OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Valued Routing Tracks for FPGAs in 28nm FDSOI Technology
Venue: arXiv cs.AR 2016
Year: 2016

In this paper we present quaternary and ternary routing tracks for FPGAs, and their implementation in 28nm FDSOI technology. We discuss the transistor level design of multi-valued repeaters, multiplexers and translators, and specific features of FDSOI technology which make it possible. Next we compare the multi-valued routing architectures with equivalent single driver two-valued routing architectures. We show that for long tracks, it is possible to achieve upto 3x reduction in dynamic switching energy, upto 2x reduction in routing wire area and 10% reduction in area dedicated to routing resources. The multi-valued tracks are slightly more susceptible to process variation. We present a layout method for multivalued standard cells and determine the layout overhead.We conclude with various usage scenarios of these tracks.
Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses
Venue: arXiv cs.NE 2016
Year: 2016

Current large scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. Emerging memory technologies such as nanoscale two-terminal resistive switching memory devices offer a compact, scalable and low power alternative that permits on-chip co-located processing and memory in fine-grain distributed parallel architecture. Here we report first use of resistive switching memory devices for implementing and training a Restricted Boltzmann Machine (RBM), a generative probabilistic graphical model as a key component for unsupervised learning in deep networks. We experimentally demonstrate a 45-synapse RBM realized with 90 resistive switching phase change memory (PCM) elements trained with a bio-inspired variant of the Contrastive Divergence (CD) algorithm, implementing Hebbian and anti-Hebbian weight updates. The resistive PCM devices show a two-fold to ten-fold reduction in error rate in a missing pixel pattern completion task trained over 30 epochs, compared to untrained case. Measured programming energy consumption is 6.1 nJ per epoch with the resistive switching PCM devices, a factor of ~150 times lower than conventional processor-memory systems. We analyze and discuss the dependence of learning performance on cycle-to-cycle variations as well as number of gradual levels in the PCM analog memory devices.
Access to Taxicabs for Unbanked Households: An Exploratory Analysis in New York City
Venue: arXiv cs.CY 2016
Year: 2016

Taxicabs are a critical aspect of the public transit system in New York City. The yellow cabs that are ubiquitous in Manhattan are as iconic as the city's subway system, and in recent years green taxicabs were introduced by the city to improve taxi service in areas outside of the central business districts and airports. Approximately 500,000 taxi trips are taken daily, carrying about 800,000 passengers, and not including other livery firms such as Uber, Lyft or Carmel. Since 2008 yellow taxis have been able to process fare payments with credit cards, and credits cards are a growing share of total fare payments. However, the use of credit cards to pay for taxi fares varies widely across neighborhoods, and there are strong correlations between cash payments for taxi fares and the presence of unbanked or underbanked populations. These issues are of concern for policymakers as approximately ten percent of households in the city are unbanked, and in some neighborhoods the share of unbanked households is over 50 percent. In this paper we use multiple datasets to explore taxicab fare payments by neighborhood and examine how access to taxicab services is associated with use of conventional banking services. There is a clear spatial dimension to the propensity of riders to pay cash, and we find that both immigrant status and being 'unbanked' are strong predictors of cash transactions for taxicabs. These results have implications for local regulations of the for-hire vehicle industry, particularly in the context of the rapid growth of services that require credit cards. Without some type of cash-based payment option taxi services will isolate certain neighborhoods. At the very least, existing and new providers of transit services must consider access to mainstream financial products as part of their equity analyses.
Telecom data for efficient malaria interventions
Venue: arXiv cs.CY 2016
Year: 2016

Telecom data is rich on mobility information and as such can be used to identify mobility patterns of people in near real time, enabling to build epidemiological models for understanding where epidemics might spread over time. Based on previous research, we have built an operational tool fed with telecom data which shows malaria risk flows in Zambia in near real time. It provides insights on which areas should eradicate malaria first in order to have a maximum impact on the overall country malaria flows, and it highlights regions that should coordinate their eradication efforts together. Such information is particularly relevant for countries like Zambia, which are getting close to malaria elimination and need to prevent its re-introduction into areas that are already malaria-free.
Moving the Needle: What Works Cities and the use of data and evidence
Venue: arXiv cs.CY 2016
Year: 2016

