Separating NOF communication complexity classes RP and NP

We provide a non-explicit separation of the number-on-forehead communication complexity classes RP and NP when the number of players is up to \delta log(n) for any \delta<1. Recent lower bounds on Set-Disjointness [LS08,CA08] provide an explicit separation between these classes when the number of players is only up to o(loglog(n))

Web-Based Interactive Social Media Visual Analytics

Real-time social media platforms enable quick information broadcasting and response during disasters and emergencies. Analyzing the massive amount of generated data to understand the human behavior requires data collection and acquisition, parsing, filtering, augmentation, processing, and representation. Visual analytics approaches allow decision makers to observe trends and abnormalities, correlate them with other variables and gain invaluable insight into these situations. In this paper, we propose a set of visual analytic tools for analyzing and understanding real-time social media data in times of crisis and emergency situations. First, we model the degree of risk of individuals’ movement based on evacuation zones and post-event damaged areas. Identified movement patterns are extracted using clustering algorithms and represented in a visual and interactive manner. We use Twitter data posted in New York City during Hurricane Sandy in 2012 to demonstrate the efficacy of our approach. Second, we extend the Social Media Analytics and Reporting Toolkit (SMART) to supporting the spatial clustering analysis and temporal visualization. Our work would help first responders enhance awareness and understand human behavior in times of emergency, improving future events’ times of response and the ability to predict the human reaction. Our findings prove that today’s high-resolution geo-located social media platforms can enable new types of human behavior analysis and comprehension, helping decision makers take advantage of social media

Development of a National Anthropogenic Heating Database with an Extrapolation for International Cities

Given increasing utility of numerical models to examine urban impacts on meteorology and climate, there exists an urgent need for accurate representation of seasonally and diurnally varying anthropogenic heating data, an important component of the urban energy budget for cities across the world. Incorporation of anthropogenic heating data as inputs to existing climate modeling systems has direct societal implications ranging from improved prediction of energy demand to health assessment, but such data are lacking for most cities. To address this deficiency we have applied a standardized procedure to develop a national database of seasonally and diurnally varying anthropogenic heating profiles for 61 of the largest cities in the United Stated (U.S.). Recognizing the importance of spatial scale, the anthropogenic heating database developed includes the city scale and the accompanying greater metropolitan area. Our analysis reveals that a single profile function can adequately represent anthropogenic heating during summer but two profile functions are required in winter, one for warm climate cities and another for cold climate cities. On average, although anthropogenic heating is 40% larger in winter than summer, the electricity sector contribution peaks during summer and is smallest in winter. Because such data are similarly required for international cities where urban climate assessments are also ongoing, we have made a simple adjustment accounting for different international energy consumption rates relative to the U.S. to generate seasonally and diurnally varying anthropogenic heating profiles for a range of global cities. The methodological approach presented here is flexible and straightforwardly applicable to cities not modeled because of presently unavailable data. Because of the anticipated increase in global urban populations for many decades to come, characterizing this fundamental aspect of the urban environment – anthropogenic heating – is an essential element toward continued progress in urban climate assessment

Rapid and sensitive analytical method for the determination of amoxicillin and related compounds in water meeting the requirements of the European union watch list

The presence of antibiotics in the aquatic environment is becoming one of the main research focus of scientists and policy makers. Proof of that is the inclusion of four antibiotics, amongst which is amoxicillin, in the EU Watch List (WL) (Decision 2020/1161/EU)) of substances for water monitoring. The accurate quantification of amoxicillin in water at the sub-ppb levels required by the WL is troublesome due to its physicochemical properties. In this work, the analytical challenges related to the determination of amoxicillin, and six related penicillins (ampicillin, cloxacillin, dicloxacillin, penicillin G, penicillin V and oxacillin), have been carefully addressed, including sample treatment, sample stability, chromatographic analysis and mass spectrometric detection by triple quadrupole. Given the low recoveries obtained using different solid-phase extraction cartridges, we applied the direct injection of water samples using a reversed-phase chromatographic column that allowed working with 100% aqueous mobile phase. Matrix effects were evaluated and corrected using the isotopically labelled internal standard or correction factors based on signal suppression observed in the analysis of spiked samples. The methodology developed was satisfactorily validated at 50 and 500 ng L − 1 for the seven penicillins studied, and it was applied to different types of water matrices, revealing the presence of ampicillin in one surface water sample and cloxacillin in three effluent wastewater samples.Funding for open access charge: CRUE-Universitat Jaume

