DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis

Training convolutional neural networks (CNNs) requires intense compute throughput and high memory bandwidth. Especially, convolution layers account for the majority of the execution time of CNN training, and GPUs are commonly used to accelerate these layer workloads. GPU design optimization for efficient CNN training acceleration requires the accurate modeling of how their performance improves when computing and memory resources are increased. We present DeLTA, the first analytical model that accurately estimates the traffic at each GPU memory hierarchy level, while accounting for the complex reuse patterns of a parallel convolution algorithm. We demonstrate that our model is both accurate and robust for different CNNs and GPU architectures. We then show how this model can be used to carefully balance the scaling of different GPU resources for efficient CNN performance improvement

Use of human pluripotent stem cells to define initiating molecular mechanisms of cataract for anti-cataract drug discovery

Cataract is a leading cause of blindness worldwide. Currently, restoration of vision in cataract patients requires surgical removal of the cataract. Due to the large and increasing number of cataract patients, the annual cost of surgical cataract treatment amounts to billions of dollars. Limited access to functional human lens tissue during the early stages of cataract formation has hampered efforts to develop effective anti-cataract drugs. The ability of human pluripotent stem (PS) cells to make large numbers of normal or diseased human cell types raises the possibility that human PS cells may provide a new avenue for defining the molecular mechanisms responsible for different types of human cataract. Towards this end, methods have been established to differentiate human PS cells into both lens cells and transparent, light-focusing human micro-lenses. Sensitive and quantitative assays to measure light transmittance and focusing ability of human PS cell-derived micro-lenses have also been developed. This review will, therefore, examine how human PS cell-derived lens cells and micro-lenses might provide a new avenue for development of much-needed drugs to treat human cataract

Beyond individual, population, and community: Considering information, cell number, and energy flux as fundamental dimensions of life across scales

We suggest rethinking ecology as a set of continuous, interconnected dynamics and spatial networks that would represent an alternative framework to the traditional organizational levels—cell, organism, population, community, and ecosystem. We draw on unifying biological theories—information theory, cell theory, and metabolic theories—to propose a continuous space for living systems that avoids epistemological constraints imposed by a priori assumptions of discrete levels of biological organization. The discrete levels of organization that ecologists have traditionally envisioned can be viewed instead as coarse-grained, localized groupings within continuous dimensions of information (I), cell number (C), and energy flux (E). Moving from levels of organization to emergent properties of information, cells, and energy flux allows us to illustrate how diverse ecological and evolutionary phenomena—such as allometric and diversity-related scaling patterns, predator-prey dynamics, evolutionary trajectories, and alternative stable states—can be represented within the same continuum. We suggest that there may be structure within this information-cells-energetic flux (ICE) framework that unifies ecology from the beginning of life to the present and provides a useful lens through which patterns and processes in nature can be viewed

TRECVID 2004 experiments in Dublin City University

In this paper, we describe our experiments for TRECVID 2004 for the Search task. In the interactive search task, we developed two versions of a video search/browse system based on the Físchlár Digital Video System: one with text- and image-based searching (System A); the other with only image (System B). These two systems produced eight interactive runs. In addition we submitted ten fully automatic supplemental runs and two manual runs. A.1, Submitted Runs: • DCUTREC13a_{1,3,5,7} for System A, four interactive runs based on text and image evidence. • DCUTREC13b_{2,4,6,8} for System B, also four interactive runs based on image evidence alone. • DCUTV2004_9, a manual run based on filtering faces from an underlying text search engine for certain queries. • DCUTV2004_10, a manual run based on manually generated queries processed automatically. • DCU_AUTOLM{1,2,3,4,5,6,7}, seven fully automatic runs based on language models operating over ASR text transcripts and visual features. • DCUauto_{01,02,03}, three fully automatic runs based on exploring the benefits of multiple sources of text evidence and automatic query expansion. A.2, In the interactive experiment it was confirmed that text and image based retrieval outperforms an image-only system. In the fully automatic runs, DCUauto_{01,02,03}, it was found that integrating ASR, CC and OCR text into the text ranking outperforms using ASR text alone. Furthermore, applying automatic query expansion to the initial results of ASR, CC, OCR text further increases performance (MAP), though not at high rank positions. For the language model-based fully automatic runs, DCU_AUTOLM{1,2,3,4,5,6,7}, we found that interpolated language models perform marginally better than other tested language models and that combining image and textual (ASR) evidence was found to marginally increase performance (MAP) over textual models alone. For our two manual runs we found that employing a face filter disimproved MAP when compared to employing textual evidence alone and that manually generated textual queries improved MAP over fully automatic runs, though the improvement was marginal. A.3, Our conclusions from our fully automatic text based runs suggest that integrating ASR, CC and OCR text into the retrieval mechanism boost retrieval performance over ASR alone. In addition, a text-only Language Modelling approach such as DCU_AUTOLM1 will outperform our best conventional text search system. From our interactive runs we conclude that textual evidence is an important lever for locating relevant content quickly, but that image evidence, if used by experienced users can aid retrieval performance. A.4, We learned that incorporating multiple text sources improves over ASR alone and that an LM approach which integrates shot text, neighbouring shots and entire video contents provides even better retrieval performance. These findings will influence how we integrate textual evidence into future Video IR systems. It was also found that a system based on image evidence alone can perform reasonably and given good query images can aid retrieval performance

The refugee/migrant crisis dichotomy on twitter: A network and sentiment perspective

Media reports, political statements, and social media debates on the refugee/migrant crisis shape the ways in which people and societies respond to those displaced people arriving at their borders world wide. These current events are framed and experienced as a crisis, entering the media, capturing worldwide political attention, and producing diverse and contradictory discourses and responses. The labels “migrant” and “refugee” are frequently distinguished and conflated in traditional as well as social media when describing the same groups of people. In this paper, we focus on the simultaneous struggle over meaning, legitimization, and power in representations of the refugee crisis, through the specific lens of Twitter. The 369,485 tweets analyzed in this paper cover two days after a picture of Alan Kurdi - a three-year-old Syrian boy who drowned in the Mediterranean Sea while trying to reach Europe with his family - made global headlines and sparked wide media engagement. More specifically, we investigate the existence of the dichotomy between the “deserving” refugee versus the “undeserving” migrant, as well as the relationship between sentiment expressed in tweets, their influence, and the popularity of Twitter users involved in this dichotomous characterization of the crisis. Our results show that the Twitter debate was predominantly focused on refugee related hashtags and that those tweets containing such hashtags were more positive in tone. Furthermore, we find that popular Twitter users as well as popular tweets are characterized by less emotional intensity and slightly less positivity in the debate, contrary to prior expectations. Co-occurrence networks expose the structure underlying hashtag usage and reveal a refugee-centric core of meaning, yet divergent goals of some prominent users. As social media become increasingly prominent venues for debate over a crisis, how and why people express their opinions offer valuable insights into the nature and direction of these debates