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Despite the advancement in sequencing technologies, around 98% of the genome is usually disregarded due to the lack of interpretation methods. Here, I compare different sequence-based deep-learning approaches for predicting the functionality of the non-coding genome. Using the largest non-coding variant database, I tested the change in prediction as pathogenic vs. benign variants were introduced. Then, I benchmarked their performance on different genomic regions and phenotypes and built a logistic regression model for cell- and phenotype-specific track selection. The models outperformed state-of-the-art evolutionary- and variantbased methods. Finally, I compared different target-gene annotation databases using ontology-based Resnik’s semantic similarity. I combined the previous steps in a variant-to-phenotype or phenotype-to-variant workflow and applied it to rare variants.

Video applications have become more and more common in the past few decades, in the meantime, optimizing video coding has received more attention. Existing video codecs usually focus on the encoder itself, and try to do everything possible to compress video with spatial (intraframe) compression and temporal (interframe) compression with the premise of reasonable distortion rate and video performance. In this work, we proposed a practical approach to improve video coding efficiency at a lower bitrate, which is to combine traditional Video Codec with interpolation neural network. A new concept called ``virtual frames'' was proposed and applied to the video coding process. We use raw frames as Ground Truth and virtual frames to train the interpolation neural network GDCN (Generalized Deformable Convolution Network), then encode the video synthesized with virtual frames via traditional AV1 video codec. With the pre-trained network, we could simply reconstruct the frames. This method can significantly improve the video compression effect compared with traditional video codec technology. / Thesis / Master of Applied Science (MASc)

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Following recent theoretical works on successive Wyner-Ziv coding, we propose a practical layered Wyner-Ziv video coder using the DCT, nested scalar quantiza- tion, and irregular LDPC code based Slepian-Wolf coding (or lossless source coding with side information at the decoder). Our main novelty is to use the base layer of a standard scalable video coder (e.g., MPEG-4/H.26L FGS or H.263+) as the decoder side information and perform layered Wyner-Ziv coding for quality enhance- ment. Similar to FGS coding, there is no performance di®erence between layered and monolithic Wyner-Ziv coding when the enhancement bitstream is generated in our proposed coder. Using an H.26L coded version as the base layer, experiments indicate that Wyner-Ziv coding gives slightly worse performance than FGS coding when the channel (for both the base and enhancement layers) is noiseless. However, when the channel is noisy, extensive simulations of video transmission over wireless networks conforming to the CDMA2000 1X standard show that H.26L base layer coding plus Wyner-Ziv enhancement layer coding are more robust against channel errors than H.26L FGS coding. These results demonstrate that layered Wyner-Ziv video coding is a promising new technique for video streaming over wireless networks. For scalable video transmission over the Internet and 3G wireless networks, we propose a system for receiver-driven layered multicast based on layered Wyner-Ziv video coding and digital fountain coding. Digital fountain codes are near-capacity erasure codes that are ideally suited for multicast applications because of their rate- less property. By combining an error-resilient Wyner-Ziv video coder and rateless fountain codes, our system allows reliable multicast of high-quality video to an arbi- trary number of heterogeneous receivers without the requirement of feedback chan- nels. Extending this work on separate source-channel coding, we consider distributed joint source-channel coding by using a single channel code for both video compression (via Slepian-Wolf coding) and packet loss protection. We choose Raptor codes - the best approximation to a digital fountain - and address in detail both encoder and de- coder designs. Simulation results show that, compared to one separate design using Slepian-Wolf compression plus erasure protection and another based on FGS coding plus erasure protection, the proposed joint design provides better video quality at the same number of transmitted packets.

Multiple Description Coding (MDC) was fi rst formulated by A. Gersho and H. Witsenhausen as a way to improve the robustness of telephony links to outages. Lots of studies have been done in this area up to now. Another application of MDC is the transmission of an image in diff erent descriptions. If because of the link outage during transmission, any one of the descriptions fails, the image could still be reconstructed with some quality at the decoder side. In video coding, inter prediction is a way to reduce temporal redundancy. From an information theoretical point of view, one can model inter prediction with Causal Video Coding (CVC). If because of link outage, we lose any I-frame, how can we reconstruct the corresponding P- or B-frames at the decoder? In this thesis, we are interested in answering this question and we call this scenario as causal video coding with possible loss of the fi rst encoded frame and we denote it by CVC-PL as PL stands for possible loss. In this thesis for the fi rst time, CVC-PL is investigated. Although, due to lack of time, we mostly study two-frame CVC-PL, we extend the problem to M-frame CVC-PL as well. To provide more insight into two-frame CVC-PL, we derive an outer-bound to the achievable rate-distortion sets to show that CVC-PL is a subset of the region combining CVC and peer-to-peer coding. In addition, we propose and prove a new achievable region to highlight the fact that two-frame CVC-PL could be viewed as MDC followed by CVC. Afterwards, we present the main theorem of this thesis, which is the minimum total rate of CVC-PL with two jointly Gaussian distributed sources, i.e. X1 and X2 with normalized correlation coeffi cient r, for di fferent distortion pro files (D1,D2,D3). Defi ning Dr = r^2(D1 -1) + 1, we show that for small D3, i.e. D3 < Dr +D2 -1, CVC-PL could be treated as CVC with two jointly Gaussian distributed sources; for large D3, i.e. D3 > DrD2/(Dr+D2-DrD2), CVC-PL could be treated as two parallel peer-to-peer networks with distortion constraints D1 and D2; and for the other cases of D3, the minimum total rate is 0.5 log (1+ ??)(D3+??)/ (Dr+?? )(D2+?? ) + 0.5 log Dr/(D1D3) where ??=D3-DrD2+r[(1-D1)(1-D2)(D3-Dr)(D3-D2)]^0.5/[Dr+D2-(D3+1) ] We also determine the optimal coding scheme which achieves the minimum total rate. We conclude the thesis by comparing the scenario of CVC-PL with two frames with a coding scheme, in which both of the sources are available at the encoders, i.e. distributed source coding versus centralized source coding. We show that for small D2 or large D3, the distributed source coding can perform as good as the centralized source coding. Finally, we talk about future work and extend and formulate the problem for M sources.

