Miracle-dz/UniFAR-FTUs huggingface dataset:
Institutional Knowledge at Singapore Management University Dynamic inventory pooling policies to deliver differentiated service

Resource pooling strategies have been widely used in industry to match supply with demand. However, eﬀective implementation of these strategies can be challenging. Firms need to integrate the heterogeneous service level requirements of diﬀerent customers into the pooling model and allocate the resources (inventory or capacity) appropriately in the most eﬀective manner. The traditional analysis of inventory pooling, for instance, considers the performance metric in a centralized system and does not address the associated issue of inventory allocation. Using Blackwell’s Approachability Theorem, we derive a set of necessary and suﬃcient conditions to relate the ﬁll rate requirement of each customer to the resources needed in the system. This provides a new approach to study the value of resource pooling in a system with diﬀerentiated service requirements. Furthermore, we show that with “allocation ﬂexibility” the amount of safety stock needed in a system with independent and identically distributed demands does not grow with the number of customers but instead diminishes to zero and eventually becomes negative as the number of customers grows suﬃciently large. This surprising result holds for all demand distributions with bounded ﬁrst and second moments.
A Convolutional Neural Network Accelerator for Power-Efﬁcient Real-Time Vision Processing

—Deep Convolutional Neural Networks (CNN) constitute a promising framework for many applications. Such networks are often employed for vision processing algorithms, because CNNs offer better accuracy than traditional signal processing algorithms. However, it is challenging to apply high-accuracy deep CNNs for real-time vision processing, because they require high computational power and large data movement. Since general-purpose processors do not efﬁciently support CNNs, various hardware accelerators have been proposed. While it is required to support all the layers of the CNN for real-time vision processing, the large amount of weights (more than 100s of MB) limit the speedup of hardware acceleration, because the performance is largely bounded by memory access times. Recent CNN architectures, such as SqueezeNet and GoogLeNet, address this problem by employing narrow layers. However, their irregular architecture necessitates a re-design of hardware accelerators. In this paper, we propose a novel hardware accelerator for advanced CNNs aimed at realizing real-time vision processing with high accuracy.
Two Biological Applications of Optimal Control to Hybrid Differential Equations and Elliptic Partial Differential Equations

In this dissertation, we investigate optimal control of hybrid differential equations and elliptic partial differential equations with two biological applications. We prove the existence of an optimal control for which the objective functional is maximized. The goal is to characterize the optimal control in terms of the solution of the optimality system. The optimality system consists of the state equations coupled with the adjoint equations. To obtain the optimality system we differentiate the objective functional with respect to the control. This process is applied to studying two problems: one is a type of hybrid system involving ordinary differential equations and a discrete time feature. We apply our approach to a tick-transmitted disease model in which the tick dynamics changes seasonally while hosts have continuous dynamics. The goal is to maximize disease-free ticks and minimize infected ticks through an optimal control strategy of treatment with acaricide. The other is a semilinear elliptic partial differential equation model for fishery harvesting. We consider two objective functionals: maximizing the yield and minimizing the cost or variation in the fishing effort (control). Existence, necessary conditions and uniqueness for the optimal control for both problems are established. Numerical examples are given to illustrate the results.
An advanced approach for distortionless seamcarving in video analysis

Video synopsis is a technique that creates summary of the video or it can converts the abstraction of selected frames. This innovative approach permits the organizations to review long videos in minutes. It is more convenient to superimpose objects on to the static background and parallel displaying events to generate video synopsis. In this, paper a propelled noiseless video synopsis technique, which utilizes object-extracting method for vital objects. Along this technique spatial and temporal coherence cost is used to maintain time and position of the important objects. The proposed method will generate video spots and seam craving method to reduce the input (original) video. Finally, experimental results gives that our proposed method can produce a large reducing ratio, while preserving all the important objects of choice. Therefore, this noiseless approach can facilitate users to watch the surveillance video with greater accuracy.
COVID-19: Understanding the Impact of Societal Disruption on Student Learning and Academic Progress

Luke Duncan is a doctoral student in the Engineering and Science Education Department at Clemson University. His background is in mathematical sciences and mathematics education. Luke’s primary research interests include math anxiety and student success in higher education. He is currently involved in projects surrounding the topics of transfer student success, cognitive and symbol load, math anxiety, and qualitative research methods.
An approach to the e(cid:30)cient modelling of lean, premixed HCCI-operation

