Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure distributed backup: Erasure codes and anonymous message delivery

Secure Distributed Backup: Erasure Codes and Anonymous Message Delivery Christopher Val Studholme Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2007 We address the problem of backing up one’s important data files in an efficient, secure and robust manner by suggesting that copies of such files be sent to untrusted remote peers over the Internet. To provide incentive for such peers to store copies of your files, you would be required to devote some of your surplus disk space to storing copies of their files. In this manner, your data would be distributed widely about the planet and would thus be immune to loss due to all but the most global of disasters. We address a few of the major challenges that must be overcome to make such a network a reality. First, we note that since remote peers are inherently unreliable, your data files must have some form a redundancy introduced to ensure complete recovery when needed; however, since wholesale replication of the files is neither space nor bandwidth efficient, we suggest the use of an erasure correcting code. We evaluate the use of a few existing codes for this task (Chapters 2 and 3) and propose a new class of codes, called windowed erasure codes, which aim to minimize time complexity while maintaining the greatest probability of successful recovery (Chapter 4). To help ensure the security of one’s data and to avoid the problem of an adversary engaging in a selective denial of service attack against a single participant, we propose the use of an anonymous message delivery technique for distributing file data (Chapter 6). The protocol we propose aims to be efficient in its use of network bandwidth while remaining secure in the face of clients who choose to collude against the others. Also, as a
Non-Invasive Computed Tomographic Coronary Angiography for Patients with Suspected Coronary Artery Disease

Introduction: 64 Slice CT is useful non-invasive sceening tool in evaluation of patients suspected of CAD with excellent temporal and spatial resolution. Excellent image quality is obtained by lowering heart rate <60 bpm. Atherosclerotic plaques causing stenosis can be differentiated into calcified , non-calcified and mixed density types which allow further planning for management of CAD. Objective: Our study aimed at quantification of coronary calcium by 64 slice MDCT and its implication for determining risk of cardiovascular events, detection of CAD and significant coronary artery stenosis (>50% narrowing of lumen). Material & Methods: Our study included 60 patients suspected of CAD in pre-test clinical evaluation. All these patient were subjected to Calcium-scoring (by Agatston method,AJ-130, using Smart score 4.0 software ) and Coronary Angiography on 64 Slice MDCT Scanner using 3D Workstation with vessel analysis software (Cardiac IQ Express 4.0 , GE Healthcare ). Observations: CTCA detected significant CAD in about one-third of study population who were clinically suspected of having CAD on basis of symptoms and stress tests. No obstructive CAD was present in twothird of patients and CTCA prevented them to undergo cost-extensive Invasive Coronary Angiography (ICA). The atherosclerotic plaques causing stenosis could be differentiated into Calcified, non-calcified and mixed density , which helped in further planning for management of CAD. Conclusion: CTCA is better than calcium score in evaluation of CAD. Patient with zero calcium score can harbour significant CAD which is reliably detected on CTCA. Therefore, CTCA is an excellent non-invasive tool in work-up of patients of CAD.
Comprehensive management of epithelial ovarian cancer with peritoneal metastases

Ovarian cancer has as its predominant pattern of dissemination metastases to the peritoneal surfaces and disease spread within the abdomen and pelvis that most commonly causes the patients demise. To combat peritoneal metastases, cytoreductive surgery with peritoneal and visceral resections is combined with intraperitoneal and systemic chemotherapy. Chemotherapy given in the operating room after the complete visible removal of ovarian cancer is hyperthermic intraperitoneal chemotherapy. The results of the combined treatment are determined by the extent of prior surgery, the extent of disease as established by the peritoneal cancer index, and the quality of the cytoreduction as measured by the completeness of cytoreduction score. Recent clinical information on patients with recurrent ovarian cancer suggest a median overall survival of up to 60 mo. These data are greatly improved over the one year survival observed in the past.
Spectrum Handover Strategies for Cognitive Femtocell Networks

