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Recent advances in dimensionality reduction have achieved more accurate lower-dimensional embeddings of high-dimensional data. In addition to visualisation purposes, these embeddings can be used for downstream processing, including batch effect normalisation, clustering, community detection or trajectory inference. We use the notion of structure preservation at both local and global levels to create a deep learning model, based on a variational autoencoder (VAE) and the stochastic quartet loss from the SQuadMDS algorithm. Our encoder model, called GroupEnc, uses a 'group loss' function to create embeddings with less global structure distortion than VAEs do, while keeping the model parametric and the architecture flexible. We validate our approach using publicly available biological single-cell transcriptomic datasets, employing RNX curves for evaluation., Comment: Submitted to BNAIC/BeNeLearn 2023

A key purpose of the transportation system is to provide access to critical services such as grocery stores. Maintaining food access during an emergency or other disruption is all the more important, particularly for vulnerable households. Most people in the United States rely on the use of private automobiles for grocery shopping. Thus, disruptions to road networks due to heavy precipitation, flooding, or even major maintenance and repair projects present notable threats to accessibility. Regional planning models that address food accessibility issues (not all do) typically do not consider households’ familiarity with grocery locations. However, during a disruptive event, a household’s familiarity with at least one available route to a retail grocery location becomes paramount. Identifying the roadways that are most critical to food access can help decision makers devise strategies to mitigate the risks of food insecurity for vulnerable households and populations.Researchers at the University of Vermont developed a methodology that provides an ordinal measure of demand-side food access. It takes into account the spatial distribution of both the origin and destination, the topology of the road network, and the characteristics of the roadway network such as capacities, volumes, and travel speeds. The analysis considers household familiarity with retail grocery locations, destination weighting to account for retail grocery characteristics (square footage), and origin weighting to account for household vulnerability. The researchers demonstrated the methodology using the travel demand model for Chittenden County, Vermont. This policy brief summarizes the findings from that research and provide policy implications.View the NCST Project Webpage

Machine learning (ML) algorithms are increasingly being used to help implement clinical decision support systems. In this new field, we define as “translational machine learning”, joint efforts and strong communication between data scientists and clinicians help to span the gap between ML and its adoption in the clinic. These collaborations also improve interpretability and trust in translational ML methods and ultimately aim to result in generalizable and reproducible models. To help clinicians and bioinformaticians refine their translational ML pipelines, we review the steps from model building to the use of ML in the clinic. We discuss experimental setup, computational analysis, interpretability and reproducibility, and emphasize the challenges involved. We highly advise collaboration and data sharing between consortia and institutes to build multi-centric cohorts that facilitate ML methodologies that generalize across centers. In the end, we hope that this review provides a way to streamline translational ML and helps to tackle the challenges that come with it.

A brief profile of Senyawa and their roots in the underground music scene in Yogyakarta, Indonesia.

This article describes the political performances of the annual Project Fukushima! festival that—only a few months after the earthquake, tsunami, and nuclear disaster on 3/11/11, and each summer since—has brought thousands to sing, dance, and make music in Fukushima City. Why celebrate Fukushima in the midst of a meltdown? I argue that performance has taken on a vital critical dimension in the ambivalent pluralism that drives contemporary public protest movements. Music and dance— particularly in the festivals that have become deeply integrated into social activism in post-3/11 Japan—have become particularly expedient ways to enable broad critiques of technocultural capitalism and its silencing of marginal populations. The performativity of festival connotes but does not necessarily constitute public dialogue. Rather, it makes audible the dissonance of diverse political assemblies, who respond with ambivalence to demands to speak with a singular voice. I examine the ways in which the anthropology of social movements can attend to new performative assemblies that reframe narratives of disaster and displacement to describe emergent scenes of embodied interdependence in a global politics of survival. By blurring the lines between social expression and the disruptive noise of collective spectacle, Project Fukushima! builds the ambivalence of regional culture into a platform for amplifying the noise of political community in the disaster zone.

Retrieval pipelines commonly rely on a term-based search to obtain candidate records, which are subsequently re-ranked. Some candidates are missed by this approach, e.g., due to a vocabulary mismatch. We address this issue by replacing the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle term associations. While an exact brute-force k-NN search using this similarity function is slow, we demonstrate that an approximate algorithm can be nearly two orders of magnitude faster at the expense of only a small loss in accuracy. A retrieval pipeline using an approximate k-NN search can be more effective and efficient than the term-based pipeline. This opens up new possibilities for designing effective retrieval pipelines. Our software (including data-generating code) and derivative data based on the Stack Overflow collection is available online.