A model for the e(cid:30)cient simulation of lean, premixed self(cid:21)ignition under HCCI conditions is introduced. In the model the in(cid:21)cylinder gas is described by an ensemble of independent, homogeneous chemical reactors, which develop according to detailed chemistry. Essential information used in the model to describe the HCCI behaviour is the spatial (cid:29)uctuation of temperature in the cylinder gases. Laser(cid:21)Induced Fluorescence (LIF) of formaldehyde is used to approximately determine temperature (cid:29)uctuations in an optically accessible HCCI(cid:21)engine. These (cid:29)uctuations are used to initialize the reactor ensemble, which predicts the onset of ignition and the measured pressure trace of the same engine very well. Detailed simulations evidenced that the consideration of temperature (cid:29)uctuations with a reactor ensemble is su(cid:30)cient to predict pressure traces with good accuracy for HCCI with lean, homogeneous mixtures.
Synchrotron Radiation Computed Tomography (SRCT) of Ruled Laser Targets

High spatial resolution tomography benefits from a high brightness source (photons/(mr{sup 2} x source area)). A synchrotron radiation source provides extremely high continuous brightness with spectral characteristics suited to a wide variety of imaging needs. Therefore, during the initial testing of the new synchrotron radiation computed tomography (SRCT) system at Stanford Synchrotron Radiation Laboratory, it was suggested that we image a ruled target designed for NIF experiments. This is a detailed report of that imaging effort.
Sur la numération factorielle, application aux permutations

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Which misspecifications persist?

We use an evolutionary model to determine which misperceptions can persist. Every period, a new generation of agents use their subjective models and the data generated by the previous generation to update their beliefs, and models that induce better actions become more prevalent. An equilibrium can resist mutations that lead agents to use a model that better fits the equilibrium data but induce the mutated agents to take an action with lower payoffs. We characterize which steady states resist mutations to a nearby model, and which resist mutations that drop a qualitative restriction such as independence.
Metabolic, Anthropometric and Blood Pressure Effects of Adding Two Kiwifruit or Bottled Water into the Diets of People with Pre-Diabetes: A Randomised, Parallel Group, Intervention Study

People with pre-diabetes may be reluctant to add fruit to their diets due to concerns around sugars. Our objective was to measure outcomes associated with potential adverse effects of ingesting fruit sugars while assessing metabolic benefits from eating nutrient-rich kiwifruit. Thirty-four people with pre-diabetes were randomized to receive two kiwifruit or 250 mL bottled water per day for 12-weeks; two people withdrew. The primary outcomes were between-group differences for glycated haemoglobin (HbA1c) and plasma vitamin C concentration. Secondary outcomes included anthropometry, blood pressure, lipids, uric acid, glycaemic-, inflammatory- and oxidative-markers. Mean (95% confidence interval) vitamin C intake and plasma concentration increased by 170 (141, 200) mg/d (P<0.001) and 11 (5, 17) µM (P = 0.001), respectively, in the kiwifruit compared with the control group. There was no between-group difference for HbA1c or for anthropometric, blood pressure or metabolic markers except for Trolox equivalent antioxidant capacity (TEAC) assay, for which there was a mean decrease of 27 (3, 51) mM (P = 0.027) in the plasma of the kiwifruit compared with the control group. The inclusion of two kiwifruit per day into the diets of people with pre-diabetes raised vitamin C intake and status without causing metabolic disturbance due to the sugars contained in the fruit. The reason behind a reduction in TEAC in the kiwifruit group despite an increase in plasma vitamin C is unclear. For any beneficial effect on HbA1c to become evident, it may require a larger sample and a longer intervention.
Developing a Dataset of Overridden Information in Wikipedia

This paper proposes a new task of detecting information override. Since all information on the Web is not updated in a timely manner, the necessity is created for information that is overridden by another information source to be discarded. The task is formalized as a binary classification problem to determine whether a reference sentence has overridden a target sentence. In investigating this task, this paper describes a construction procedure for the dataset of overridden information by collecting sentence pairs from the difference between two versions of Wikipedia. Our developing dataset shows that the old version of Wikipedia contains much overridden information and that the detection of information override is necessary.
Visual Sudoku Solver

The goal of the thesis is to design and implement a visual Sudoku solver. The input of the solver is an image capturing assignment of the Sudoku puzzle. The solver converts the image into a symbolic representation and solves the puzzle. Image-to-symbol conversion and solving the puzzle will be done by a single neural network trained from examples in end-to-end fashion. The accuracy of the trained solver will be statistically evaluated and compared to existing solutions.
Magnetic and chemical interactions of the nearest neighbor transition metal impurity pairs in semiconductors: A density-functional study

