Writing thesis on game recommendations #datascience. Meet Zoi, our first (and last) own mobile game. We MU Grade Distribution Application Tuesday, October 05, Term Sep 25, · Bachelor Thesis thesis, Universitas Multimedia Nusantara. The energetics of multicomponent diffusion in molten CaO-Al 2 O 3-SiO2 (CAS) were examined experimentally at to °C and 0. C. org. The Asian Bureau of Finance and Economic Research is an institute founded by academics from Asia, North America, and Europe
Update your browser to use Google My Business - Google My Business Help
Toggle navigation. Search By Year All In this thesis, we consider a number of different landmarking and elementwise mapping problems and propose solutions that are thematically interconnected with each other. Phd thesis calin dan consider diverse problems ranging from landmarking to deep dictionary learning, pan-sharpening, compressive sensing magnetic resonance imaging and microgrid control, phd thesis calin dan, introducing novelties that go beyond the state of the art for the problems we discuss.
We start by introducing a manifold landmarking approach trainable via stochastic gradient descent that allows for the consideration of structural regularization terms in the objective. Inspired by these results, we consider an extension of this approach for general supervised and semi-supervised classification for structurally similar deep neural networks with self-modulating radial basis kernels.
Secondly, we consider convolutional networks that perform image-to-image mappings for phd thesis calin dan problems of pan-sharpening and compressive sensing magnetic resonance imaging, phd thesis calin dan. Using extensions of deep state of the art image-to-image mapping architectures specifically tailored for these problems, we show that they could be addressed naturally and effectively.
After this, we move on to describe a method for multilayer dictionary learning and feedforward sparse coding by formulating the dictionary learning problem using a general deep learning layer architecture inspired by analysis dictionary learning.
We find this method to be significantly faster to train than classical online dictionary learning approaches and capable of addressing supervised and semi-supervised classification problems more naturally.
Lastly, phd thesis calin dan, we look at the problem of per-user power supply delivery on a microgrid powered by solar energy. We approach the problem as one of demand-to-supply mapping, providing results for a policy network trained via regular propagation for worst-case control and classical deep reinforcement learning.
pdf ps. SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigation. This thesis focuses on visual semantic navigation, the task of producing actions for an active agent to navigate to a specified target object category in an unknown environment.
To complete this task, phd thesis calin dan, the algorithm should simultaneously locate and navigate to an instance of the category.
In comparison to the traditional point goal navigation, this task requires the agent to have a stronger contextual prior to indoor environments. Given a partial observation of the environment, SSCNav first infers a complete scene representation with semantic labels for the unobserved scene together with a confidence map associated with its own prediction.
Then, a policy network infers the action from the scene completion result and confidence map. The experiments demonstrate that the proposed scene completion module improves the efficiency of the downstream navigation policies.
Semantic Controllable Image Generation in Few-shot Settings. Generative Adversarial Networks GANs are able to generate high-quality images, but it remains difficult to explicitly specify the semantics of synthesized images. Interestingly, we find that a well-trained GAN encodes image semantics in its internal feature maps in a surprisingly simple way: a linear transformation of feature maps suffices to extract the generated image semantics. To phd thesis calin dan this simplicity, we conduct extensive experiments on various GANs and datasets; and thanks to this simplicity, we are able to learn a semantic segmentation model for a trained GAN from a small number e.
Last but not least, leveraging our findings, phd thesis calin dan, we propose two few-shot image editing approaches, namely Semantic-Conditional Sampling and Semantic Image Editing. Given an unsupervised GAN and as few as eight semantic annotations, the user is able to generate diverse images subject to a user-provided semantic layout, and control the synthesized image semantics.
This paper focuses on visual semantic navigation, the task of producing actions for an active agent to navigate to a specified target object category in an unknown environment. We introduce SSCNav, an algorithm that explicitly models scene priors using a confidence-aware semantic scene completion module to complete the scene and guide the agent's navigation planning.
Our experiments demonstrate that the proposed scene completion phd thesis calin dan improves the efficiency of the downstream navigation policies. Increased production efficiency combined with a slowdown in Moore's law and the end of Dennard scaling have made hardware accelerators increasingly important.
Accelerators have become available on many different systems from the cloud to embedded systems, phd thesis calin dan. This modern computing paradigm makes specialized hardware available at scale in a way it never has before, phd thesis calin dan.
While accelerators have shown great efficiency in terms of power consumption and performance, matching software functions with the best available hardware remains problematic without manual selection. Since there is some software representation of each accelerator's function, selection can be automated via code analysis.
