Temporal Streaming Research Articles

Early Alert of Elderly Cognitive Impairment using Temporal Streaming Clustering.

more than 44 million people have been diagnosed with dementia worldwide, and this number is estimated to triple by next three decades. Given this increasing trend of older adults with cognitive impairment (CI; dementia and mild cognitive impairment) and its significant underdiagnosis, early identification of CI and understanding its progression is a critical step towards a better quality of life for the aging population. Early alert of individual health changes could facilitate better ways for clinicians to diagnose CI in its early stages and come up with more effective treatment plans. However, there is a lack of approaches to characterize patient health conditions accounting for temporal information in an unsupervised manner. Limited CI cases and its costly ascertainment in clinical settings also make unsupervised learning more promising in CI research. In this paper, a streaming clustering model was used to determine distinct patterns of older adults' health changes from their clinical visits in Mayo Clinic Study of Aging. The streaming clustering was also examined to study its ability to generate early alerts for potential incidents of CI. Our analysis demonstrated that temporal characteristics incorporated in a streaming clustering model has a promising potential to increase power in predicting CI.

Effect of auditory stream segregation cues on binaural pitch fusion

Binaural pitch fusion, the fusion of dichotically presented tones that evoke different pitches across ears, can be influenced by temporal envelope cues such as coherent amplitude modulation (Oh and Reiss, ASA 2017). This suggests that binaural pitch fusion may be governed at least in part by top-down processes involved in auditory grouping. The current study was designed to investigate how temporal streaming cues either prior or posterior to a fused dichotic tone pair can influence binaural pitch fusion. Six normal-hearing (NH) listeners and six bilateral hearing-aid (HA) users were tested in a modified auditory streaming paradigm (Steiger and Bregman, 1982). A pair of simultaneous, dichotic reference and comparison stimuli was placed in rapid alternation with a third binaural (diotic) capture stimulus, for a total of 10 alternations. The binaural fusion ranges, the frequency ranges over which binaural pitch fusion occurred for dichotic stimuli, were smaller when measured with the streaming paradigm than ...

Scalable progressive analytics on big data in the cloud

Analytics over the increasing quantity of data stored in the Cloud has become very expensive, particularly due to the pay-as-you-go Cloud computation model. Data scientists typically manually extract samples of increasing data size (progressive samples) using domain-specific sampling strategies for exploratory querying. This provides them with user-control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. We propose a new progressive analytics system based on a progress model called Prism that (1) allows users to communicate progressive samples to the system; (2) allows efficient and deterministic query processing over samples; and (3) provides repeatable semantics and provenance to data scientists. We show that one can realize this model for atemporal relational queries using an unmodified temporal streaming engine, by re-interpreting temporal event fields to denote progress. Based on Prism, we build Now!, a progressive data-parallel computation framework for Windows Azure, where progress is understood as a first-class citizen in the framework. Now! works with "progress-aware reducers"- in particular, it works with streaming engines to support progressive SQL over big data. Extensive experiments on Windows Azure with real and synthetic workloads validate the scalability and benefits of Now! and its optimizations, over current solutions for progressive analytics.

Figuring the Porous Self: St. Augustine and the Phenomenology of Temporality

AbstractThis article examines the phenomenological structures of the homo temporalis filtered through Augustine's illuminating, if unsystematic, insights on temporality and the imago Dei. It situates such a phenomenological interpretation of the Augustinian self in view of current interpretations that polarize or split the Augustinian self into an either/or scheme—either an “interior” self or an “exterior” self. Given this imbalance, the article suggests that a phenomenological evaluation of Augustine brings to light how interior and exterior spheres are deeply integrated. The article elaborates this position by contending that the self's temporal streaming within the exterior world‐horizon is inescapable because it reflects basic constituents of a self created by God which is nevertheless capable of contemplating a God who transcends time. This seeming paradox is resolved by recourse to what is described as the “double entry” of the self. The temporal streaming of the self in the world‐horizon (entry one) is porous to the eternal inwardly (entry two); the eternal entry is thus interior and analyzable in terms of a non‐reflective self‐awareness on display in Augustine's De Trinitate; and finally Augustine's understanding of the temporality of faith indicates how the self of faith can be lived in light of Heidegger's emphasis on the future and Husserl's emphasis on the past.

Spatio-temporal memory streaming

Recent research advocates memory streaming techniques to alleviate the performance bottleneck caused by the high latencies of off-chip memory accesses. Temporal memory streaming replays previously observed miss sequences to eliminate long chains of dependent misses. Spatial memory streaming predicts repetitive data layout patterns within fixed-size memory regions. Because each technique targets a different subset of misses, their effectiveness varies across workloads and each leaves a significant fraction of misses unpredicted. In this paper, we propose Spatio-Temporal Memory Streaming (STeMS) to exploit the synergy between spatial and temporal streaming. We observe that the order of spatial accesses repeats both within and across regions. STeMS records and replays the temporal sequence of region accesses and uses spatial relationships within each region to dynamically reconstruct a predicted total miss order. Using trace-driven and cycle-accurate simulation across a suite of commercial workloads, we demonstrate that with similar implementation complexity as temporal streaming, STeMS achieves equal or higher coverage than spatial or temporal memory streaming alone, and improves performance by 31%, 3%, and 18% over stride, spatial, and temporal prediction, respectively.

Temporal Streaming of Shared Memory

Coherent read misses in shared-memory multiprocessors account for a substantial fraction of execution time in many important scientific and commercial workloads. We propose Temporal Streaming, to eliminate coherent read misses by streaming data to a processor in advance of the corresponding memory accesses. Temporal streaming dynamically identifies address sequences to be streamed by exploiting two common phenomena in shared-memory access patterns: (1) temporal address correlation - groups of shared addresses tend to be accessed together and in the same order, and (2) temporal stream locality - recently-accessed address streams are likely to recur. We present a practical design for temporal streaming. We evaluate our design using a combination of trace-driven and cycle-accurate full-system simulation of a cache-coherent distributed shared-memory system. We show that temporal streaming can eliminate 98% of coherent read misses in scientific applications, and between 43% and 60% in database and web server workloads. Our design yields speedups of 1.07 to 3.29 in scientific applications, and 1.06 to 1.21 in commercial workloads.

Temporal streaming based on fine structure cues

Alternating sequence of two A and B bursts of sound tends to be perceptually organized in different auditory streams when introducing either a spectral or a temporal difference between A and B. In general, the channeling theory of streaming predicts that any salient difference between the excitation patterns evoked by A and B sounds would lead to a segregated percept. Some previous studies have, however, evidenced that a sequence of sounds with similar spectral properties but with different temporal properties can be heard as segregated. In particular, temporal cues are probably responsible for the segregated percept when hearing a sequence of bursts of noises that are amplitude-modulated at different rates. All previous streaming experiments involved stimuli that had frequency components below 5000 Hz. As a consequence, the individual contribution of temporal fine structure cues and envelope cues could not be dissociated and remains largely undetermined. The current experiment is dedicated to test further the relative importance of both envelope and fine structure cues to segregate sequences of unresolved complex tones with different fundamental frequencies. Two high-frequency regions above and below 5000 Hz and several phase relationships leading to several temporal peak factors have been used in a subjective streaming task.