller composed of a verified set of behaviors. As a result, the result of the proposed technique: 1) permits offline task-satisfaction guarantees with respect to the given robot dynamics; 2) provides feedback to the user defining what assumptions on the environment (if any) are required to ensure the task. We sketch our solution to these two items.