When Machine Learning Meets Information Theory: Some Practical Applications to Data Storage

Machine learning and information theory are closely inter-related areas. In this dissertation, we explore topics in their intersection with some practical applications to data storage. Firstly, we explore how machine learning techniques can be used to improve data reliability in non-volatile memories (NVMs). NVMs, such as flash memories, store large volumes of data. However, as devices scale down towards small feature sizes, they suffer from various kinds of noise and disturbances, thus significantly reducing their reliability. This dissertation explores machine learning techniques to design decoders that make use of natural redundancy (NR) in data for error correction. By NR, we mean redundancy inherent in data, which is not added artificially for error correction. This work studies two different schemes for NR-based error-correcting decoders. In the first scheme, the NR-based decoding algorithm is aware of the data representation scheme (e.g., compression, mapping of symbols to bits, meta-data, etc.), and uses that information for error correction. In the second scenario, the NR-decoder is oblivious of the representation scheme and uses deep neural networks (DNNs) to recognize the file type as well as perform soft decoding on it based on NR. In both cases, these NR-based decoders can be combined with traditional error correction codes (ECCs) to substantially improve their performance. Secondly, we use concepts from ECCs for designing robust DNNs in hardware. Non-volatile memory devices like memristors and phase-change memories are used to store the weights of hardware implemented DNNs. Errors and faults in these devices (e.g., random noise, stuck-at faults, cell-level drifting etc.) might degrade the performance of such DNNs in hardware. We use concepts from analog error-correcting codes to protect the weights of noisy neural networks and to design robust neural networks in hardware. To summarize, this dissertation explores two important directions in the intersection of information theory and machine learning. We explore how machine learning techniques can be useful in improving the performance of ECCs. Conversely, we show how information-theoretic concepts can be used to design robust neural networks in hardware

Manipulating Memory Associations Changes Decision-making Preferences in a Preconditioning Task

Memories of past experiences can guide our decisions. Thus, if memories are undermined or distorted, decision making should be affected. Nevertheless, little empirical research has been done to examine the role of memory in reinforcement decision-making . We hypothesized that if memories guide choices in a conditioning decision-making task, then manipulating these memories would result in a change of decision preferences to gain reward. We manipulated participants’ memories by providing false feedback that their memory associations were wrong before they made decisions that could lead them to win money . Participants’ memory ratings decreased significantly after receiving false feedback. More importantly, we found that false feedback led participants’ decision bias to disappear after their memory associations were undermined . Our results suggest that reinforcement decision-making can be altered by fasle feedback on memories . The results are discussed using memory mechanisms such as spreading activation theories

The effect of eye movements on traumatic memories and the susceptibility to misinformation : a partial replication : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Psychology at Massey University, Manawatū, New Zealand

The issue of whether certain techniques used in psychotherapy might increase false memories is a major source of contention between cognitive and practising psychologists. Recently, a study by Houben, Otgaar, Merckelbach, and Roelofs (2018) found that bilateral eye movements used in Eye Movement Desensitisation and Reprocessing (EMDR) therapy increase susceptibility to misleading information. EMDR is a popular treatment for posttraumatic stress disorder and is primarily thought to reduce the vividness and emotional intensity of traumatic memories. Individuals who undergo EMDR therapy may be more susceptible to misinformation that is inadvertently introduced by the therapist due to reductions in memory vividness. Despite strong theoretical links between eye movements and false memories, few studies have investigated this effect. The current study addressed this issue by attempting to replicate the study by Houben et al. (2018). This study also investigated the working memory account underlying EMDR by comparing eye movements to an alternative dual-task. An initial pilot study comprising a reaction time task established that attentional breathing taxed working memory most comparably to bilateral eye movements. The main study predicted that eye movements would increase susceptibility to misinformation and that eye movements and attentional breathing would lead to comparable reductions in memory vividness and emotionality. 94 students (Mage= 25.74, SDage= 9.68) were recruited to participate in the study at Massey University, Manawatū, New Zealand. Participants viewed a five-minute video depicting a serious car accident. Afterwards, they were randomly assigned to perform either eye movements, attentional breathing, or a control task while simultaneously recalling the car accident. Participants rated the vividness and emotionality of their memory before and after performing the tasks. All participants then received misinformation about the video before completing a recognition test. Results indicated that the misinformation effect was not replicated, with no effect of eye movements on susceptibility to false memories. Findings also suggested that eye movements and attentional breathing were ineffective in reducing the vividness and emotional intensity of the trauma memory. The present study raises questions about the validity of materials and procedures used to instil the misinformation effect. Limitations of the study and key areas for improvement are considered for further investigation