This article describes a specific history of technological mediation in the circulation of popular music by examining local practices of listening to recordings in Japanese kissaten (often shortened to kissa and meaning, loosely, ‘coffeehouse’). In postwar music kissaten, Japanese listeners were socialised to recordings of foreign music through new modes of hyper-attentive listening. While jazz kissa (though famous as crucibles for radical pro-democracy politics and the explosion of modern urban cool in post-war Japanese cities) encouraged local listeners to develop musical appreciation through the stylistic classification of distant recorded sources, later experimental music kissa helped forge unique local performance scenes by disturbing received modes of generic classification in favour of ‘Noise’. I recount the emergence of a genre called ‘Noise’ in the story of a 1970s Kyoto ‘free’ kissa Drugstore, whose countercultural clientele came to represent ‘Noise’ as a new musical style in its transnational circulation during the 1990s. This ethnographic history presents the music kissa as a complicated translocal site that articulates the cultural marginality of Japanese popular music reception in an uneven global production; but which also helps to develop virtuosic experimental practices of listening through which imported recordings are recontextualised, renamed and recreated.

This document covers a library for fast similarity (k-NN)search. It describes only search methods and distances (spaces). Details about building, installing, Python bindings can be found online:https://github.com/searchivarius/nmslib/tree/v1.8/. Even though the library contains a variety of exact metric-space access methods, our main focus is on more generic and approximate search methods, in particular, on methods for non-metric spaces. NMSLIB is possibly the first library with a principled support for non-metric space searching., Comment: Methodology paper

PALB2/FANCN is a BRCA1- and BRCA2-interacting Fanconi Anemia (FA) protein crucial for key BRCA2 genome caretaker functions. Heterozygous germline mutations in PALB2 predispose to breast cancer and biallelic mutations cause FA. FA proteins play a critical role in the telomere maintenance pathway, with telomeric shortening observed in FA cells. Less is known about telomere maintenance in the heterozygous state. Here, we investigate the roles of PALB2 heterozygous mutations in genomic instability, an important carcinogenesis precursor. Patient-derived lymphoblastoid (LCL) and fibroblast (FCL) cell lines with monoallelic truncating PALB2 mutations were investigated using a combination of molecular imaging techniques including centromeric FISH, telomeric Q-FISH and spectral karyotyping (SKY). Mitomycin C and Cisplatin sensitivity was assayed via cellular metabolism of WST-1. The PALB2 c.229delT FCL showed increases in telomere counts associated with increased mean intensity compared with two wild-type FCLs generated from first-degree relatives (P =1.04E-10 and P =9.68E-15) and it showed evidence of chromosomal rearrangements. Significant differences in centromere distribution were observed in one of three PALB2 heterozygous FCLs analyzed when compared with PALB2 wild-type, BRCA1 and BRCA2 heterozygous FCLs. No significant consistently increased sensitivity to Mitomycin C or Cisplatin was observed in LCLs. Our results are suggestive of an altered centromere distribution profile and a telomere instability phenotype. Together, these may indicate critical nuclear organization defects associated with the predisposition to transformation and early stage development of PALB2-related cancers.

We overview current problems of audio retrieval and time-series subsequence matching. We discuss the usage of subsequence matching approaches in audio data processing, especially in automatic speech recognition (ASR) area and we aim at improving performance of the retrieval process. To overcome the problems known from the time-series area like the occurrence of implementation bias and data bias we present a Subsequence Matching Framework as a tool for fast prototyping, building, and testing similarity search subsequence matching applications. The framework is build on top of MESSIF (Metric Similarity Search Implementation Framework) and thus the subsequence matching algorithms can exploit advanced similarity indexes in order to significantly increase their query processing performance. To prove our concept we provide a design of query-by-example spoken term detection type of application with the usage of phonetic posteriograms and subsequence matching approach.

Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series or string of symbols, which can be seen as an input for various subsequence matching approaches. The variety of data types, specific tasks and their partial or full solutions is so wide that the choice, implementation and parametrization of a suitable solution for a given task might be complicated and time-consuming; a possibly fruitful combination of fragments from different research areas may not be obvious nor easy to realize. The leading authors of this field also mention the implementation bias that makes difficult a proper comparison of competing approaches. Therefore we present a new generic Subsequence Matching Framework (SMF) that tries to overcome the aforementioned problems by a uniform frame that simplifies and speeds up the design, development and evaluation of subsequence matching related systems. We identify several relatively separate subtasks solved differently over the literature and SMF enables to combine them in straightforward manner achieving new quality and efficiency. This framework can be used in many application domains and its components can be reused effectively. Its strictly modular architecture and openness enables also involvement of efficient solutions from different fields, for instance efficient metric-based indexes. This is an extended version of a paper published on DEXA 2012., Comment: This is an extended version of a paper published on DEXA 2012

