Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamical probabilistic graphical models applied to physiological condition monitoring

Intensive Care Units (ICUs) host patients in critical condition who are being monitored by sensors which measure their vital signs. These vital signs carry information about a patient’s physiology and can have a very rich structure at fine resolution levels. The task of analysing these biosignals for the purposes of monitoring a patient’s physiology is referred to as physiological condition monitoring. Physiological condition monitoring of patients in ICUs is of critical importance as their health is subject to a number of events of interest. For the purposes of this thesis, the overall task of physiological condition monitoring is decomposed into the sub-tasks of modelling a patient’s physiology a) under the effect of physiological or artifactual events and b) under the effect of drug administration. The first sub-task is concerned with modelling artifact (such as the taking of blood samples, suction events etc.), and physiological episodes (such as bradycardia), while the second sub-task is focussed on modelling the effect of drug administration on a patient’s physiology. The first contribution of this thesis is the formulation, development and validation of the Discriminative Switching Linear Dynamical System (DSLDS) for the first sub-task. The DSLDS is a discriminative model which identifies the state-of-health of a patient given their observed vital signs using a discriminative probabilistic classifier, and then infers their underlying physiological values conditioned on this status. It is demonstrated on two real-world datasets that the DSLDS is able to outperform an alternative, generative approach in most cases of interest, and that an α-mixture of the two models achieves higher performance than either of the two models separately. The second contribution of this thesis is the formulation, development and validation of the Input-Output Non-Linear Dynamical System (IO-NLDS) for the second sub-task. The IO-NLDS is a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients. More specifically, in this thesis the focus is on modelling the effect of the widely used anaesthetic drug Propofol on a patient’s monitored depth of anaesthesia and haemodynamics. A comparison of the IO-NLDS with a model derived from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature on a real-world dataset shows that significant improvements in predictive performance can be provided without requiring the incorporation of expert physiological knowledge.
An Integrated DC–DC Converter With Segmented Frequency Modulation and Multiphase Co-Work Control for Fast Transient Recovery

This paper presents a fully integrated voltage-controlled oscillator (VCO)-based switched-capacitor (SC) 15-phase dc–dc converter in 65-nm CMOS. We propose two transient-enhancement techniques: segmented frequency modulation (SFM) and multiphase co-work (MCW) control to reduce the latency of the VCO-based control loop and shorten the SC dc–dc converter’s transient response time. The SFM can improve the heavy-to-light load transient by dynamically increasing the charge pump discharge current by nine times, while the MCW can enhance the light-to-heavy load recovery by synchronously combining three interleaved flying capacitors together during the transient state. We designed the 15-phase interleaved converter to support an output voltage of 1 V from a 2.4-V input supply, delivering up to 138 mA of load current, which takes only 25/29 ns for output voltage recovering to the steady state from heavy-to-light/light-to-heavy load transients, respectively. It obtains a peak efficiency of 82.8% and maintains the efficiency above 80% from 31 mA to the maximum load current. The SC dc–dc converter chip occupies 0.61 mm2, and its output power density is 240 mW/mm2.
STRNN: End-to-end deep learning framework for video partial copy detection

The task of partial copy detection in videos aims at determine if one or more segments of the query video are already present in the data-set, while giving the information of similar portion time period. At present, most effective algorithms of partial copy detection in videos are designed as three steps: feature extraction, feature matching and time alignment. The separation of feature matching and time alignment module ignores the spatio-temporal information of partial copy to some extent. Therefore, satisfactory performance is not obtained. In order to reduce this loss, this article does not decompose it into two separate tasks, but using a single convolution neural network to solve these two aspects. First, we sample video frames and extract CNN features, calculate the spatio-temporal relationship matrix of the source video and the query video, and then graphically map the matrix and train the convolution neural network based on the object detection task of the RefineDet model. Finally, in the query phase, the time period of the partial copy is deduced based on the detection result. In this paper, we evaluate the performance of the algorithm on the real complex video copy detection data-set VCDB which is significantly improved compared with the state-of-the-art partial copy detection framework.
Generative Model Driven Representation Learning in a Hybrid Framework for Environmental Audio Scene and Sound Event Recognition

