Curling: Content-ubiquitous resolution and delivery infrastructure for next-generation services

CURLING, a Content-Ubiquitous Resolution and Delivery Infrastructure for Next Generation Services, aims to enable a future content-centric Internet that will overcome the current intrinsic constraints by efficiently diffusing media content of massive scale. It entails a holistic approach, supporting content manipulation capabilities that encompass the entire content life cycle, from content publication to content resolution and, finally, to content delivery. CURLING provides to both content providers and customers high flexibility in expressing their location preferences when publishing and requesting content, respectively, thanks to the proposed scoping and filtering functions. Content manipulation operations can be driven by a variety of factors, including business relationships between ISPs, local ISP policies, and specific content provider and customer preferences. Content resolution is also natively coupled with optimized content routing techniques that enable efficient unicast and multicast-based content delivery across the global Internet

A Hybrid Time-Scaling Transformation for Time-Delay Optimal Control Problems

In this paper, we consider a class of nonlinear time-delay optimal control problems with canonical equality and inequality constraints. We propose a new computational approach, which combines the control parameterization technique with a hybrid time-scaling strategy, for solving this class of optimal control problems. The proposed approach involves approximating the control variables by piecewise constant functions, whose heights and switching times are decision variables to be optimized. Then, the resulting problem with varying switching times is transformed, via a new hybrid time-scaling strategy, into an equivalent problem with fixed switching times, which is much preferred for numerical computation. Our new time-scaling strategy is hybrid in the sense that it is related to two coupled time-delay systems—one defined on the original time scale, in which the switching times are variable, the other defined on the new time scale, in which the switching times are fixed. This is different from the conventional time-scaling transformation widely used in the literature, which is not applicable to systems with time-delays. To demonstrate the effectiveness of the proposed approach, we solve four numerical examples. The results show that the costs obtained by our new approach are lower, when compared with those obtained by existing optimal control methods

Massive End-to-end Models for Short Search Queries

In this work, we investigate two popular end-to-end automatic speech recognition (ASR) models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN-T), for offline recognition of voice search queries, with up to 2B model parameters. The encoders of our models use the neural architecture of Google's universal speech model (USM), with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference. We perform extensive studies on vocabulary size, time reduction strategy, and its generalization performance on long-form test sets. Despite the speculation that, as the model size increases, CTC can be as good as RNN-T which builds label dependency into the prediction, we observe that a 900M RNN-T clearly outperforms a 1.8B CTC and is more tolerant to severe time reduction, although the WER gap can be largely removed by LM shallow fusion

An Online RFID Localization in the Manufacturing Shopfloor

{Radio Frequency Identification technology has gained popularity for cheap and easy deployment. In the realm of manufacturing shopfloor, it can be used to track the location of manufacturing objects to achieve better efficiency. The underlying challenge of localization lies in the non-stationary characteristics of manufacturing shopfloor which calls for an adaptive life-long learning strategy in order to arrive at accurate localization results. This paper presents an evolving model based on a novel evolving intelligent system, namely evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an interval type-2 quantum fuzzy set with uncertain jump positions. The quantum fuzzy set possesses a graded membership degree which enables better identification of overlaps between classes. The eT2QFNN works fully in the evolving mode where all parameters including the number of rules are automatically adjusted and generated on the fly. The parameter adjustment scenario relies on decoupled extended Kalman filter method. Our numerical study shows that eT2QFNN is able to deliver comparable accuracy compared to state-of-the-art algorithms

Fluorescent Gold Nanoprobes for the Sensitive and Selective Detection for Hg2+

A simple, cost-effective yet rapid and sensitive sensor for on-site and real-time Hg2+ detection based on bovine serum albumin functionalized fluorescent gold nanoparticles as novel and environmentally friendly fluorescent probes was developed. Using this probe, aqueous Hg2+ can be detected at 0.1 nM in a facile way based on fluorescence quenching. This probe was also applied to determine the Hg2+ in the lake samples, and the results demonstrate low interference and high sensitivity