Source coding and channel coding are two important problems in communications. Although side information exists in everyday scenario, the e&#64256;ect of side information is not taken into account in the conventional setups. In this thesis, we focus on the practical designs of two interesting coding problems with side information: Wyner-Ziv coding (source coding with side information at the decoder) and Gel??fand-Pinsker coding (channel coding with side information at the encoder). For WZC, we split the design problem into the two cases when the distortion of the reconstructed source is zero and when it is not. We review that the &#64257;rst case, which is commonly called Slepian-Wolf coding (SWC), can be implemented using conventional channel coding. Then, we detail the SWC design using the low-density parity-check (LDPC) code. To facilitate SWC design, we justify a necessary requirement that the SWC performance should be independent of the input source. We show that a su&#64259;cient condition of this requirement is that the hypothetical channel between the source and the side information satis&#64257;es a symmetry condition dubbed dual symmetry. Furthermore, under that dual symmetry condition, SWC design problem can be simply treated as LDPC coding design over the hypothetical channel. When the distortion of the reconstructed source is non-zero, we propose a practical WZC paradigm called Slepian-Wolf coded quantization (SWCQ) by combining SWC and nested lattice quantization. We point out an interesting analogy between SWCQ and entropy coded quantization in classic source coding. Furthermore, a practical scheme of SWCQ using 1-D nested lattice quantization and LDPC is implemented. For GPC, since the actual design procedure relies on the more precise setting of the problem, we choose to investigate the design of GPC as the form of a digital watermarking problem as digital watermarking is the precise dual of WZC. We then introduce an enhanced version of the well-known spread spectrum watermarking technique. Two applications related to digital watermarking are presented.

In previous work (Olshausen & Field 1996), an algorithm was described for learning linear sparse codes which, when trained on natural images, produces a set of basis functions that are spatially localized, oriented, and bandpass (i.e., wavelet-like). This note shows how the algorithm may be interpreted within a maximum-likelihood framework. Several useful insights emerge from this connection: it makes explicit the relation to statistical independence (i.e., factorial coding), it shows a formal relationship to the algorithm of Bell and Sejnowski (1995), and it suggests how to adapt parameters that were previously fixed.

Since its introduction in the year 2000 by Ahlswede et al., the network coding paradigm has revolutionized the way we understand information flows in networks. Traditionally, information transmitted in a communication network was treated as a commodity in a transportation network, much like cars on highways or fluids in pipes. This approach, however, fails to capture the very nature of information, which in contrast to material goods, can be coded and decoded. The network coding techniques take full advantage of the inherent properties of information, and allow the nodes in a network, not only to store and forward, but also to "mix", i.e., encode, their received data. This approach was shown to result in a substantial throughput gain over the traditional routing and tree packing techniques. In this dissertation, we study applications of network coding for guarantying reliable and secure information transmission in networks with compromised edges. First, we investigate the construction of robust network codes for achieving network resilience against link failures. We focus on the practical important case of unicast networks with non-uniform edge capacities where a single link can fail at a time. We demonstrate that these networks exhibit unique structural properties when they are minimal, i.e., when they do not contain redundant edges. Based on this structure, we prove that robust linear network codes exist for these networks over GF(2), and devise an efficient algorithm to construct them. Second, we consider the problem of securing a multicast network against an eavesdropper that can intercept the packets on a limited number of network links. We recast this problem as a network generalization of the classical wiretap channel of Type II introduced by Ozarow and Wyner in 1984. In particular, we demonstrate that perfect secrecy can be achieved by using the Ozarow-Wyner scheme of coset coding at the source, on top of the implemented network code. Consequently, we transparently recover important results available in the literature on secure network coding. We also derive new bounds on the required secure code alphabet size and an algorithm for code construction. In the last part of this dissertation, we study the connection between index coding, network coding, and matroid linear representation. We devise a reduction from the index coding problem to the network coding problem, implying that in the linear case these two problems are equivalent. We also present a second reduction from the matroid linear representability problem to index coding, and therefore, to network coding. The latter reduction establishes a strong connection between matroid theory and network coding theory. These two reductions are then used to construct special instances of the index coding problem where vector linear codes outperform scalar linear ones, and where non-linear encoding is needed to achieve the optimal number of transmission. Thereby, we provide a counterexample to a related conjecture in the literature and demonstrate the benefits of vector linear codes.