META Group

The European Union aims to achieve climate neutrality by 2050, prioritizing energy efficiency particularly in the building sector. Despite significant policies, such as the EU Green Deal and Renovation Wave initiative, the rate of deep energy renovations remains insufficient, with only 0.2% annually versus the 3% required. Multiple barriers hinder the progress of deep energy renovations (DERs), including fragmentation among stakeholders, the limited coordination of RDI (Research, Development, and Innovation) efforts, and a lack of systemic approaches. The objective of this paper is to illustrate a holistic methodological approach for enhancing the DER market uptake based on transition pathways theory (TPT) and is designed to drive structural evolution in DER markets aimed at overcoming their main current constraints. To this end, five key transition pathways are outlined—namely institutionalization, clusterization, capitalization, digitalization, and exploitation—and are conceived for fostering coordination, integration, promotion, and efficient scaling of innovations along the whole DER value chain. This approach was tested in seven EU building market ecosystems under the H2020 re-MODULEES project, aimed at developing a market activation platform conceived as a digital enabler for next-generation One-Stop Shops (OSSs). This project yielded practical evidence on the potentiality of the TPT frame to strengthen and empower local ecosystems through stakeholders’ engagement and cooperation. The findings suggest that the TPT-based approach tested in re-MODULEES can effectively address structural challenges in diverse DER renovation markets, and for this reason, it may be also tested and extended in other ecosystems across Europe in order to be validated as a strategic approach at the EU level for facilitating the transition to low-carbon buildings.

Human visual abilities vary dramatically with location. For example, grating acuity is several hundred times higher at the fovea than in the far periphery (Frisen and Glansholm, Invest. Ophthalmol. Vis. Sci., 1975). This decline in performance with eccentricity generalizes to most visual tasks. A parsimonious hypothesis is that the representation of any visual feature is accompanied by blurring (spatial pooling) that increases with eccentricity, where the spatial scale of the blur depends on the choice of image feature. Here, we built models of two types of visual representations, luminance and spectral energy. Both models average the corresponding feature in pooling windows whose diameters grow linearly with eccentricity. We then synthesized a set of "model metamers", physically distinct images with matched model responses, which served as stimuli in psychophysical experiments to measure the window size for which human and model discrimination abilities match. These stimuli were much larger than those previously used (Freeman and Simoncelli, Nat Neurosci, 2011; Wallis et al., eLife, 2019), subtending 53.6 by 42.2 degrees of visual angle. We found the critical scaling for the luminance model was about four times smaller than that of the energy model. Further, consistent with earlier studies (Wallis et al., eLife, 2019; Deza et al., ICLR, 2019), we find smaller critical scaling when discriminating a synthesized image from a natural image than when discriminating two synthesized images. We also demonstrate that initializing metamer synthesis with a natural image (as opposed to a sample of white noise) decreases critical scaling when discriminating two synthesized images, but not when discriminating a synthesized from a natural image. We provide a coherent explanation of all these perceptual results in terms of interactions between the choice of local features, the synthesis algorithm, and the initialization.

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. This study presents a simple yet effective sparse self-attention for speech enhancement, called ripple attention, which simultaneously performs fine- and coarse-grained modeling for local and global dependencies, respectively. Specifically, we employ local band attention to enable each frame to attend to its closest neighbor frames in a window at fine granularity, while employing dilated attention outside the window to model the global dependencies at a coarse granularity. We evaluate the efficacy of our ripple attention for speech enhancement on two commonly used training objectives. Extensive experimental results consistently confirm the superior performance of the ripple attention design over standard full self-attention, blockwise attention, and dual-path attention (Sep-Former) in terms of speech quality and intelligibility.

Estudos da Organização Internacional do Trabalho (2020) indicam que a participação das mulheres no mercado de trabalho global tenha crescido sobremaneira nas últimas décadas, passando de uma a cada cinco para uma a cada duas vagas. A equidade de gênero, no entanto, ainda é uma meta a ser alcançada (OIT, 2020). Este estudo, de natureza aplicada, com interpretação qualitativa dos dados, investiga como o tema “Equidade de gênero e empoderamento feminino” tem sido incorporado na vida de trabalhadoras e propõe uma intervenção a partir dos resultados obtidos. Os dados foram gerados a partir de questionários e oficinas com as participantes. Os resultados indicam que o tema “Gênero e empoderamento feminino” reverbera no cotidiano e nas oportunidades profissionais das participantes, que a intervenção gerou conscientização sobre essas questões e uma série de ações, visando transformar a realidade das participantes do estudo.