Energy expenditure during flight in relation to body mass: effects of natural increases in mass and artificial load in Rose Coloured Starlings

Rose Coloured Starlings (Sturnus roseus) flew repeatedly for several hours in a wind tunnel while undergoing spontaneous variation in body mass. The treatments were as follows: flying unrestrained (U), with a control harness of 1.2% of their body mass (C), or with a harness of 7.4% of their body mass, which was either applied immediately before the flight (LS) or at least 9 days in advance (LL). Energy expenditure during flight (ef in W) was measured with the Doubly Labelled Water method. Flight costs in LS and LL were not significantly different and therefore were pooled (L). The harness itself did not affect ef, i.e. U and C flights were not different. ef was allometrically related with body mass m (in g). The slopes were not significantly different between the treatments, but ef was increased by 5.4% in L compared to C flights (log10(ef) = 0.050 + 0.47 × log10(m) for C, and log10(ef) = 0.073 + 0.47 × log10(m) for L). The difference in ef between C, LS and LL was best explained by taking the transported mass mtransp (in g) instead of m into account (log10(ef) = −0.08 + 0.54 × log10(mtransp)). Flight costs increased to a lesser extent than expected from interspecific allometric comparison or aerodynamic theory, regardless of whether the increase in mass occurred naturally or artificially. We did not observe an effect of treatment on breast muscle size and wingbeat frequency. We propose that the relatively low costs at a high mass are rather a consequence of immediate adjustments in physiology and/or flight behaviour than of long-term adaptations

Molecular and Electrophysiological Characterization of GFP-Expressing CA1 Interneurons in GAD65-GFP Mice

The use of transgenic mice in which subtypes of neurons are labeled with a fluorescent protein has greatly facilitated modern neuroscience research. GAD65-GFP mice, which have GABAergic interneurons labeled with GFP, are widely used in many research laboratories, although the properties of the labeled cells have not been studied in detail. Here we investigate these cells in the hippocampal area CA1 and show that they constitute ∼20% of interneurons in this area. The majority of them expresses either reelin (70±2%) or vasoactive intestinal peptide (VIP; 15±2%), while expression of parvalbumin and somatostatin is virtually absent. This strongly suggests they originate from the caudal, and not the medial, ganglionic eminence. GFP-labeled interneurons can be subdivided according to the (partially overlapping) expression of neuropeptide Y (42±3%), cholecystokinin (25±3%), calbindin (20±2%) or calretinin (20±2%). Most of these subtypes (with the exception of calretinin-expressing interneurons) target the dendrites of CA1 pyramidal cells. GFP-labeled interneurons mostly show delayed onset of firing around threshold, and regular firing with moderate frequency adaptation at more depolarized potentials

The Mechanism for RNA Recognition by ANTAR Regulators of Gene Expression

ANTAR proteins are widespread bacterial regulatory proteins that have RNA–binding output domains and utilize antitermination to control gene expression at the post-initiation level. An ANTAR protein, EutV, regulates the ethanolamine-utilization genes (eut) in Enterococcus faecalis. Using this system, we present genetic and biochemical evidence of a general mechanism of antitermination used by ANTARs, including details of the antiterminator structure. The novel antiterminator structure consists of two small hairpins with highly conserved terminal loop residues, both features being essential for successful antitermination. The ANTAR protein dimerizes and associates with its substrate RNA in response to signal-induced phosphorylation. Furthermore, bioinformatic searches using this conserved antiterminator motif identified many new ANTAR target RNAs in phylogenetically diverse bacterial species, some comprising complex regulons. Despite the unrelatedness of the species in which they are found, the majority of the ANTAR–associated genes are thematically related to nitrogen management. These data suggest that the central tenets for gene regulation by ANTAR antitermination occur widely in nature to specifically control nitrogen metabolism