Bloomberg Philanthropies launched What Works Cities (WWC) in 2015 to help cities better leverage data and evidence to drive decision-making and improve residents' lives. Over three years, WWC will work with 100 American cities with populations between 100,000 and 1,000,000 to measure their state of practice and provide targeted technical assistance. This paper uses the data obtained through the WWC discovery process to understand how 67 cities are currently using data to deliver city services. Our analysis confirms that while cities possess a strong desire to use data and evidence, government leaders are constrained in their ability to apply these practices. We find that a city's stated commitment to using data is the strongest predictor of overall performance and that strong practice in almost any one specific technical area of using data to inform decisions is an indicator of strong practices in other areas. The exception is open data; we find larger cities are more adept at adopting open data policies and programs, independent of their performance using data overall. This paper seeks to develop a deeper understanding of the issues underlying these findings and to continue the conversation on how to best support cities' efforts in this work.
A Case in Kenya: Unlocking bottlenecks in public health supply chains through data dashboards and enhanced governance structures
Venue: arXiv cs.CY 2016
Year: 2016

The link between data and governance are key to making public health supply chains more integrated and responsive in order to get life-saving commodities to those in need. In particular, considering its significant health challenges, poor maternal and child health indicators, and major recent devolution in political authority, Kenya represents a country in need of an innovative revamp of their data management and governance. John Snow, Inc. (JSI) adapted various elements of proven interventions to build a customized structure to support routine data collection in order to drive decision making around supply chain improvement.
A Functioning Beta Solution to the Challenge of Opening Transit Payment System Transaction Data
Venue: arXiv cs.CY 2016
Year: 2016

The deployment of smart-card-based public transit fare payment systems provides government the opportunity to create a valuable derivative data product. Companies such as Urban Engines have demonstrated an ability to add value to the data derived from transit fare transactions. The challenge for the public sector is to, for the societal good, leverage private sector interest by giving access to useful fare transaction data in a manner that protects customer privacy. This challenge is particularly acute in California, where privacy laws make sharing data in a manner that supports the public interest difficult. This paper presents the Metropolitan Transportation Commission's (MTC's) proposed solution to the problem. MTC operates the Clipper(r) transit fare payment system for the San Francisco Bay Area. In an effort to share usable data that protects customer privacy, MTC developed an anonymizing scheme that is the subject of the present paper. We seek feedback on our approach from the Data for Good Exchange community, asking: in seeking a balance between customer privacy and usability, does the scheme go too far in either direction? And, should we take a different anonymizing approach?
Public Intervention Strategies for Distressed Communities
Venue: arXiv cs.CY 2016
Year: 2016

This research presents a methodology to comprehensively define Distressed Communities. We further identify if there is a significant difference in public investment between Distressed communities and Wealthy communities. One of the key tools in sustainability planning is the use of sustainability indicators (Sis). A considerable amount of scholarship has contributed to define and develop SI programs for local level application (Elgert and Krueger, 2012). Much of the focus of SI research is on developing the ideal indicator based on defined criteria for each indicator (Hart, 1999; Innes and Booher 2000; Holman, 2009). Here we suggest a methodology beyond defining the ideal indicators to demonstrating how indicators can be used for more in-depth analysis of complex urban problems. In this analysis we reduce 34 development metrics to a smaller number of factors that represent how the data can be classified into groups based on similarities among 88 communities. Using the factor (group) that contained measures identifying Distressed Communities, the communities were alloted an index score and ranked. The top 10 communities were then compared to the bottom 10 communities according to 14 place based variables related to opportunities for local government led improvement.
Understanding data augmentation for classification: when to warp?
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier. Two approaches for creating additional training samples are data warping, which generates additional samples through transformations applied in the data-space, and synthetic over-sampling, which creates additional samples in feature-space. We experimentally evaluate the benefits of data augmentation for a convolutional backpropagation-trained neural network, a convolutional support vector machine and a convolutional extreme learning machine classifier, using the standard MNIST handwritten digit dataset. We found that while it is possible to perform generic augmentation in feature-space, if plausible transforms for the data are known then augmentation in data-space provides a greater benefit for improving performance and reducing overfitting.
Data Accessibility as a Pathway to Genuine Equality for Atlanta's Westside Communities
Venue: arXiv cs.CY 2016
Year: 2016