Magnetic and chemical interactions of the nearest neighbor transition metal impurity pairs in semiconductors have been studied over the past few decades in order to understand, and ultimately control magnetic properties of transition metal doped semiconductors in the search for novel magnetic materials. For the mechanisms of these pairwise interactions, several pictures based on conventional phenomenological models have been presented. However, these pictures can only partially capture trends in these pairwise interactions and have not even given a congruous view of these pairwise interactions for the very same materials. This thesis tackles the issues of the mechanisms of these pairwise interactions based on density functional theory calculations. Density functional theory calculations are among the most widely used ab initio electronic structure approaches today, particularly in the condensed matter physics community. By using such calculations, one can obtain electronic structure as solution to the fundamental manyelectron-ion Hamiltonian instead of presupposing the preferred type of electronic interactions. Therefore, the calculations may allow us to identify the important factors for the mechanisms of these pairwise interactions that are omitted in the formulation of the conventional phenomenological models, and may unveil the true mechanisms underlying the observed different types of magnetic interactions. We first reveal that a peculiar ferromagnetic coupling of the Cr impurity pairs in AlN that cannot be interpreted by conventional magnetic models is mainly due to d-d interactions that are usually assumed to be negligible. Next, we reveal for what kind of materials d-d interactions become important in the description of pairwise magnetic and chemical interactions of the TM impurities in III-V semiconductors.
WoTHive: Enabling Syntactic and Semantic Discovery in the Web of Things

In the last decade the Internet of Things (IoT) has experienced a significant growth and its adoption has become ubiquitous in either business and private life. As a result, several initiatives have emerged for addressing specific challenges and provide a standard or a specification to address them; like CoRE, Web of Things (WoT), oneM2M, or OGC among others. One of these challenges revolves around the discovery procedures to find IoT devices within IoT infrastructures and whether the discovery performed is semantic or syntactic. This article focusses on the WoT initiative and reports the benefits that Semantic Web technologies bring to discovery in WoT. In particular, one of the implementations for the WoT discovery is presented, which is named WoTHive and provides syntactic and semantic discovery capabilities. WoTHive is the only candidate implementation that addresses at the same time the syntactic and semantic functionalities specified in the discovery described by WoT. Several experiments have been carried out to test WoTHive; these advocate that the implementation is technically sound for CRUD operations and that its semantic discovery outperforms the syntactic one implemented. Furthermore, an experiment has been carried out to compare whether syntactic discovery is faster than semantic discovery using the Link Smart implementation for syntactic discovery and WoTHive for semantic. TYPE OF PAPER AND
Learning Symbolic Failure Detection for Grasping and Mobile Manipulation Tasks

The ability to detect failure during task execution and to recover from failure is vital for autonomous robots performing tasks in previously unknown environments. In this paper, we present an approach for failure detection during the execution of grasping and mobile manipulation tasks by a humanoid robot. The approach combines multi-modal sensory information consisting of proprioceptive, force and visual information to learn task models from multiple successful task executions, in order to detect failures and to externalize them for humans in an interpretable way. To this end, we define symbolic action predicates based on multi-modal sensory information to allow high-level state estimation based on action-specific decision trees. To allow symbolic failure detection, we then learn task models that are represented as Markov chains. We evaluated the approach in several pick-and-place and mobile manipulation tasks performed by a humanoid robot in a decommissioning and a household scenario. The evaluation shows that the learned task models are capable of detecting failure with an F1-score of 93 %.
STRESS PHYSIOLOGY IN RESPONSE TO PROLONGED PERIODS IN NATURE

Research suggests urban settings may induce stress and attentional fatigue. Stress Recovery Theory and Attention Restoration Theory propose exposure to natural environments reduce stress and restore attention. Exploring this, we had 26 participants complete testing before, during, and after a 5-day nature trip. Participant heart rate variability (measured by Respiratory Sinus Arrhythmia) and self-reported perceived stress were used to determine if prolonged immersion in nature promotes stress recovery. Responses to a cognitive task were used to determine if prolonged immersion in nature promotes attention restoration. We hypothesized that the exposure to nature will increase activity of the parasympathetic nervous system, reflected by an increase in heart rate variability, which would indicate recovery of stress and greater control over the stress response during attention regulation. We also expected the participants would report less perceived stress and show improved performance on the cognitive task in the nature condition relative to the urban sessions. Results for self-reported perceived stress were consistent with our hypothesis, while results for heart rate variability and cognitive tasks were inconsistent with our predictions. Notably, we found a decrease in heart rate variability during the nature condition, contradicting our hypothesis. This study provides additional data on how immersion in nature affects humans on a physiological level, which could help us better understand how nature impacts the individual at a time of increased urbanization. what are most process of stress and the mechanisms which may measured. Implications of these findings may then be used for community well-being, such as natural design in urban environments, and management of natural spaces.
On-Demand or On-Premises: Online Mitigation of DDoS Attacks via Cloud-Edge Coordination