Cognitive femtocell is a promising technology for the next generation wireless networks to improve the efficiency of spectrum utilization, coverage, and to attain higher data rates for indoor communications. In this chapter, the new Cognitive Femtocell Switching Unit (CFSU) is proposed to support handover management for 10-20 cognitive femtocells as a local geographical cluster. Thus, CFSU acts as a service coordinator between femtocells and macrocell areas to improve spectrum utilization and coexistence. Then, the chapter presents solutions for spectrum handover to achieve guaranteed quality of radio service, spectrum utilization, and enable an excellent local handover management to reduce unnecessary handovers between femtocell base stations. The challenges and solutions that are presented in this chapter have the ability to maintain services by evaluating the requested quality of services.
Circumdederunt Me Canes Multi: Christ's Tormentors in Northern European Art of the Late Middle Ages and Early Renaissance

One of the best-known aspects of the piety of the late Middle Ages is the rise of devotion to the suffering Christ.1 The desire of pious men and women to approach the divine through intimate knowledge and empathic experience of Christ's humanity and Passion led to profound developments in religious literature and art: content and style underwent evolutionary changes mirroring the humanized and emotional religiosity of the period, and entirely new genres were created, for example, Passion Plays, and the visual representations known as Andachtsbilder, or devotional images. Interest in the specifics of Christ's life and sufferings produced analogous transformations in literature and art. In both we find a growth and popularization of narrative treatments of Christ's life and Passion, particularly those that supplement the meager accounts of the Gospels by introducing descriptive details and anecdotes not reported by the four Evangelists. Literary evidence for this development first appears in concerted form .
Dengue Human Challenge Model: Is There A Risk Caveat? Persistent Infection of Dengue Virus

Copyright: © 2015 Perng GC. This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. *Corresponding Author: Guey Chuen Perng, Department of Microbiology and Immunology, College of Medicine, National Cheng Kung University, Tainan, Taiwan, E-mail: gperng@mail.ncku. edu.tw
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26×? 629× while guaranteeing the same predictions, with 95% probability, as the full model.
Reinforcement learning architecture for automated quantum-adiabatic-algorithm design

Quantum algorithm design lies in the hallmark of applications of quantum computation and quantum simulation. Here we put forward a deep reinforcement learning (RL) architecture for automated algorithm design in the framework of quantum adiabatic algorithm, where the optimal Hamiltonian path to reach a quantum ground state that encodes a compution problem is obtained by RL techniques. We benchmark our approach in Grover search and 3-SAT problems, and find that the adiabatic algorithm obtained by our RL approach leads to significant improvement in the success probability and computing speedups for both moderate and large number of qubits compared to conventional algorithms. The RL-designed algorithm is found to be qualitatively distinct from the linear algorithm in the resultant distribution of success probability. Considering the established complexity-equivalence of circuit and adiabatic quantum algorithms, we expect the RL-designed adiabatic algorithm to inspire novel circuit algorithms as well. Our approach offers a recipe to design quantum algorithms for generic problems through a machinery RL process, which paves a novel way to automated quantum algorithm design using artificial intelligence, potentially applicable to different quantum simulation and computation platforms from trapped ions and optical lattices to superconducting-qubit devices.
A Framework for Automated Pop-song Melody Generation with Piano Accompaniment Arrangement

We contribute a pop-song automation framework for lead melody generation and accompaniment arrangement. The framework reflects the major procedures of human music composition, generating both lead melody and piano accompaniment by a unified strategy. Specifically, we take chord progression as an input and propose three models to generate a structured melody with piano accompaniment textures. First, the harmony alternation model transforms a raw input chord progression to an altered one to better fit the specified music style. Second, the melody generation model generates the lead melody and other voices (melody lines) of the accompaniment using seasonal ARMA (Autoregressive Moving Average) processes. Third, the melody integration model integrates melody lines (voices) together as the final piano accompaniment. We evaluate the proposed framework using subjective listening tests. Experimental results show that the generated melodies are rated significantly higher than the ones generated by bi-directional LSTM, and our accompaniment arrangement result is comparable with a state-of-the-art commercial software, Band in a Box.
Sparse Nonnegative CANDECOMP/PARAFAC Decomposition in Block Coordinate Descent Framework: A Comparison Study