The new field of terrorism informatics is not only multidisciplinary but also requires new approaches to processing its complex underlying collections of data. In this paper, we introduce Multi-Feature Indexing Network (MUFIN) which is able to manage and search a large class of digital data according to the metric model of similarity. It is also scalable with respect to the data volume as well as the query execution throughput. At the same time, its performance can be tuned by mapping the search structure on a needed computation infrastructure. We illustrate the capabilities of MUFIN by outlining some of its current applications. Finally, we show how MUFIN can be used to deal with many biometric data and illustrate such possibility on the face retrieval application., See also ADA562563. RTO-MP-MSG-069 Current Uses of M&S Covering Support to Operations, Human Behaviour Representation, Irregular Warfare, Defence against Terrorism and Coalition Tactical Force Integration (Utilisation actuelle M&S couvrant le soutien aux operations, la representation du comportement humain, la guerre asymetrique, la defense contre le terrorisme et l'integration d'une force tactique de coalition). Proceedings of the NATO RTO Modelling and Simulation Group Symposium held in Brussels, Belgium on 15 and 16 October 2009., The original document contains color images.

Network administration has become more difficult as the number of Internet users has grown and customer usage patterns have changed over time. Rapidly increasing subscriber bases, data intensive applications (such as streaming audio and video), heavy Web browsing, and large file downloads require significant resources and may tax existing network bandwidth. Reliability and quality of service are becoming serious issues for service providers across the country. Due to the dynamic nature of the information technology (IT) sector in general, it is difficult to predict future network usage patterns or what types of applications may be available, and how these applications may be used over time. This research presents a methodology to facilitate capacity planning and to improve the evaluation of network performance for connection-based networks using the Virginia Tech modem pool as a test bed. The abstract research question is can innovative business strategies be employed in lieu of, or in addition to, traditional management practices such as adding capacity in order to improve the performance of a dialup network? Examples of such strategies or business rules may include limiting the duration of an online session or limiting the number of times a given customer can dial into the pool in a specified time period. A complete network traffic characterization is conducted based on service time and interarrival time variables. A longitudinal analysis is performed to examine how traffic patterns have changed over time. Finally, a simulation model is utilized to examine how imposing different business rules during peak-periods of operation can reduce the blocking probability and improve the overall level-of-service. The potential contribution of this research appears to be significant based on the lack of existing literature.

Solid waste management has become an increasingly costly issue for localities throughout Virginia. The Commonwealth has established a state-mandated recycling rate of 25 percent to be achieved by July 31, 1995. The mandate, however, may not adequately consider the diversity of individual localities throughout the state. Some localities may have financial, technical, or physical difficulty in complying with the mandate. Rural localities in particular may have trouble complying with mandated rates of recycling, due to the high costs and physical difficulty of collecting, separating, transporting, and marketing recyclable waste products. The focus of local recycling efforts has typically been on minimizing recycling program costs. Little attention has been given to the household costs associated with various recycling tasks or programs, and how implicit household costs associated with recycling might affect residential participation levels. Local governments often set recycling goals or implement programs with little insight into the household costs of recycling. The household’s perception of the time, effort, inconvenience, and mess involved with recycling may influence participation levels, recovery rates, and the overall effectiveness of a program. This study examines and discusses the factors that influence the recycling behavior of rural residents. It also provides an indication as to the household perception of the implicit costs associated with various recycling tasks. Three separate recycling programs are examined to determine if household recycling behavior differs between counties, depending on the type of waste disposal and recycling system used in the county.