Imagery Rescripting : The Impact of Conceptual and Perceptual Changes on Aversive Autobiographical Memories

BACKGROUND: Imagery rescripting (ImRs) is a process by which aversive autobiographical memories are rendered less unpleasant or emotional. ImRs is thought only to be effective if a change in the meaning-relevant (semantic) content of the mental image is produced, according to a cognitive hypothesis of ImRs. We propose an additional hypothesis: that ImRs can also be effective by the manipulation of perceptual features of the memory, without explicitly targeting meaning-relevant content. METHODS: In two experiments using a within-subjects design (both N = 48, community samples), both Conceptual-ImRs-focusing on changing meaning-relevant content-and Perceptual-ImRs-focusing on changing perceptual features-were compared to Recall-only of aversive autobiographical image-based memories. An active control condition, Recall + Attentional Breathing (Recall+AB) was added in the first experiment. In the second experiment, a Positive-ImRs condition was added-changing the aversive image into a positive image that was unrelated to the aversive autobiographical memory. Effects on the aversive memory's unpleasantness, vividness and emotionality were investigated. RESULTS: In Experiment 1, compared to Recall-only, both Conceptual-ImRs and Perceptual-ImRs led to greater decreases in unpleasantness, and Perceptual-ImRs led to greater decreases in emotionality of memories. In Experiment 2, the effects on unpleasantness were not replicated, and both Conceptual-ImRs and Perceptual-ImRs led to greater decreases in emotionality, compared to Recall-only, as did Positive-ImRs. There were no effects on vividness, and the ImRs conditions did not differ significantly from Recall+AB. CONCLUSIONS: Results suggest that, in addition to traditional forms of ImRs, targeting the meaning-relevant content of an image during ImRs, relatively simple techniques focusing on perceptual aspects or positive imagery might also yield benefits. Findings require replication and extension to clinical samples

VLSI implementation of a multi-mode turbo/LDPC decoder architecture

Flexible and reconfigurable architectures have gained wide popularity in the communications field. In particular, reconfigurable architectures for the physical layer are an attractive solution not only to switch among different coding modes but also to achieve interoperability. This work concentrates on the design of a reconfigurable architecture for both turbo and LDPC codes decoding. The novel contributions of this paper are: i) tackling the reconfiguration issue introducing a formal and systematic treatment that, to the best of our knowledge, was not previously addressed; ii) proposing a reconfigurable NoCbased turbo/LDPC decoder architecture and showing that wide flexibility can be achieved with a small complexity overhead. Obtained results show that dynamic switching between most of considered communication standards is possible without pausing the decoding activity. Moreover, post-layout results show that tailoring the proposed architecture to the WiMAX standard leads to an area occupation of 2.75 mm2 and a power consumption of 101.5 mW in the worst case

Rewriting Codes for Joint Information Storage in Flash Memories

Memories whose storage cells transit irreversibly between states have been common since the start of the data storage technology. In recent years, flash memories have become a very important family of such memories. A flash memory cell has q states—state 0.1.....q-1 - and can only transit from a lower state to a higher state before the expensive erasure operation takes place. We study rewriting codes that enable the data stored in a group of cells to be rewritten by only shifting the cells to higher states. Since the considered state transitions are irreversible, the number of rewrites is bounded. Our objective is to maximize the number of times the data can be rewritten. We focus on the joint storage of data in flash memories, and study two rewriting codes for two different scenarios. The first code, called floating code, is for the joint storage of multiple variables, where every rewrite changes one variable. The second code, called buffer code, is for remembering the most recent data in a data stream. Many of the codes presented here are either optimal or asymptotically optimal. We also present bounds to the performance of general codes. The results show that rewriting codes can integrate a flash memory’s rewriting capabilities for different variables to a high degree