Nonnegative CANDECOMP/PARAFAC (NCP) decomposition is an important tool to process nonnegative tensor. Sometimes, additional sparse regularization is needed to extract meaningful nonnegative and sparse components. Thus, an optimization method for NCP that can impose sparsity efficiently is required. In this paper, we construct NCP with sparse regularization (sparse NCP) by l1-norm. Several popular optimization methods in block coordinate descent framework are employed to solve the sparse NCP, all of which are deeply analyzed with mathematical solutions. We compare these methods by experiments on synthetic and real tensor data, both of which contain third-order and fourth-order cases. After comparison, the methods that have fast computation and high effectiveness to impose sparsity will be concluded. In addition, we proposed an accelerated method to compute the objective function and relative error of sparse NCP, which has significantly improved the computation of tensor decomposition especially for higher-order tensor.
Future semantic segmentation of time-lapsed videos with large temporal displacement

An important aspect of video understanding is the ability to predict the evolution of its content in the future. This paper presents a future frame semantic segmentation technique for predicting semantic masks of the current and future frames in a time-lapsed video. We specifically focus on time-lapsed videos with large temporal displacement to highlight the model's ability to capture large motions in time. We first introduce a unique semantic segmentation prediction dataset with over 120,000 time-lapsed sky-video frames and all corresponding semantic masks captured over a span of five years in North America region. The dataset has immense practical value for cloud cover analysis, which are treated as non-rigid objects of interest. %Here the model provides both semantic segmentation of cloud region and solar irradiance emitted from a region from the sky-videos. Next, our proposed recurrent network architecture departs from existing trend of using temporal convolutional networks (TCN) (or feed-forward networks), by explicitly learning an internal representations for the evolution of video content with time. Experimental evaluation shows an improvement of mean IoU over TCNs in the segmentation task by 10.8% for 10 mins (21% over 60 mins) ahead of time predictions. Further, our model simultaneously measures both the current and future solar irradiance from the same video frames with a normalized-MAE of 10.5% over two years. These results indicate that recurrent memory networks with attention mechanism are able to capture complex advective and diffused flow characteristic of dense fluids even with sparse temporal sampling and are more suitable for future frame prediction tasks for longer duration videos.
Identifying Computer-Translated Paragraphs using Coherence Features

We have developed a method for extracting the coherence features from a paragraph by matching similar words in its sentences. We conducted an experiment with a parallel German corpus containing 2000 human-created and 2000 machine-translated paragraphs. The result showed that our method achieved the best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is compared with previous methods on various computer-generated text including translation and paper generation (best accuracy = 67.9%, equal error rate = 32.0%). Experiments on Dutch, another rich resource language, and a low resource one (Japanese) attained similar performances. It demonstrated the efficiency of the coherence features at distinguishing computer-translated from human-created paragraphs on diverse languages.
Sequential algorithms and the computational content of classical proofs

We develop a correspondence between the theory of sequential algorithms and classical reasoning, via Kreisel's no-counterexample interpretation. Our framework views realizers of the no-counterexample interpretation as dynamic processes which interact with an oracle, and allows these processes to be modelled at any given level of abstraction. We discuss general constructions on algorithms which represent specific patterns which often appear in classical reasoning, and in particular, we develop a computational interpretation of the rule of dependent choice which is phrased purely on the level of algorithms, giving us a clearer insight into the computational meaning of proofs in classical analysis.
Enhanced Touchable Projector-depth System with Deep Hand Pose Estimation

Touchable projection with structured light range cameras is a prolific medium for large interaction surfaces, affording multiple simultaneous users and simple, cheap setup. However robust touch detection in such projector-depth systems is difficult to achieve due to measurement noise. We propose a novel combination of surface touch detection and a deep network for hand pose estimation, which aids in detecting both on- and above-surface hand gestures, disambiguating multiple touch fingers, as well as recovering fingertip positions in face of noisy input. We present the details of our GPU-accelerated system and an evaluation of its performance, as well as applications such as an enhanced virtual keyboard that utilizes the added features.
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize

Building open domain conversational systems that allow users to have engaging conversations on topics of their choice is a challenging task. Alexa Prize was launched in 2016 to tackle the problem of achieving natural, sustained, coherent and engaging open-domain dialogs. In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses. The 2018 competition also included the provision of a suite of tools and models to the competitors including the CoBot (conversational bot) toolkit, topic and dialog act detection models, conversation evaluators, and a sensitive content detection model so that the competing teams could focus on building knowledge-rich, coherent and engaging multi-turn dialog systems. This paper outlines the advances developed by the university teams as well as the Alexa Prize team to achieve the common goal of advancing the science of Conversational AI. We address several key open-ended problems such as conversational speech recognition, open domain natural language understanding, commonsense reasoning, statistical dialog management, and dialog evaluation. These collaborative efforts have driven improved experiences by Alexa users to an average rating of 3.61, the median duration of 2 mins 18 seconds, and average turns to 14.6, increases of 14%, 92%, 54% respectively since the launch of the 2018 competition. For conversational speech recognition, we have improved our relative Word Error Rate by 55% and our relative Entity Error Rate by 34% since the launch of the Alexa Prize. Socialbots improved in quality significantly more rapidly in 2018, in part due to the release of the CoBot toolkit.
TripleAgent: Monitoring, Perturbation and Failure-Obliviousness for Automated Resilience Improvement in Java Applications