To mitigate Distributed Denial-of-Service (DDoS) attacks towards enterprise networks, we study the problem of scheduling DDoS traffic through on-premises scrubbing at the local edge and on-demand scrubbing in the remote clouds. We model this problem as a nonlinear mixed-integer program, which is characterized by the inputs of arbitrary dynamics and the trade-offs between staying at suboptimal scrubbing locations and using different best locations with switching overhead. We first design a prediction-oblivious online algorithm which consists of a carefully-designed fractional algorithm to pursue the long-term total cost minimization but avoid excessive switching overhead over time, and a randomized rounding algorithm to derive the flow-based, integral decisions. We next design a prediction-aware online algorithm which leverages the predicted inputs and can make even better scheduling decisions through invoking our prediction-oblivious online algorithm and improving its solutions via re-solving the original problem slice over each prediction window. We further rigorously prove the worst-case, constant competitive performance guarantees of our online algorithms. We finally conduct extensive evaluations and validate the superiority of our approach over multiple existing alternatives.
Estimation of the Mutual Pose of Camera Stereo Pair Subject to Parasitic Dynamics

Review existing solutions aplicable for extrinsic calibration of a stereo pair of fisheye cameras onboard of a flying unmanned aerial vehicle (UAV). These cameras are a component of a relative localization system UVDAR, which provides estimates of relative poses of marked nearby UAVs [3]. While the initial mutual pose of the cameras can be estimated, the precise mutual pose of these cameras changes over time.This is due to the combined effects of their far-apart layout, attachment materials, environmental heat and vibrations of the UAV, often even leading to persistant warpage of their connecting holder. This effect leads to degraded precision of the estimated relative positions of surrounding UAVs in cases when a given target is observed by both cameras if their mutual relative pose is assumed to be static. Based on the review, select an appropriate algorithm for the task and implement a program capable of dynamically obtaining the mutual pose of the camera pair in flight.This program has to be compatible with UAV system used by the MRS group and be able to operate during flight. Optionally, the system may be tested in real-world deployment of UAVs where the properties of a leader-follower flight can be compared between cases when the mutual camera pose is based on a static prior estimate and when it is estimated dynamically.
Measuring Website Quality using Service Quality and User Satisfaction Dimensions: A Conceptual Framework

: The study aimed to develop a conceptual framework for measuring the website quality of public sector organizations through users' feedback on service quality and satisfaction. The methodology involved a full review of previous studies on website quality service quality user satisfaction and website quality evaluation models. The review highlighted the need for further research to explore public organizations’ website quality from users' perspectives. A conceptual framework was proposed to explore website quality using sequel and user satisfaction models.
Deep Limits and a Cut-Off Phenomenon for Neural Networks

We consider dynamical and geometrical aspects of deep learning. For many standard choices of layer maps we display semi-invariant metrics which quantify diﬀerences between data or decision functions. This allows us, when considering random layer maps and using non-commutative ergodic theorems, to deduce that certain limits exist when letting the number of layers tend to inﬁnity. We also examine the random initialization of standard networks where we observe a surprising cut-oﬀ phenomenon in terms of the number of layers, the depth of the network. This could be a relevant parameter when choosing an appropriate number of layers for a given learning task, or for selecting a good initialization procedure. More generally, we hope that the notions and results in this paper can provide a framework, in particular a geometric one, for a part of the theoretical understanding of deep neural networks.
CoVault: A Secure Analytics Platform

—In a secure analytics platform , data sources consent to the exclusive use of their data for a pre-deﬁned set of analytics queries performed by a speciﬁc group of analysts, and for a limited period. If the platform is secure under a sufﬁciently strong threat model, it can provide the missing link to enabling powerful analytics of sensitive personal data, by alleviating data subjects’ concerns about leakage and misuse of data. For instance, many types of powerful analytics that beneﬁt public health, mobility, infrastructure, ﬁnance, or sustainable energy can be made differentially private, thus alleviating concerns about privacy . However, no platform currently exists that is sufﬁciently secure to alleviate concerns about data leakage and misuse ; as a result, many types of analytics that would be in the interest of data subjects and the public are not done. CoVault uses a new multi-party implementation of functional encryption (FE) for secure analytics, which relies on a unique combination of secret sharing, multi-party secure computation (MPC), and different trusted execution environments (TEEs). CoVault is secure under a very strong threat model that tolerates compromise and side-channel attacks on any one of a small set of parties and their TEEs. Despite the cost of MPC, we show that CoVault scales to very large data sizes using map-reduce based query parallelization. For example, we show that CoVault can perform queries relevant to epidemic analytics at scale.