Static similarity analysis has traditionally been based on solving satisfiable modulo theorems SMTbut continuous logic networks CLNs have provided a faster and more efficient alternative to traditional SMT-solving by replacing boolean functions with smooth estimations.
These smooth estimates create the opportunity to leverage gradient descent to learn the solution. We present AccFinder, the first CLN-based code similarity solution and evaluate its effectiveness on a realistically complex accelerator benchmark. SABER: Identifying SimilAr BEhavioR for Program Comprehension.
Modern software engineering practices rely on program comprehension as phd thesis calin dan most basic underlying component for improving developer productivity and software reliability.
Software developers are often tasked to work with unfamiliar code in order to remove security vulnerabilities, port and refactor legacy code, and enhance software with new features desired by users. Automatic identification of behavioral clones, phd thesis calin dan, or behaviorally-similar code, is one program comprehension technique that can provide developers with assistance.
The idea is to identify other code that "does the same phd thesis calin dan and that may be more intuitive; better documented; or familiar to the developer, to help them understand the code at hand. Unlike the detection of syntactic or structural code clones, behavioral clone detection requires executing workloads or test cases to find code that executes similarly on the same inputs.
However, a key problem in behavioral clone detection that has not received adequate attention is the "preponderance of the evidence" problem, which advocates for more convincing evidence from nontrivial test case executions to gain confidence in the behavioral similarities. In other words, similar outputs for some inputs matter more than for others. We present a novel system, SABER, to address the "preponderance of the evidence" problem, phd thesis calin dan, for which we adapt the legal metaphor of "more likely to be true than not true" burden of proof.
We develop a novel test case generation methodology with three primary dynamic analysis techniques for identifying important behavioral clones. Further, we investigate filtering and weighting schemes to guide developers toward the most convincing behavioral similarities germane to specific software engineering tasks, such as code review, debugging, phd thesis calin dan, and introducing new features.
Then the developers need to test whether their candidate patch indeed fixes the bug, without breaking other functionality, while racing to deploy before cyberattackers pounce on exposed user installations.
This can be challenging when the bug discovery was due to factors that arose, perhaps transiently, in a specific user environment. If recording execution traces when the bad behavior occurred, record-replay technology faithfully replays the execution, in the developer environment, as if the program were executing in that user environment under the same conditions as the bug manifested.
This includes intermediate program states dependent on system calls, memory layout, phd thesis calin dan, etc. as well as any externally-visible behavior. So the bug is reproduced, and many modern record-replay tools also integrate bug reproduction with interactive debuggers to help locate the root cause, but how do developers check whether their patch indeed eliminates the bug under those same conditions?
State-of-the-art record-replay does not support replaying candidate patches that modify the program in ways that diverge program state from the original phd thesis calin dan, but successful repairs necessarily diverge so the bug no longer manifests. This work builds on recordreplay, and binary rewriting, to automatically generate and run tests for candidate patches. Unlike conventional ad hoc testing, each test is reproducible and can be applied to as many prospective patches as needed until developers are satisfied.
The proposed approach also enables users to make new recordings of her own workloads with the original version of the program, and automatically generate and run the corresponding ad hoc tests on the patched version, to validate that the patch does not introduce new problems before adopting.
The FHW Project: High-Level Hardware Synthesis from Haskell Programs. The goal of the FHW project was to produce a compiler able to translate programs written in a functional language we chose Haskell into synthesizable RTL we chose SystemVerilog suitable for execution on an FPGA or ASIC that was highly parallel, phd thesis calin dan. We ultimately produced such a compiler, relying on the Glasgow Haskell Compiler GHC as a front-end and writing our own back-end that performed a series of lowering transformations to restructure such constructs as recursion, polymorphism, and frst-order functions, into a form suitable for hardware, then transform the now-restricted functional IR into a datafow representation that is then finally transformed into synthesizable SystemVerilog.
Many HLS systems produce efficient hardware designs for regular algorithms i. HLS tools typically provide imperative, phd thesis calin dan, side-effectful languages to the designer, which makes it difficult to correctly specify and optimize complex, memory-bound applications. In this dissertation, I present an alternative HLS methodology that leverages properties of functional languages to synthesize hardware for irregular algorithms.
The main contribution is an optimizing compiler that translates pure functional programs into modular, parallel dataflow networks in phd thesis calin dan. I give an overview of this compiler, explain how its source and target together enable parallelism in the face of irregularity, and present two specific optimizations that further exploit this parallelism.