Fully exploiting the learning capacity of neural networks requires overparameterized dense networks. On the other side, directly training sparse neural networks typically results in unsatisfactory performance. Lottery Ticket Hypothesis (LTH) provides a novel view to investigate sparse network training and maintain its capacity. Concretely, it claims there exist winning tickets from a randomly initialized network found by iterative magnitude pruning and preserving promising trainability (or we say being in trainable condition). In this work, we regard the winning ticket from LTH as the subnetwork which is in trainable condition and its performance as our benchmark, then go from a complementary direction to articulate the Dual Lottery Ticket Hypothesis (DLTH): Randomly selected subnetworks from a randomly initialized dense network can be transformed into a trainable condition and achieve admirable performance compared with LTH -- random tickets in a given lottery pool can be transformed into winning tickets. Specifically, by using uniform-randomly selected subnetworks to represent the general cases, we propose a simple sparse network training strategy, Random Sparse Network Transformation (RST), to substantiate our DLTH. Concretely, we introduce a regularization term to borrow learning capacity and realize information extrusion from the weights which will be masked. After finishing the transformation for the randomly selected subnetworks, we conduct the regular finetuning to evaluate the model using fair comparisons with LTH and other strong baselines. Extensive experiments on several public datasets and comparisons with competitive approaches validate our DLTH as well as the effectiveness of the proposed model RST. Our work is expected to pave a way for inspiring new research directions of sparse network training in the future. Our code is available at https://github.com/yueb17/DLTH.

Continuous wound infusion (CWI) may protect from inflammation, hyperalgesia and persistent pain. Current local anesthetics display suboptimal pharmacokinetic profile during CWI; chloroprocaine (CP) has ideal characteristics, but has never been tested for CWI. We performed an animal study to investigate the pharmacokinetic profile and anti-inflammatory effect of CP during CWI. A total of 14 piglets received an infusion catheter after pararectal laparotomy and were randomly allocated to one of three groups: 5 mL/h infusion of saline (group A), CP 1.5% (group B) and CP 0.5% (group C). Blood sampling was performed to assess absorption and systemic inflammation at 0, 3, 6, 12, 24, 48, 72, 96, 102 and 108 hours. The wound and contralateral healthy abdominal wall were sampled for histological analyses. Absorption of CP from the site of infusion, evaluated as the plasmatic concentrations of CP and its metabolite, 4-amino-2-chlorobenzoic acid (CABA), showed a peak during the first 6 hours, but both CP and its metabolite rapidly disappeared after stopping CP infusion. Local inflammation was reduced in groups B and C (CP-treated p < 0.001), in a CP dose-dependent fashion. While CP inhibited in a dose-dependent manner pig mononuclear cells (MNCs) in vitro proliferation to a polyclonal activator, no effect on systemic cytokines’ concentrations or on ex vivo monocytes’ responsiveness was observed, suggesting the lack of systemic effects, in line with the very short half-life of CP in plasma. CP showed a very good profile for use in CWI, with dose-dependent local anti-inflammatory effects, limited absorption and rapid clearance from the bloodstream upon discontinuation. No cytotoxicity or side effects were observed. CP, therefore, may represent an optimal choice for clinical CWI, adaptable to each patient’s need, and protective on wound inflammatory response (and hyperalgesia) after surgery.

Background. Intraperitoneal nebulization of ropivacaine reduces postoperative pain and morphine consumption after laparoscopic surgery. The aim of this multicenter double-blind randomized controlled trial was to assess the efficacy of different doses and dose-related absorption of ropivacaine when nebulized in the peritoneal cavity during laparoscopic cholecystectomy. Methods. Patients were randomized to receive 50, 100, or 150 mg of ropivacaine 1% by peritoneal nebulization through a nebulizer. Morphine consumption, pain intensity in the abdomen, wound and shoulder, time to unassisted ambulation, discharge time, and adverse effects were collected during the first 48 hours after surgery. The pharmacokinetics of ropivacaine was evaluated using high performance liquid chromatography. Results. Nebulization of 50 mg of ropivacaine had the same effect of 100 or 150 mg in terms of postoperative morphine consumption, shoulder pain, postoperative nausea and vomiting, activity resumption, and hospital discharge timing (>0.05). Plasma concentrations did not reach toxic levels in any patient, and no significant differences were observed between groups (P > 0.05). Conclusions. There is no enhancement in analgesic efficacy with higher doses of nebulized ropivacaine during laparoscopic cholecystectomy. When administered with a microvibration-based aerosol humidification system, the pharmacokinetics of ropivacaine is constant and maintains an adequate safety profile for each dosage tested.

Abstract In the wake of acquisitions, new hiring, and a shift from a functional to a customer channel structure, the company pulled its best and brightest talent into practice centers (PCs) to share knowledge, transfer best practices, standardize policies and practices across the channels, drive key initiatives, and cement collaborative relationships. The authors present the business case for communities of practice; describe LPL's vision and process for implementing its PCs, including strategies for enhancing success and avoiding potential pitfalls; and highlight the PCs' accomplishments. They also share how the PCs facilitated the company's response to the 2008–2009 financial crisis. © 2010 Wiley Periodicals, Inc.