Since its national debut in 1987, Fresh Air with Terry Gross has been a highly acclaimed and much adored weekday magazine among public radio listeners. Each week, nearly 4.8 million people turn to Peabody Award-winning host Terry Gross for insightful conversations with the leading voices in contemporary arts and issues. The renowned program reaches a global audience, with over 620 public radio stations broadcasting Fresh Air, and 3 million podcast downloads each week. Fresh Air has broken the mold of 'talk show' by weaving together superior journalism and intimate storytelling from modern-day intellectuals, politicians and artists alike. Through probing questions and careful research, Gross's interviews are lauded for revealing a fresh perspective on cultural icons and trends. Her thorough conversations are often complemented by commentary from well-known contributors. Fresh Air is produced at WHYY-FM in Philadelphia and broadcast nationally by NPR., INT. 1:MARIAN FAYE NOVAK and DAVID NOVAK. Marian was pregnant when her husband, David, a Marine, was sent to Vietnam. Her daughter, Jeannie is now an army officer serving in the war. We talk with Marian about her new book, LONELY GIRLS WITH BURNING EYES: A Wife Recalls Her Husband's Journey Home from Vietnam, (Little, Brown) and her feelings today about her daughter in the Gulf. We also talk with David about what it's like for him to be waiting at the other end. REV. 1: Commentator MAUREEN CORRIGAN reviews a new biography on Frederick Douglas by pulitzer prize-winning author William McFeely. INT. 3:Retired Col. HOWARD LEVIE, Professor Emeritus of Law, St. Louis University. He is also Adjunct Prof. at the Naval War College. He's written a study about prisoners of war, "Prisoners of War in International Armed Conflict," (Naval War College Press, 1979). He'll talk to Terry about the history of P.O.W.'s and the laws governing their treatment. INT. 4:Terry talks with the great violinist ISAAC STERN. Last week he was performing a concert in Israel when the sirens went off signaling a missle attack. The audience put on their gas masks, and the orchestra left the stage. But STERN stayed, walking up to the edge of the stage, and continued to play for the audience.

Photo of Jim Szymanski in Cleveland State University's 1968 production of "Oh What a Lovely War."

1970 Yearbook pictures of "The Connection," a CSU Players production. Pictured left is Peter Shokalook. Pictured top right is (from left to right) June Rosebrook and Leon Fisher. Bottom right picture shows Tony Zupancic (left) and June Rosebrook (right).

69A3551747114, A key purpose of the transportation system is to provide access to critical services such as grocery stores. Maintaining food access during an emergency or other disruption is all the more important, particularly for vulnerable households. Most people in the United States rely on the use of private automobiles for grocery shopping. Thus, disruptions to road networks due to heavy precipitation, flooding, or even major maintenance and repair projects present notable threats to accessibility. Regional planning models that address food accessibility issues (not all do) typically do not consider households’ familiarity with grocery locations. However, during a disruptive event, a household’s familiarity with at least one available route to a retail grocery location becomes paramount. Identifying the roadways that are most critical to food access can help decision makers devise strategies to mitigate the risks of food insecurity for vulnerable households and populations.

The purpose of this study was to evaluate the effects of road-network representation on the application of the Network Robustness Index (NRI), using the Chittenden County Regional Transportation Model. The results are expected to improve the requirements for how a road network must be represented for an effective application of the NRI. This work was performed under Year 2 of UVM Transportation Research Center (TRC) Signature Project 1H – Network Robustness Index: A Comprehensive Spatial-Based Measure for Transportation Infrastructure Management. Funding for this work comes from the USDOT through the University Transportation Center (UTC) at the University of Vermont.

MnDOT No. 06428, In recent years, many states have experienced heavy burdens on their snow and ice control budgets. Increases in winter/spring precipitation results in increased costs to state departments of transportation (DOTs) for winter roadway maintenance materials (salt, sand, chemicals, etc.), plow operator time, equipment maintenance and replacement budgets, and fuel use. As state DOTs adjust to climate conditions that include not only more precipitation, but more severe and unpredictable weather events, it will become increasingly important to integrate the cost of roadway snow and ice control (RSIC) operations into their capital-project planning processes. The overall goal of this project was to support state DOTs’ operations & maintenance efforts by developing an automated method for quantifying the expected impact that new capital projects will have on RSIC operations. The effects of a new suburban roadway were found to be the most significant, requiring 266 vehicle-minutes of travel along with almost 40 minutes of additional service time or one additional fleet truck for each mile of new roadway. The results and findings of this research have implications for short-term funding allocations for RSIC operations staff and for long-term consideration of RSIC in the highway planning and design processes. The findings of this project provide defensible data for operations staff to advocate for increases in funding to offset the increased RSIC burden when a project is completed. The calculation tool created incorporates all of the results above into a MS Excel decision support platform, providing quick estimates of the monetary impact of a variety of major highway project types.

The purpose of this study was to evaluate the effects of road-network representation on the application of the Network Robustness Index (NRI), using the Chittenden County Regional Transportation Model. The results are expected to improve the requirements for how a road network must be represented for an effective application of the NRI. This work was performed under Year 2 of UVM Transportation Research Center (TRC) Signature Project 1H -- Network Robustness Index: A Comprehensive Spatial-Based Measure for Transportation Infrastructure Management. Funding for this work comes from the USDOT through the University Transportation Center (UTC) at the University of Vermont.