Taken together, this dissertation verifies my thesis that pure functional programs exhibiting irregular memory access patterns can be compiled into specialized hardware and optimized for parallelism.
This work extends the scope of modern HLS toolchains, phd thesis calin dan. By relying on properties of pure functional languages, our compiler can synthesize hardware from programs containing constructs that commercial HLS tools prohibit, e. Hardware designers may thus use our compiler in conjunction with existing HLS systems to accelerate a wider class of algorithms than before. Extractive Text Summarization Methods Inspired By Reinforcement Learning for Better Generalization. This master thesis opens with a description of several text summarization methods based on machine learning approaches inspired by reinforcement learning.
While in many cases Maximum Likelihood Estimation MLE approaches work well for text summarization, they tend to suffer from poor generalization. We show that techniques which expose the model to more opportunities to learn from data tend to generalize better and generate summaries with less lead bias.
In our experiments we show that out of the box these new models do not perform significantly better than MLE when evaluated using Rouge, however do possess interesting properties which may be used to assemble more sophisticated and better performing summarization systems. The main theme of the thesis is getting machine learning models to generalize better using ideas from reinforcement learning.
We develop a new labeling scheme inspired by Reward Augmented Maximum Likelihood RAML methods developed originally for the machine translation task, and discuss how difficult it is to develop models which sample from their own distribution while estimating the gradient e. in Minimum Risk Training MRT and Reinforcement Learning Policy Gradient methods.
We show that RAML can be seen as a compromise between direct optimization of the model towards optimal expected reward using Monte Carlo methods which may fail to converge, and standard MLE methods which fail to explore the entire space of summaries, overfit during training by capturing prominent position features and thus perform poorly on unseen data.
To that end we describe and show results of domain transfer experiments, where we train the model on one dataset and evaluate on another, and position distribution experiments, in which we show how the distribution of positions of our models differ from the distribution in MLE. We also show that our models work better on documents which are less lead biased, while standard MLE models get significantly worse performance on those documents in particular.
Another topic covered in the thesis is Query Focused text summarization, where a search query is used to produce a summary with the query in mind. Phd thesis calin dan summary needs to be relevant to the query, rather than solely contain important information from the document. We use ii the recently published Squad dataset and adapt it for the Query Focused summarization task.
We also train deep learning Query Focused models for summarization and discuss problems associated with that approach. Finally we describe a method to reuse phd thesis calin dan already trained QA model for the Query Focused text summarization by introducing a reduction of the QA task into the Query Focused text summarization.
Email privacy is of crucial importance. Existing email encryption approaches are comprehensive but seldom used due to their complexity and inconvenience. We take a new approach to simplify email encryption and improve its usability by implementing receiver-controlled encryption: newly received messages are transparently downloaded and encrypted to a locally-generated key; the original message is then replaced. To avoid the problem of users having to move a single private key between devices, we implement per-device key pairs: only public keys need be synchronized to a single device.
Compromising an email account or email server only provides access to encrypted emails. Mail, phd thesis calin dan acceptable overhead, and that users consider it intuitive and easy to use. Analysis of the CLEAR Protocol per the National Academies' Framework. On the one hand, some people claim it can be accomplished safely; others dispute that.
In an attempt to make progress, a National Academies study committee propounded a framework to use when analyzing proposed solutions. We apply that framework to the CLEAR protocol and show the limitations of the design. Robot Learning in Simulation for Grasping and Manipulation. Teaching a robot to acquire complex motor skills in complicated environments is one of the most ambitious problems facing roboticists today.
Grasp planning is a subset of this problem which can be solved through complex geometric and physical analysis or computationally expensive data driven analysis.
How To Write A Dissertation Introduction Or Thesis Introduction Chapter: 7 Steps + Loads Of Examples
, time: 30:12About Poki - Let the world play
Steven M. Bellovin, Matt Blaze, Dan Boneh, Susan Landau, Ronald L. Rivest: The debate over "exceptional access"--the government’s ability to read encrypted data--has been going on for many years and shows no signs of resolution any time soon. On the one hand, some people claim it can be accomplished safely; others dispute that We would like to show you a description here but the site won’t allow blogger.com more Dan is a coach who really embodies what he is teaching on a regular basis. On today’s show, Dan talks about how his plyometric programs have changed over the years, where his plyometrics volume has shifted, volume in performing variations of various sport jumps, as well as in submaximal plyometrics, where big rocks like depth jumps fit in now
No comments:
Post a Comment