An adaptive generation method for electrophoretic display driving waveform design

Electrophoretic display (EPD) technology is attractive when used for reading devices such as e‐paper because of its paper‐like appearance. EPD driving is more complex than other display technologies such as liquid crystal display or organic light emission diode because the driving result of a pixel strongly depends on the initial display state. The particle size and distribution in the EPD film may vary, even if the initial state is the same. Therefore, the display devices vary between different manufacturing batches. Furthermore, different display modes such as videos, pictures or documents need different driving waveforms to achieve an optimal result. EPD manufacturers need to build a customized driving waveform for every manufacturing batch. This is very inconvenient if new applications on EPD are to be developed. And the workload is huge. In this work, an adaptive method is described for automatically creating EPD driving waveforms to fit different conditions. The central idea of this method is generally adjusting the driving time and the voltage state after getting the feedback from a measurement model. In this method, a new driving waveform is used to reduce refresh time and visual flicker. The experimental results show that the proposed approach can automatically and adaptively generate an EPD driving waveform with reasonable quality.     

Pointwise and pairwise clothing annotation: combining features from social media

In this paper, we present effective algorithms to automatically annotate clothes from social media data, such as Facebook and Instagram. Clothing annotation can be informally stated as recognizing, as accurately as possible, the garment items appearing in the query photo. This task brings huge opportunities for recommender and e-commerce systems, such as capturing new fashion trends based on which clothes have been used more recently. It also poses interesting challenges for existing vision and recognition algorithms, such as distinguishing between similar but different types of clothes or identifying a pattern of a cloth even if it has different colors and shapes. We formulate the annotation task as a multi-label and multi-modal classification problem: (i) both image and textual content (i.e., tags about the image) are available for learning classifiers, (ii) the classifiers must recognize a set of labels (i.e., a set of garment items), and (iii) the decision on which labels to assign to the query photo comes from a set of instances that is used to build a function, which separates labels that should be assigned to the query photo, from those that should not be assigned. Using this configuration, we propose two approaches: (i) the pointwise one, called MMCA, which receives a single image as input, and (ii) a multi-instance classification, called M3CA, also known as pairwise approach, which uses pair of images to create the classifiers. We conducted a systematic evaluation of the proposed algorithms using everyday photos collected from two major fashion-related social media, namely pose.com and chictopia.com. Our results show that the proposed approaches provide improvements when compared to popular first choice multi-label, multi-modal, multi-instance algorithms that range from 20 % to 30 % in terms of accuracy.     

Characterizing peers communities and dynamics in a P2P live streaming system

Despite the large number of works devoted to understand P2P live streaming applications, most of them put forth so far rely on characterizing the static view of these systems. In this work, we characterize the SopCast, one of the most important P2P live streaming applications. We focus on its dynamics behavior as well as on the community formation phenomena. Our results show that SopCast presents a low overlay topology diameter and low end-to-end shortest path. In fact, diameter is smaller than 6 hops in almost 90 % of the observation time. More than 96 % of peers’ end-to-end connections present only 3 hops. These values combined may lead to low latencies and a fast streaming diffusion. Second, we show that communities in SopCast are well defined by the streaming data exchange process. Moreover, the SopCast protocol does not group peers according to their Autonomous System. In fact, the probability that a community contains 50 % of its members belonging to the same AS (when we observe the largest AS of our experiments) is lower then 10 %. Peers exchange more data with partners belonging to the same community instead of peers inside the same AS. For the largest AS we have, less than 18 % of peer traffic has been exchanged with another AS partners. Finally, our analysis provides important information to support the future design of more efficient P2P live streaming systems and new protocols that exploit communities’ relationships.     

Our current understanding of lake ecosystem response to climate change: What have we really learned from the north temperate deep lakes?

