Patterns of spatial clustering of Spanish. Start-ups and the formation of entrepreneurship in the manufacturing sector

Treballs Finals del Màster de Recerca en Empresa, Facultat d'Economia i Empresa, Universitat de Barcelona. Curs: 2022-2023, Tutor: Javier Romaní FernándezEntrepreneurship is spatially unequally distributed in Spain and manufacturing entry rates have not been high in the last three years. Using data from the Spanish National Institute of Statistics (INE) and SABI databases, this study explores the factors that influence manufacturing entrepreneurship clusters in the Spanish provinces by highlighting the importance of industrial localization and urbanization factors. At the industry level, our results support the existence of a significant Marshallian effect in the manufacturing sector, as more manufacturing entrepreneurship is likely to occur where there is a concentration of upstream firms and a strong labor market. In the case of urbanized economies, population size is positively related to manufacturing entrepreneurship, while population density is negatively related to it. Based on Marshallian theory, our paper's analysis of the spatial clustering of start-ups in the Spanish manufacturing sector can be useful to local and national policy makers planning to encourage entrepreneurship

Stochastic Controlled Averaging for Federated Learning with Communication Compression

Communication compression, a technique aiming to reduce the information volume to be transmitted over the air, has gained great interests in Federated Learning (FL) for the potential of alleviating its communication overhead. However, communication compression brings forth new challenges in FL due to the interplay of compression-incurred information distortion and inherent characteristics of FL such as partial participation and data heterogeneity. Despite the recent development, the performance of compressed FL approaches has not been fully exploited. The existing approaches either cannot accommodate arbitrary data heterogeneity or partial participation, or require stringent conditions on compression. In this paper, we revisit the seminal stochastic controlled averaging method by proposing an equivalent but more efficient/simplified formulation with halved uplink communication costs. Building upon this implementation, we propose two compressed FL algorithms, SCALLION and SCAFCOM, to support unbiased and biased compression, respectively. Both the proposed methods outperform the existing compressed FL methods in terms of communication and computation complexities. Moreover, SCALLION and SCAFCOM accommodates arbitrary data heterogeneity and do not make any additional assumptions on compression errors. Experiments show that SCALLION and SCAFCOM can match the performance of corresponding full-precision FL approaches with substantially reduced uplink communication, and outperform recent compressed FL methods under the same communication budget.Comment: 45 pages, 4 figure

摩擦的失業、技術選択と国際貿易に関する一般均衡分析

Tohoku University曽道智課

ASAP-SML: An Antibody Sequence Analysis Pipeline Using Statistical Testing and Machine Learning

Antibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibody-based inhibitors is the lack of fundamental information relating sequences of antibodies to their unique properties as inhibitors. We develop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML), to identify features that distinguish one set of antibody sequences from antibody sequences in a reference set. The pipeline extracts feature fingerprints from sequences. The fingerprints represent germline, CDR canonical structure, isoelectric point and frequent positional motifs. Machine learning and statistical significance testing techniques are applied to antibody sequences and extracted feature fingerprints to identify distinguishing feature values and combinations thereof. To demonstrate how it works, we applied the pipeline on sets of antibody sequences known to bind or inhibit the activities of matrix metalloproteinases (MMPs), a family of zinc-dependent enzymes that promote cancer progression and undesired inflammation under pathological conditions, against reference datasets that do not bind or inhibit MMPs. ASAP-SML identifies features and combinations of feature values found in the MMP-targeting sets that are distinct from those in the reference sets

CQNV: A combination of coarsely quantized bitstream and neural vocoder for low rate speech coding

Recently, speech codecs based on neural networks have proven to perform better than traditional methods. However, redundancy in traditional parameter quantization is visible within the codec architecture of combining the traditional codec with the neural vocoder. In this paper, we propose a novel framework named CQNV, which combines the coarsely quantized parameters of a traditional parametric codec to reduce the bitrate with a neural vocoder to improve the quality of the decoded speech. Furthermore, we introduce a parameters processing module into the neural vocoder to enhance the application of the bitstream of traditional speech coding parameters to the neural vocoder, further improving the reconstructed speech's quality. In the experiments, both subjective and objective evaluations demonstrate the effectiveness of the proposed CQNV framework. Specifically, our proposed method can achieve higher quality reconstructed speech at 1.1 kbps than Lyra and Encodec at 3 kbps.Comment: Accepted by INTERSPEECH 202

Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks

Improved analysis of inorganic coal properties based on near-infrared reflectance spectroscopy

A novel method is proposed to improve the analysis accuracy of inorganic properties by adding organic information.</p