The analysis of sound information is helpful for audio surveillance, multimedia information retrieval, audio tagging, and forensic applications. Environmental audio scene recognition (EASR) and sound event recognition (SER) for audio surveillance are challenging tasks due to the presence of multiple sound sources, background noises, and the existence of overlapping or polyphonic contexts. We focus on learning robust and compact representations for environmental audio scenes and sound events using mel-frequency cepstral coefficients as basic features, which have proved to be effective in speech and audio-related tasks. In this paper, we propose a common hybrid model-based framework that learns representations with the help of generative models. We explore instance-specific adapted Gaussian mixture models for environmental audio scenes and instance-specific hidden Markov models for sound events to compute a robust, compact, and discriminatory representations. A discriminative model based classifier is then used to recognize these representations as environmental audio scenes and sound events. The performance of the proposed approaches is evaluated using the DCASE2013 scene dataset and TUT-DCASE2016 scene dataset for EASR task. Environmental Sound Classification (ESC-10) and UrbanSound8K datasets are used for SER task. The recognition accuracy of the proposed framework is significantly better than many of the state-of-the-art approaches proposed in the recent literature. The discriminative nature of the model-driven representations leads to improved efficiency for EASR and SER task. The proposed approaches are more suitable for tasks with less training data.
Approaches for incremental learning and image generation

This dissertation explores two related topics in the context of deep learning: incremental learning and image generation. Incremental learning studies training of models with the objective function evolving over time, eg, addition of new categories to a classification task. Image generation seeks to learn a distribution of natural images for generating new images resembling original ones.Incremental learning is a challenging problem due to the phenomenon called catastrophic forgetting: any significant change to the objective during training causes a severe degradation of previously learned knowledge. We present a learning framework to introduce new classes to an object detection network. It is based on the idea of knowledge distillation to counteract catastrophic forgetting effects: fixed copy of the network evaluates old samples and its output is reused in an auxiliary loss to stabilize learning of new classes. Our framework mines these samples of old classes on the fly from incoming images, in contrast to other solutions that keep a subset of samples in memory.On the second topic of image generation, we build on the Generative Adversarial Network (GAN) model. Recently, GANs significantly improved the quality of generated images. However, they suffer from poor coverage of the dataset: while individual samples have great quality, some modes of the original distribution may not be captured. In addition, existing GAN evaluation methods are focused on image quality, and thus do not evaluate how well the dataset is covered, in contrast to the likelihood measure commonly used for generative models. We present two approaches to address these problems.The first method evaluates class-conditional GANs using two complementary measures based on image classification - GAN-train and GAN-test, which approximate recall (diversity) and precision (quality of the image) of GANs respectively. We evaluate several recent GAN approaches based on these two measures, and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures.Inspired by our study of GAN models, we present a method to explicitly enforce dataset coverage during the GAN training phase. We develop a generative model that combines GAN image quality with VAE architecture in the feature space engendered by a flow-based model Real-NVP. This allows us to evaluate a valid likelihood and simultaneously relax the independence assumption in RGB space which is common for VAEs. We achieve Inception score and FID competitive with state-of-the-art GANs, while maintaining good likelihood for this class of models.
Leveraging the Saudi Linked Open Government Data: A Framework and Potential Benefits

Open data initiatives are a crucial aspect of effective e-governance strategy. They embody aspirations towards sociopolitical values of transparency, trust, confidence, and accountability, pertaining to the relationship between a government and its citizens. The importance of such initiatives is especially important for an emerging economy such as Saudi Arabia which is undergoing rapid social changes directed by a contemporary national vision. The effectiveness of open data initiatives depends strongly on (a) the quality of the data available, (b) the soundness of the methodologies and suitability of platforms used to prepare and present the data, and (c) the ability of the data to facilitate the kinds of insights and social-action that are sought from that data to ensure successful e-governance. This paper investigates the feasibility of current Saudi government open data initiatives in this regard. It assesses existing approaches to improve the effectiveness of open government data through transforming it into linked-open data (using the Resource Description Framework [RDF]) by connecting disparate sources of structured data therein. It proposes to improve existing approaches by suggesting a framework for automating the linking sub-process of existing approaches and organizing the data to be queried through SPARQL. Moreover, it evaluates the potential benefit of this proposal by discussing the kinds of policy insights this could generate which would be difficult without it.
Novel Technological Solutions for Assessment, Treatment, and Assistance in Mild Cognitive Impairment