Climatic change is recognized as an important factor capable of influencing the structural properties of aquatic ecosystems. Lake ecosystems are particularly sensitive to climate change. Several long time-series studies have shown close coupling between climate, lake thermal properties and individual organism physiology, population abundance, community structure, and food-web structure. Understanding the complex interplay between climate, hydrological variability, and ecosystem structure and functioning is essential to inform water resources risk assessment and fisheries management. The purpose of this paper is to present the current understanding of climate-induced changes on lake ecosystem phenology. We first review the ability of climate to modulate the interactions among lake hydrodynamics, chemical factors, and food-web structure in several north temperate deep lakes (e.g., Lake Washington, Lake Tahoe, Lake Constance, Lake Geneva, Lake Baikal, and Lake Zurich). Our aim is to assess long-term trends in the physical (e.g., temperature, timing of stratification, and duration of ice cover), chemical (e.g., nutrient concentrations), and biological (e.g., timing of the spring bloom, phytoplankton composition, and zooplankton abundance) characteristics of the lakes and to examine the signature of local weather conditions (e.g., air temperature and rainfall) and large-scale climatic variability (e.g., ENSO and PDO) on the lake physics, chemistry and biology. We also conducted modeling experiments to quantify the relative effect of climate change and nutrient loading on lake phenology. These modeling experiments focused on the relative changes to the major causal associations underlying plankton dynamics during the spring bloom and the summer stratified period. To further understand the importance of climate change on lakes, we propose two complementary directions of future research. First, additional research is needed to elucidate the wide array of in-lake processes that are likely to be affected by the climate change. Second, it is essential to examine the heterogeneity in responses among different water bodies. The rationale of this approach and its significance for dealing with the uncertainty that the climate signals cascade through lake ecosystems and shape abiotic variability and/or biotic responses have been recently advocated by several other synthesis papers.     

Application Awareness Makes Storage More Useful

Application-aware storage offers a technology that can respond intelligently and quickly enough for organizations to meet the demands of their different users and programs.     

Data-Intensive Computing in the 21st Century

The deluge of data that future applications must process—in domains ranging from science to business informatics—creates a compelling argument for substantially increased R&D targeted at discovering scalable hardware and software solutions for data-intensive problems.     

Eigenfeature regularization and extraction in face recognition

This work proposes a subspace approach that regularizes and extracts eigenfeatures from the face image. Eigenspace of the within-class scatter matrix is decomposed into three subspaces: a reliable subspace spanned mainly by the facial variation, an unstable subspace due to noise and finite number of training samples and a null subspace. Eigenfeatures are regularized differently in these three subspaces based on an eigenspectrum model to alleviate problems of instability, over-fitting or poor generalization. This also enables the discriminant evaluation performed in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after the discriminant assessment. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experiments comparing the proposed approach with some other popular subspace methods on the FERET, ORL, AR and GT databases show that our method consistently outperforms others.     

A two-stage hardware scheduler combining greedy and optimal scheduling

Greedy scheduling heuristics provide a low complexity and scalable albeit particularly sub-optimal strategy for hardware-based crossbar schedulers. In contrast, the maximum matching algorithm for Bipartite graphs can be used to provide optimal scheduling for crossbar-based interconnection networks with a significant complexity and scalability cost. In this paper, we show how maximum matching can be reformulated in terms of Boolean operations rather than the more traditional formulations. By leveraging the inherent parallelism available in custom hardware design, we reformulate maximum matching in terms of Boolean operations rather than matrix computations and introduce three maximum matching implementations in hardware. Specifically, we examine a Pure Logic Scheduler with three dimensions of parallelism, a Matrix Scheduler with two dimensions of parallelism and a Vector Scheduler with one dimension of parallelism. These designs reduce the algorithmic complexity for an N×N$N\times N$ network from O(N³)$O\left(N^3\right)$ to O(1)$O\left(1\right)$, O(K)$O\left(K\right)$, and O(KN)$O\left(KN\right)$, respectively, where K$K$ is the number of optimization steps. While an optimal scheduling algorithm requires K=2N−1$K=2N-1$ steps, by starting with our hardware-based greedy strategy to generate an initial schedule, our simulation results show that the maximum matching scheduler can achieve 99% of the optimal schedule when K=9$K=9$. We examine hardware and time complexity of these architectures for crossbar sizes of up to N=1024$N=1024$. Using FPGA synthesis results, we show that a greedy schedule for crossbars, ranging from 8×8 to 256×256, can be optimized in less than 20 ns per optimization step. For crossbars reaching 1024×1024 the scheduling can be completed in approximately 10 μs with current technology and could reach under 90 ns with future technologies.     

Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.