Herpes Simplex Virus Type 2 Infection-Induced Expression of CXCR3 Ligands Promotes CD4(+) T Cell Migration and Is Regulated by the Viral Immediate-Early Protein ICP4

HSV-2 infection-induced CXCR3 ligands are important for the recruitment of virus-specific CD8+ T cells, but their impact on CD4+ T cell trafficking remains to be further determined. Given that recruitment of CD4+ T cells to infection areas may be one of the mechanisms that account for HSV-2 infection-mediated enhancement of HIV-1 sexual transmission, here we investigated the functionality of HSV-2 infection-induced CXCR3 ligands CXCL9, CXCL10, and CXCL11 in vivo and in vitro, and determined the viral components responsive for such induction and the underlying mechanisms. We first found that the expression of CXCR3 ligands CXCL9, CXCL10, and CXCL11 was increased in mice following vaginal challenge with HSV-2, while CXCL9 played a predominant role in the recruitment of CD4+ T cells to the vaginal foci of infected mice. HSV-2 infection also induced the production of CXCL9, CXCL10, and CXCL11 in human cervical epithelial cells. Of note, although HSV-2 induced the expression of all the three CXCR3 ligands, the induced CXCL9 appeared to play a predominant role in promoting CD4+ T cell migration, reflecting that the concentrations of CXCL10 and CXCL11 required for CD4+ T cell migration are higher than that of CXCL9. We further revealed that, ICP4, an immediate-early protein of HSV-2, is crucial in promoting CXCR3 ligand expression through the activation of p38 MAPK pathway. Mechanistically, ICP4 binds to corresponding promoters of CXCR3 ligands via interacting with the TATA binding protein (TBP), resulting in the transcriptional activation of the corresponding promoters. Taken together, our study highlights HSV-2 ICP4 as a vital viral protein in promoting CXCR3 ligand expression and CXCL9 as the key induced chemokine in mediating CD4+ T cell migration. Findings in this study have shed light on HSV-2 induced leukocyte recruitment which may be important for understanding HSV-2 infection-enhanced HIV-1 sexual transmission and the development of intervention strategies

Photon Energy-Dependent Ultrafast Exciton Transfer in Chlorosomes of Chlorobium tepidum and the Role of Supramolecular Dynamics

The antenna complex of green sulfur bacteria, the chlorosome, is one of the most efficient supramolecular systems for efficient long-range exciton transfer in nature. Femtosecond transient absorption experiments provide new insight into how vibrationally induced quantum overlap between exciton states supports highly efficient long-range exciton transfer in the chlorosome of Chlorobium tepidum. Our work shows that excitation energy is delocalized over the chlorosome in &lt;1 ps at room temperature. The following exciton transfer to the baseplate occurs in ∼3 to 5 ps, in line with earlier work also performed at room temperature, but significantly faster than at the cryogenic temperatures used in previous studies. This difference can be attributed to the increased vibrational motion at room temperature. We observe a so far unknown impact of the excitation photon energy on the efficiency of this process. This dependency can be assigned to distinct optical domains due to structural disorder, combined with an exciton trapping channel competing with exciton transfer toward the baseplate. An oscillatory transient signal damped in &lt;1 ps has the highest intensity in the case of the most efficient exciton transfer to the baseplate. These results agree well with an earlier computational finding of exciton transfer driven by low-frequency rotational motion of molecules in the chlorosome. Such an exciton transfer process belongs to the quantum coherent regime, for which the Förster theory for intermolecular exciton transfer does not apply. Our work hence strongly indicates that structural flexibility is important for efficient long-range exciton transfer in chlorosomes

Deep Brain Stimulation-Induced Transient Effects in the Habenula

The habenula, located in the epithalamus, has been implicated in various psychiatric disorders including mood disorders and schizophrenia. This study explored the transient effects of deep brain stimulation in the habenula. Each of the four patients (two with bipolar disorder and two with schizophrenia) was tested with eight deep brain stimulation contacts. Patients were examined via transient electrical stimulation 1 month after deep brain stimulation surgery. The pulse width was 60 μs and the voltage ranged from 0 V to a maximum of 10 V, increasing in increments of 1 V. Each patient received stimulation at two frequencies, 60 and 135 Hz. A total of 221 out of 385 active trials elicited stimulation-induced effects. The three most common transient effects were numbness, heart rate changes, and pain. The incidence of numbness, heart rate changes, pain, and involuntary movements increased with the increase in stimulation voltage. Through contralateral stimulation, numbness was triggered in all parts of the body except the scalp. The obtained stimulus-response maps suggested a possible somatosensory organization of the habenula