Data is a dominant force during the decision-making process. It can help determine which roads to expand and the optimal location for a grocery store. Data can also be used to influence which schools to open or to shutter and "appropriate" city services to continue or discontinue. Considered fact-based, objective, and impartial, data can trump emotional appeals during the final evaluation of a project; thus creating a power imbalance between those with the resources to access data and those without. Most often left behind are communities already struggling to stay afloat due to years of disinvestment by market forces and external decision-makers. For long ignored residents in Atlanta's Westside neighborhoods, the burden of inaccessible data continuously thwarts their opportunity for mobility. However, with the advent of the internet and the global push for open data, access to information is no longer solely in the hands of those with power, influence and money. Online tools, like the Westside Communities Alliance (WCA) Data Dashboard, quickly disseminate data to those most impacted by "data driven decision-making," thus creating the potential of a genuinely equitable society. Based out of the Georgia Institute of Technology, the WCA works to build and sustain relationships among constituencies located in West Atlanta with the goal to strengthen partnerships around issues of common concern. The creation of the Data Dashboard stemmed from a recognized community desire for more localized control and the need for improvements to the communities' overall prosperity. Development of the site progressed through significant engagement between the WCA, community groups, and local agencies. The Dashboard takes the vast abundance of data and synthesizes it into a format that is both visually and geographically user-friendly. Through different portals, users can access neighborhood-level data around demographics, housing, education, and history that is formatting in a way that is easily accessible and understandable. The site includes qualitative research that goes beyond data and stats to give a totality of the community. By allowing West Atlanta advocacy groups to easily retrieve data, the WCA Data Dashboard empowers residents, nonprofits, and neighborhood associations to be full participants in the decision-making process.
Reduced-Complexity SCL Decoding of Multi-CRC-Aided Polar Codes
Venue: arXiv cs.IT 2016
Year: 2016

Cyclic redundancy check (CRC) aided polar codes are capable of achieving better performance than low-density parity-check (LDPC) codes under the successive cancelation list (SCL) decoding scheme. However, the SCL decoding scheme suffers from very high space and time complexities. Especially, the high space complexity is a major concern for adopting polar codes in modern mobile communication standards. In this paper, we propose a novel reduced-complexity successive cancelation list (R-SCL) decoding scheme which is effective to reduce the space complexity. Simulation results show that, with a (2048, 1024) CRC-aided polar code, the R-SCL decoders with 25% reduction of space complexity and 8% reduction of time complexity can still achieve almost the same performance levels as those decoded by SCL decoders. To further reduce the complexity, we propose a multi-CRC coding scheme for polar codes. Simulation results show that, with a (16384, 8192) multi-CRC-aided polar code, a R-SCL decoder with about 85% reduction of space complexity and 20% reduction of time complexity results in a worst performance loss of only 0.04dB.
A Nondeterministic Model for Abstract Geometrical Computation
Venue: arXiv cs.ET 2016
Year: 2016

A signal machine is an abstract geometrical model for computation, proposed as an extension to the one-dimensional cellular automata, in which discrete time and space of cellular automata is replaced with continuous time and space in signal machine. A signal machine is defined as a set of meta-signals and a set of rules. A signal machine starts from an initial configuration which is a set of moving signals. Signals are moving in space freely until a collision. Rules of signal machine specify what happens after a collision, or in other words, specify out-coming signals for each set of colliding signals. Originally signal machine is defined by its rule as a deterministic machine. In this paper, we introduce the concept of non-deterministic signal machine, which may contain more than one defined rule for each set of colliding signals. We show that for a specific class of nondeterministic signal machines, called k-restricted nondeterministic signal machine, there is a deterministic signal machine computing the same result as the nondeterministic one, on any given initial configuration. k-restricted nondeterministic signal machine is a nondeterministic signal machine which accepts an input iff produces a special accepting signal, which have at most two nondeterministic rule for each collision, and at most k collisions before any acceptance.
Deep Architectures for Face Attributes
Venue: arXiv cs.CV 2016
Year: 2016

We train a deep convolutional neural network to perform identity classification using a new dataset of public figures annotated with age, gender, ethnicity and emotion labels, and then fine-tune it for attribute classification. An optimal sharing pattern of computational resources within this network is determined by experiment, requiring only 1 G flops to produce all predictions. Rather than fine-tune by relearning weights in one additional layer after the penultimate layer of the identity network, we try several different depths for each attribute. We find that prediction of age and emotion is improved by fine-tuning from earlier layers onward, presumably because deeper layers are progressively invariant to non-identity related changes in the input.
A Study on Altering PostgreSQL from Multi-Processes Structure to Multi-Threads Structure
Venue: arXiv cs.DB 2016
Year: 2016

How to altering PostgreSQL database from multi-processes structure to multi-threads structure is a difficult problem. In the paper, we bring forward a comprehensive alteration scheme. Especially, put rational methods to account for three difficult points: semaphores, signal processing and global variables. At last, applied the scheme successfully to modify a famous open source DBMS.
Refugee Resettlement Housing Scout
Venue: arXiv cs.CY 2016
Year: 2016