In this paper, we present a novel resilience improvement system for Java applications. The unique feature of this system is to combine automated monitoring, automated perturbation injection, and automated resilience improvement. The latter is achieved thanks to the failure-oblivious computing, a concept introduced in 2004 by Rinard and colleagues. We design and implement the system as agents for the Java virtual machine. We evaluate the system on two real-world applications: a file transfer client and an email server. Our results show that it is possible to automatically improve the resilience of Java applications with respect to uncaught or mishandled exceptions.
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis

Aspect level sentiment classification is a fine-grained sentiment analysis task. To detect the sentiment towards a particular aspect in a sentence, previous studies have developed various attention-based methods for generating aspect-specific sentence representations. However, the attention may inherently introduce noise and downgrade the performance. In this paper, we propose constrained attention networks (CAN), a simple yet effective solution, to regularize the attention for multi-aspect sentiment analysis, which alleviates the drawback of the attention mechanism. Specifically, we introduce orthogonal regularization on multiple aspects and sparse regularization on each single aspect. Experimental results on two public datasets demonstrate the effectiveness of our approach. We further extend our approach to multi-task settings and outperform the state-of-the-art methods.
Approximate counting and NP search problems

We study a new class of NP search problems, those which can be proved total using standard combinatorial reasoning based on approximate counting. Our model for this kind of reasoning is the bounded arithmetic theory [Formula: see text] of [E. Jeřábek, Approximate counting by hashing in bounded arithmetic, J. Symb. Log. 74(3) (2009) 829–860]. In particular, the Ramsey and weak pigeonhole search problems lie in the new class. We give a purely computational characterization of this class and show that, relative to an oracle, it does not contain the problem CPLS, a strengthening of PLS. As CPLS is provably total in the theory [Formula: see text], this shows that [Formula: see text] does not prove every [Formula: see text] sentence which is provable in bounded arithmetic. This answers the question posed in [S. Buss, L. A. Kołodziejczyk and N. Thapen, Fragments of approximate counting, J. Symb. Log. 79(2) (2014) 496–525] and represents some progress in the program of separating the levels of the bounded arithmetic hierarchy by low-complexity sentences. Our main technical tool is an extension of the “fixing lemma” from [P. Pudlák and N. Thapen, Random resolution refutations, Comput. Complexity, 28(2) (2019) 185–239], a form of switching lemma, which we use to show that a random partial oracle from a certain distribution will, with high probability, determine an entire computation of a [Formula: see text] oracle machine. The introduction to the paper is intended to make the statements and context of the results accessible to someone unfamiliar with NP search problems or with bounded arithmetic.
Stanza: Distributed Deep Learning with Small Communication Footprint

The parameter server architecture is prevalently used for distributed deep learning. Each worker machine in a parameter server system trains the complete model, which leads to a hefty amount of network data transfer between workers and servers. We empirically observe that the data transfer has a non-negligible impact on training time. To tackle the problem, we design a new distributed training system called Stanza. Stanza exploits the fact that in many models such as convolution neural networks, most data exchange is attributed to the fully connected layers, while most computation is carried out in convolutional layers. Thus, we propose layer separation in distributed training: the majority of the nodes just train the convolutional layers, and the rest train the fully connected layers only. Gradients and parameters of the fully connected layers no longer need to be exchanged across the cluster, thereby substantially reducing the data transfer volume. We implement Stanza on PyTorch and evaluate its performance on Azure and EC2. Results show that Stanza accelerates training significantly over current parameter server systems: on EC2 instances with Tesla V100 GPU and 10Gb bandwidth for example, Stanza is 1.34x--13.9x faster for common deep learning models.