Alzheimer's disease, and dementia, represent a common cause of disability and one of the most relevant challenges in the health world. In addition, these conditions do not have, at moment, a pharmacological treatment that can stop the pathological progress. Mild cognitive impairment (MCI), defined as the borderline between normal aging and early dementia, represents a meaningful field of study because, in the transition to dementia, clinicians have defined a useful therapeutic window. Additionally, due to the lack of effective pharmacological interventions, recent years have seen an increase in research into new technological solutions to assess, stimulate, and assist patients afflicted with Alzheimer's disease. This review aims to outline the use of information and communication technologies in the field studying MCI. Particularly, the goal is to depict the framework and describe the most worthwhile research efforts, in order to display the current technologies available, describe the research objectives, and delineate prospective future researches. Regarding data sources, the research was conducted within three databases, PubMed Central, Web of Science, and Scopus, between January 2009 and December 2017. A total of 646 articles were found in the initial search. Accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers to use for the study. Finally, 56 papers were fully evaluated and included in this review. Three major clinical application areas have been portrayed, namely “Cognitive Assessment,” “Treatment,” and “Assistance. ” These have been combined with three main technological solutions, specifically “Sensors,” “Personal Devices,” and “Robots. ” Furthermore, the study of the publications time series illustrates a steadily increasing trend, characterized by the enrollment of small groups of subjects, and particularly oriented to the subjects assistance using robots companion. In conclusion, despite the new technological solutions for people with MCI have received much interest, particularly regarding robots for assistance, nowadays it still owns vast room for improvement.
Global Illumination Shadow Layers

Computer graphics artists often resort to compositing to rework light effects in a synthetic image without requiring a new render. Shadows are primary subjects of artistic manipulation as they carry important stylistic information while our perception is tolerant with their editing. In this paper we formalize the notion of global shadow, generalizing direct shadow found in previous work to a global illumination context. We define an object's shadow layer as the difference between two altered renders of the scene. A shadow layer contains the radiance lost on the camera film because of a given object. We translate this definition in the theoretical framework of Monte‐Carlo integration, obtaining a concise expression of the shadow layer. Building on it, we propose a path tracing algorithm that renders both the original image and any number of shadow layers in a single pass: the user may choose to separate shadows on a per‐object and per‐light basis, enabling intuitive and decoupled edits.
An analysis of the characteristics of fishers and their behaviour strategies following area closures

Closed areas are often used as either temporary or permanent measures to reduce fishing pressure on stocks. A major concern, however, is what happens to the effort that was previously employed in these areas. When modelling the potential impacts of the closed areas, it is necessary to model changes in fisher behaviour. However, most models have relied on assumptions based on previous behaviour that assume fishers will follow similar patterns. In 2001, an area of the North Sea was closed to fishing that had previously been heavily exploited. While most fishers reallocated effort to previously exploited grounds, a number reallocated their effort to grounds that had not been exploited in recent years, or had only been very lightly exploited. In this paper, the relationship between the characteristics of both the outre-istic and orthodox group of fishers and their behaviour are examined using a logit modelling approach in order to better understand fisher behaviour.
EVALUATING MANAGEMENT SYSTEMS AND THE INTANGIBLE PERFORMANCE OF ORGANIZATION INFLUENCED BY THE ENTERPRISE RESOURCE PLANNING

This study investigates the effects of enterprise resource planning systems adoption on Intangible performance of a firm. Specifically, the role of informal management control systems is investigated as mechanisms which mediate the effect of enterprise resource planning systems adoption on intangible performance of a firm.The empirical analyses of this study are based on survey data drawn from 4 companies that were collected by questionnaire. This study test our structural equation model using the multivariate statistical method Partial Least Squares (PLS) . The findings of this study demonstrate that informal types of management control systems do not mediate the positive effect between enterprise resource planning systems adoption and Intangible performance. These results are very important because the evidence on the joint roles of enterprise resource planning systems and management control system on improving the firm performance is very limited in prior literature. The results of this study show that the use of enterprise systems results in improved firm performance in the long run, and that more formal than informal types of management controls help firms achieve future performance goals.
The Arbitrarily Varying Broadcast Channel With Causal Side Information at the Encoder