According to the United States High Commission for Refugees (UNHCr), there are 65.3 million forcibly displaced people in the world today, 21.5 million of them being refugees. This has led to an unprecedented refugee crisis which has led countries to accept refugee families and to resettle them. Diverse agencies are helping refugees coming to US to resettle and start their new life in the country. One of the first and most challenging steps of this process is to find affordable housing that also meets a suite of additional constraints and priorities. These include being within a mile of public transportation and near schools, faith centers and international grocery stores. We detail an interactive data-driven web-based tool, which incorporates in one consolidated platform most of the needed information. The tool searches, filters and demonstrates a list of possible housing locations, and allows for the dynamic prioritization based on user-specified importance weights on the diverse criteria. The platform was created in a partnership with New American Pathways, a nonprofit that supports refugee resettlement in the metro Atlanta, but exemplifies a methodology that can help many other organizations with similar goals.
Using Big Data to Decode Private Sector Wage Growth
Venue: arXiv cs.CY 2016
Year: 2016

The U.S. labor market is dynamic and complex, and understanding wage data across different segments of the workforce is critical to providing policymakers and business leaders with actionable insights. There is no labor index that assesses the labor market performance at such a detailed level as the ADP Research Institute's Workforce Vitality Report (WVR). Drawing on the actual, aggregated and anonymous payroll data of 24 million Americans paid by ADP, the WVR looks at key dynamics and market indicators including wage growth, hours worked and turnover rate. Unlike other data sets, the WVR calculates wage growth of individual workers on a quarter-to-quarter basis, avoiding the deviations caused by various workplace occurrences, like when new workers are hired and older ones retire. In this paper, Dr. Ahu Yildirmaz, head of the ADP Research Institute, drills down into wage growth by industry, age, gender and income level, as well as for both job holders and job switchers. Using WVR data, Ahu walks through those factors contributing to overall shifts in wage growth, the future of the labor market and what this data means for today's U.S. workforce.
Analysis of Massive Heterogeneous Temporal-Spatial Data with 3D Self-Organizing Map and Time Vector
Venue: arXiv cs.LG 2016
Year: 2016

Self-organizing map(SOM) have been widely applied in clustering, this paper focused on centroids of clusters and what they reveal. When the input vectors consists of time, latitude and longitude, the map can be strongly linked to physical world, providing valuable information. Beyond basic clustering, a novel approach to address the temporal element is developed, enabling 3D SOM to track behaviors in multiple periods concurrently. Combined with adaptations targeting to process heterogeneous data relating to distribution in time and space, the paper offers a fresh scope for business and services based on temporal-spatial pattern.
Exploiting Energy Accumulation Against Co-channel Interference in Wireless Energy Harvesting MIMO Relaying
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates a three-node multiple-input multiple-output relay system suffering from co-channel interference (CCI) at the multi-antenna relay. Contrary to the conventional relay networks, we consider the scenario that the relay is an energy harvesting (EH) node and has no embedded energy supply. But it is equipped with a rechargeable battery such that it can harvest and accumulate the harvested energy from RF signals sent by the source and co-channel interferers to support its operation. Leveraging the inherent feature of the considered system, we develop a novel accumulate-then-forward (ATF) protocol to eliminate the harmful effect of CCI. In the proposed ATF scheme, at the beginning of each transmission block, the relay can choose either EH operation to harvest energy from source and CCI or information decoding (ID) operation to decode and forward source's information while suffering from CCI. Specifically, ID operation is activated only when the accumulated energy at the relay can support an outage-free transmission in the second hop. Otherwise, EH operation is invoked at the relay to harvest and accumulate energy. By modeling the finite-capacity battery of relay as a finite-state Markov Chain (MC), we derive a closed-form expression for the system throughput of the proposed ATF scheme over mixed Nakagami-m and Rayleigh fading channels. Numerical results validate our theoretical analysis, and show that the proposed ATF scheme with energy accumulation significantly outperforms the existing one without energy accumulation.
DPHMM: Customizable Data Release with Differential Privacy via Hidden Markov Model
Venue: arXiv cs.DB 2016
Year: 2016

Hidden Markov model (HMM) has been well studied and extensively used. In this paper, we present DPHMM ({Differentially Private Hidden Markov Model}), an HMM embedded with a private data release mechanism, in which the privacy of the data is protected through a graph. Specifically, we treat every state in Markov model as a node, and use a graph to represent the privacy policy, in which "indistinguishability" between states is denoted by edges between nodes. Due to the temporal correlations in Markov model, we show that the graph may be reduced to a subgraph with disconnected nodes, which become unprotected and might be exposed. To detect such privacy risk, we define sensitivity hull and degree of protection based on the graph to capture the condition of information exposure. Then to tackle the detected exposure, we study how to build an optimal graph based on the existing graph. We also implement and evaluate the DPHMM on real-world datasets, showing that privacy and utility can be better tuned with customized policy graph.