Incremental View Maintenance For Collection Programming

In the context of incremental view maintenance (IVM), delta query derivation is an essential technique for speeding up the processing of large, dynamic datasets. The goal is to generate delta queries that, given a small change in the input, can update the materialized view more efficiently than via recomputation. In this work we propose the first solution for the efficient incrementalization of positive nested relational calculus (NRC+) on bags (with integer multiplicities). More precisely, we model the cost of NRC+ operators and classify queries as efficiently incrementalizable if their delta has a strictly lower cost than full re-evaluation. Then, we identify IncNRC+; a large fragment of NRC+ that is efficiently incrementalizable and we provide a semantics-preserving translation that takes any NRC+ query to a collection of IncNRC+ queries. Furthermore, we prove that incremental maintenance for NRC+ is within the complexity class NC0 and we showcase how recursive IVM, a technique that has provided significant speedups over traditional IVM in the case of flat queries [25], can also be applied to IncNRC+.Comment: 24 pages (12 pages plus appendix

Dediščina izjemnih osebnosti kot potencial za razvoj kulturnega turizma v Romuniji

This article investigates how domestic tourists perceive the possibilities of boosting cultural heritage tourism in Romania, through the capitalization of national genius personalities. The methodology is based on the survey method. The research identified 22 geniuses, largely represented in national culture, and acknowledged and demanded by the market. The vast majority have been converted into tourist attractions, however those of international visibility are missing or are underrepresented in Romanian heritage tourism. An increased focus on geniuses would be highly valued by tourists and could reinforce the value of cultural heritage, consequently, boosting tourism resources. This would lead to multiple and sustainable benefits for destinations’ development, but certain infrastructure and management gaps would need to be filled.Avtorji v članku proučujejo mnenja domačih turistov o možnostih spodbujanja razvoja kulturnega turizma v Romuniji na podlagi izjemnih osebnosti iz romunske kulturne zgodovine. Uporabljena metodologija temelji na anketi, v kateri so vprašani izpostavili 22 romunskih kulturnih osebnosti, prepoznanih na trgu. Večina je bila preobražena v turistične zanimivosti, pri čemer pa v romunskem dediščinskem turizmu manjkajo mednarodno prepoznavne osebnosti ali so te slabo zastopane. Večji poudarek na tovrstnih osebnostih bi turisti zelo dobro sprejeli, hkrati bi se s tem povečala vrednost kulturne dediščine, kar bi posledično spodbudilo razvoj novih turističnih virov. Navedeno bi imelo različne trajnostne koristi za razvoj destinacij, treba pa bi bilo zapolniti nekatere vrzeli v infrastrukturi in upravljanju

Intracellular Doppler Signatures of Platinum Sensitivity Captured by Biodynamic Profiling in Ovarian Xenografts

Three-dimensional (3D) tissue cultures are replacing conventional two-dimensional (2D) cultures for applications in cancer drug development. However, direct comparisons of in vitro 3D models relative to in vivo models derived from the same cell lines have not been reported because of the lack of sensitive optical probes that can extract high-content information from deep inside living tissue. Here we report the use of biodynamic imaging (BDI) to measure response to platinum in 3D living tissue. BDI combines low-coherence digital holography with intracellular Doppler spectroscopy to study tumor drug response. Human ovarian cancer cell lines were grown either in vitro as 3D multicellular monoculture spheroids or as xenografts in nude mice. Fragments of xenografts grown in vivo in nude mice from a platinum-sensitive human ovarian cell line showed rapid and dramatic signatures of induced cell death when exposed to platinum ex vivo, while the corresponding 3D multicellular spheroids grown in vitro showed negligible response. The differences in drug response between in vivo and in vitro growth have important implications for predicting chemotherapeutic response using tumor biopsies from patients or patient-derived xenografts

Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM). Existing work has combined these in simple "retrieve-then-read" pipelines in which the RM retrieves passages that are inserted into the LM prompt. To begin to fully realize the potential of frozen LMs and RMs, we propose Demonstrate-Search-Predict (DSP), a framework that relies on passing natural language texts in sophisticated pipelines between an LM and an RM. DSP can express high-level programs that bootstrap pipeline-aware demonstrations, search for relevant passages, and generate grounded predictions, systematically breaking down problems into small transformations that the LM and RM can handle more reliably. We have written novel DSP programs for answering questions in open-domain, multi-hop, and conversational settings, establishing in early evaluations new state-of-the-art in-context learning results and delivering 37-200%, 8-40%, and 80-290% relative gains against vanilla LMs, a standard retrieve-then-read pipeline, and a contemporaneous self-ask pipeline, respectively