In this paper, we study the arbitrarily varying broadcast channel (AVBC) when the state information is available at the transmitter in a causal manner. We establish the inner and outer bounds on both the random code capacity region and the deterministic code capacity region with degraded message sets. The capacity region is then determined for a class of channels satisfying a condition on the mutual information between the strategy variables and the channel outputs. As an example, we consider the arbitrarily varying binary symmetric broadcast channel. We show the cases where the condition holds and, hence, the capacity region is determined and other cases where there is a gap between the bounds. This gap shows that the minimax theorem does not hold for rate regions.
Effect of herbicides and hand weeding on weed control in guava cv. L-49

Guava (Psidium guajava L.), the apple of tropics, belongs to the family Myrtaceae is one of the most popular fruit in India for its nutritive value, high yield, hardy in nature, good processing quality and wide adaptability all over the tropics and subtropics. Although guava grows well in all states of India, Uttar Pradesh is the largest in area and production and Allahabad produces the best quality guava in the country as well as in the world. Weeds in fruit orchard are considered as the unwanted crops which compete with the associated crop for nutrient, light, moisture, space and adversely affected the growth, yield and quality of many plants as well as fruit crops. The use of herbicides in guava orchard to control weeds is a new practice. The conventional method of controlling weeds by manually is becoming more and more expensive due to steep rise in labour wages. The herbicidal method of weed control, which is considered less expensive, is gaining importance. Nishimoto and Yee (1980) at Hawaii and Martinez and Pareira (1984) at Brazil, obtained good control of weeds in guava orchard by application of herbicides. However, there is very little information available regarding the use of herbicides and their effect on the yield and fruit quality of guava in India. Weeds compete with guava at all the stages of development for soil moisture, soil nutrient and light besides harbouring insects, pests and diseases. As guava is considered to be one of the exquisite, nutritionally valuable and remunerative crops and weed is a serious menace to this crop therefore, it is imperative to develop effective control measures of weeds in guava for normal growth and quality fruits production. An attempt therefore, has been undertaken to assess the influence of herbicidal and manual weed management on growth, yield and fruit quality of guava cv. L-49, grown under new alluvial zone of West Bengal. The experiment was carried out at the Horticulture Research Station, Mondouri, Bidhan Chandra Krishi Viswavidyalaya, Nadia, West Bengal, during the year 2007. The soil of the experimental orchard is sandy loam in nature with soil pH 6.5 6.9. Soil depth is sufficient, moderately fertile and having good drainage system. The experime ntal site was first plough thoroughly followed by proper leveling. Separate blocks were prepared for each treatment. The block size was made at 12m in east west and 6m in north south, thus each block
Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation

In this paper, a hybrid deep neural network scheduler (HDNNS) is proposed to solve job-shop scheduling problems (JSSPs). In order to mine the state information of schedule processing, a job-shop scheduling problem is divided into several classification-based subproblems. And a deep learning framework is used for solving these subproblems. HDNNS applies the convolution two-dimensional transformation method (CTDT) to transform irregular scheduling information into regular features so that the convolution operation of deep learning can be introduced into dealing with JSSP. The simulation experiments designed for testing HDNNS are in the context of JSSPs with different scales of machines and jobs as well as different time distributions for processing procedures. The results show that the MAKESPAN index of HDNNS is 9% better than that of HNN and the index is also 4% better than that of ANN in ZLP dataset. With the same neural network structure, the training time of the HDNNS method is obviously shorter than that of the DEEPRM method. In addition, the scheduler has an excellent generalization performance, which can address large-scale scheduling problems with only small-scale training data.
Empirical Characterization of the Indoor Radio Channel for Array Antenna Systems in the 3 to 4 GHz Frequency Band

Concerning the design and planning of new radio interfaces for the fifth-generation (5G) systems, this paper presents a useful contribution to the characterization of the wideband indoor radio channel in the 3–4-GHz frequency band. A measurement campaign has been carried out in two different indoor scenarios to analyze some of the most important wideband parameters of the propagation channel, including a thorough analysis of its behavior to meet the new radio technology challenges. The channel measurement setup consists of a virtual vertical uniform array at the receiver side of the link that remains at a fixed position, whereas the transmitter side, which is equipped with a single antenna, is placed at different positions in the environment under analysis. The measurement setup emulates the up-link of a multi-user multiple-input multiple-output (MIMO) system and allows obtaining the broadband parameters of the multiple channels that are established between the transmitter and each one of the antennas of the receiver array. The results and conclusions about the path loss, temporal dispersion, and coherence bandwidth are included, along with an analysis of the spatial correlation between wideband channels when one of the antennas is an array.