Ensemble Analysis of Adaptive Compressed Genome Sequencing Strategies

Acquiring genomes at single-cell resolution has many applications such as in the study of microbiota. However, deep sequencing and assembly of all of millions of cells in a sample is prohibitively costly. A property that can come to rescue is that deep sequencing of every cell should not be necessary to capture all distinct genomes, as the majority of cells are biological replicates. Biologically important samples are often sparse in that sense. In this paper, we propose an adaptive compressed method, also known as distilled sensing, to capture all distinct genomes in a sparse microbial community with reduced sequencing effort. As opposed to group testing in which the number of distinct events is often constant and sparsity is equivalent to rarity of an event, sparsity in our case means scarcity of distinct events in comparison to the data size. Previously, we introduced the problem and proposed a distilled sensing solution based on the breadth first search strategy. We simulated the whole process which constrained our ability to study the behavior of the algorithm for the entire ensemble due to its computational intensity. In this paper, we modify our previous breadth first search strategy and introduce the depth first search strategy. Instead of simulating the entire process, which is intractable for a large number of experiments, we provide a dynamic programming algorithm to analyze the behavior of the method for the entire ensemble. The ensemble analysis algorithm recursively calculates the probability of capturing every distinct genome and also the expected total sequenced nucleotides for a given population profile. Our results suggest that the expected total sequenced nucleotides grows proportional to $\log$ of the number of cells and proportional linearly with the number of distinct genomes

A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion

Speech Emotion Recognition (SER) is a challenging task. In this paper, we introduce a modality conversion concept aimed at enhancing emotion recognition performance on the MELD dataset. We assess our approach through two experiments: first, a method named Modality-Conversion that employs automatic speech recognition (ASR) systems, followed by a text classifier; second, we assume perfect ASR output and investigate the impact of modality conversion on SER, this method is called Modality-Conversion++. Our findings indicate that the first method yields substantial results, while the second method outperforms state-of-the-art (SOTA) speech-based approaches in terms of SER weighted-F1 (WF1) score on the MELD dataset. This research highlights the potential of modality conversion for tasks that can be conducted in alternative modalities

Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems

In this paper, we introduce a novel Artificial Intelligence (AI) system inspired by the philosophical and psychoanalytical concept of imagination as a ``Re-construction of Experiences". Our AI system is equipped with an imagination-inspired module that bridges the gap between textual inputs and other modalities, enriching the derived information based on previously learned experiences. A unique feature of our system is its ability to formulate independent perceptions of inputs. This leads to unique interpretations of a concept that may differ from human interpretations but are equally valid, a phenomenon we term as ``Interpretable Misunderstanding". We employ large-scale models, specifically a Multimodal Large Language Model (MLLM), enabling our proposed system to extract meaningful information across modalities while primarily remaining unimodal. We evaluated our system against other large language models across multiple tasks, including emotion recognition and question-answering, using a zero-shot methodology to ensure an unbiased scenario that may happen by fine-tuning. Significantly, our system outperformed the best Large Language Models (LLM) on the MELD, IEMOCAP, and CoQA datasets, achieving Weighted F1 (WF1) scores of 46.74%, 25.23%, and Overall F1 (OF1) score of 17%, respectively, compared to 22.89%, 12.28%, and 7% from the well-performing LLM. The goal is to go beyond the statistical view of language processing and tie it to human concepts such as philosophy and psychoanalysis. This work represents a significant advancement in the development of imagination-inspired AI systems, opening new possibilities for AI to generate deep and interpretable information across modalities, thereby enhancing human-AI interaction.Comment: 18 pages

Compensation of Nonlinear Optical Fiber Impairments Using Coding and Electronic Equalizer

Ultra-high capacity fiber optic systems with data rates exceeding 100 Giga bits per second per fiber are currently being deployed with higher capacity systems in development. The requirement of a minimum energy per bit for reliable communication means that the power launched into a single fiber is now at a level where significant nonlinearites exist. Nonlinearities can also be produced for lower power intensity-modulated systems because of the square-law nature of sensors.In order to maximize the information capacity, the combined channel that includes a combination of nonlinear impairments along with additional linear impairments must be mitigated. This mitigation can be achieved by a combination of modulation coding at the transmitter and equalization at the receiver. The development of these techniques for nonlinear channels is significantly more complex than the corresponding techniques for linear channels because of the nature of the nonlinearity and the extremely high data rate. This rate limits the complexity of the equalization algorithm.This thesis presents modulation coding and equalization techniques for several nonlinear fiber optic channels. We consider two classes of nonlinearity. The first arises from the combination of linear dispersion in an optical fiber and square-law sensing. The second arises from nonlinear propagation characteristics caused by a power-dependent index of refraction change called a Kerr nonlinearity.A variety of nonlinear channel models can be constructed from these two fundamental forms of nonlinearity along with linear impairments. The dominant linear impairment is dispersion. One form occurs when the propagation characteristics for each mode depend on the frequency. A second form of dispersion arises because different polarization modes can have different propagation characteristics.The research premise of this thesis is that a combination of modulation coding, sequence estimation (both single-user and multi-user) and nonlinear equalization based on heuristic algorithms can produce significant performance improvement relative to published techniques. We present several abstracted scenarios reflecting practical systems where a combination of these techniques is effective. We also describe situations where they are ineffective. These results lay the foundation for further work using these techniques to optimize specific nonlinear channels

Compensation of Nonlinear Optical Fiber Impairments Using Coding and Electronic Equalizer

Ultra-high capacity fiber optic systems with data rates exceeding 100 Giga bits per second per fiber are currently being deployed with higher capacity systems in development. The requirement of a minimum energy per bit for reliable communication means that the power launched into a single fiber is now at a level where significant nonlinearites exist. Nonlinearities can also be produced for lower power intensity-modulated systems because of the square-law nature of sensors.In order to maximize the information capacity, the combined channel that includes a combination of nonlinear impairments along with additional linear impairments must be mitigated. This mitigation can be achieved by a combination of modulation coding at the transmitter and equalization at the receiver. The development of these techniques for nonlinear channels is significantly more complex than the corresponding techniques for linear channels because of the nature of the nonlinearity and the extremely high data rate. This rate limits the complexity of the equalization algorithm.This thesis presents modulation coding and equalization techniques for several nonlinear fiber optic channels. We consider two classes of nonlinearity. The first arises from the combination of linear dispersion in an optical fiber and square-law sensing. The second arises from nonlinear propagation characteristics caused by a power-dependent index of refraction change called a Kerr nonlinearity.A variety of nonlinear channel models can be constructed from these two fundamental forms of nonlinearity along with linear impairments. The dominant linear impairment is dispersion. One form occurs when the propagation characteristics for each mode depend on the frequency. A second form of dispersion arises because different polarization modes can have different propagation characteristics.The research premise of this thesis is that a combination of modulation coding, sequence estimation (both single-user and multi-user) and nonlinear equalization based on heuristic algorithms can produce significant performance improvement relative to published techniques. We present several abstracted scenarios reflecting practical systems where a combination of these techniques is effective. We also describe situations where they are ineffective. These results lay the foundation for further work using these techniques to optimize specific nonlinear channels