A Practical Scheme for Frequency Offset Estimation in MIMO-OFDM Systems

This paper deals with training-assisted carrier frequency offset (CFO) estimation in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The exact maximum likelihood (ML) solution to this problem is computationally demanding as it involves a line search over the CFO uncertainty range. To reduce the system complexity, we divide the CFO into an integer part plus a fractional part and select the pilot subcarriers such that the training sequences have a repetitive structure in the time domain. In this way, the fractional CFO is efficiently computed through a correlation-based approach, while ML methods are employed to estimate the integer CFO. Simulations indicate that the proposed scheme is superior to the existing alternatives in terms of both estimation accuracy and processing load

Power electronics based on wide-bandgap semiconductors: opportunities and challenges

The expansion of the electric vehicle market is driving the request for efficient and reliable power electronic systems for electric energy conversion and processing. The efficiency, size, and cost of a power system is strongly related to the performance of power semiconductor devices, where massive industrial investments and intense research efforts are being devoted to new wide bandgap (WBG) semiconductors, such as silicon carbide (SiC) and gallium nitride (GaN). The electrical and thermal properties of SiC and GaN enable the fabrication of semiconductor power devices with performance well beyond the limits of silicon. However, a massive migration of the power electronics industry towards WBG materials can be obtained only once the corresponding fabrication technology reaches a sufficient maturity and a competitive cost. In this paper, we present a perspective of power electronics based on WBG semiconductors, from fundamental material characteristics of SiC and GaN to their potential impacts on the power semiconductor device market. Some application cases are also presented, with specific benchmarks against a corresponding implementation realized with silicon devices, focusing on both achievable performance and system cost

The Neurospora crassa carotenoid biosynthetic gene (albino 3) reveals highly conserved regions among prenyltransferases.

In the filamentous fungus Neurospora crassa the biosynthesis of carotenoids is regulated by blue light. Here we report the characterization of the albino-3 (al-3) gene of N. crassa, which encodes the carotenoid biosynthetic enzyme geranylgeranyl-pyrophosphate synthetase. This is the first geranylgeranyl-pyrophosphate synthetase gene isolated. Nucleotide sequence comparison of al-3 genomic and cDNA clones revealed that the al-3 gene is not interrupted by introns. Transcription of the al-3 gene has been examined in dark-grown and light-induced mycelia. The analysis revealed that the al-3 gene is not expressed in the dark and that its transcription is induced by blue light (Nelson, M. A., Morelli, G., Carattoli, A., Romano, N., and Macino, G. (1989) Mol. Cell. Biol. 9, 1271-1276). The al-3 gene encodes a polypeptide of 428 amino acids. Comparison of the deduced amino acid sequence of al-3 with the sequences of prenyltransferases of other species, from bacteria to humans, showed three highly conserved homologous regions. These homologous regions may be involved in the formation of the catalytic site of the prenyltransferases

Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing

Fashion illustration is used by designers to communicate their vision and to bring the design idea from conceptualization to realization, showing how clothes interact with the human body. In this context, computer vision can thus be used to improve the fashion design process. Differently from previous works that mainly focused on the virtual try-on of garments, we propose the task of multimodal-conditioned fashion image editing, guiding the generation of human-centric fashion images by following multimodal prompts, such as text, human body poses, and garment sketches. We tackle this problem by proposing a new architecture based on latent diffusion models, an approach that has not been used before in the fashion domain. Given the lack of existing datasets suitable for the task, we also extend two existing fashion datasets, namely Dress Code and VITON-HD, with multimodal annotations collected in a semi-automatic manner. Experimental results on these new datasets demonstrate the effectiveness of our proposal, both in terms of realism and coherence with the given multimodal inputs. Source code and collected multimodal annotations will be publicly released at: https://github.com/aimagelab/multimodal-garment-designer

LaDI-VTON: Latent Diffusion Textual-Inversion Enhanced Virtual Try-On

The rapidly evolving fields of e-commerce and metaverse continue to seek innovative approaches to enhance the consumer experience. At the same time, recent advancements in the development of diffusion models have enabled generative networks to create remarkably realistic images. In this context, image-based virtual try-on, which consists in generating a novel image of a target model wearing a given in-shop garment, has yet to capitalize on the potential of these powerful generative solutions. This work introduces LaDI-VTON, the first Latent Diffusion textual Inversion-enhanced model for the Virtual Try-ON task. The proposed architecture relies on a latent diffusion model extended with a novel additional autoencoder module that exploits learnable skip connections to enhance the generation process preserving the model's characteristics. To effectively maintain the texture and details of the in-shop garment, we propose a textual inversion component that can map the visual features of the garment to the CLIP token embedding space and thus generate a set of pseudo-word token embeddings capable of conditioning the generation process. Experimental results on Dress Code and VITON-HD datasets demonstrate that our approach outperforms the competitors by a consistent margin, achieving a significant milestone for the task. Source code and trained models will be publicly released at: https://github.com/miccunifi/ladi-vton

OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data

The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements. In the context of automatic tagging classification and multimodal retrieval, prior works either defined a low generalizable supervised learning approach or more reusable CLIP-based techniques while, however, training on closed source data. In this work, we propose OpenFashionCLIP, a vision-and-language contrastive learning method that only adopts open-source fashion data stemming from diverse domains, and characterized by varying degrees of specificity. Our approach is extensively validated across several tasks and benchmarks, and experimental results highlight a significant out-of-domain generalization capability and consistent improvements over state-of-the-art methods both in terms of accuracy and recall. Source code and trained models are publicly available at: https://github.com/aimagelab/open-fashion-clip.Comment: International Conference on Image Analysis and Processing (ICIAP) 202

OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data

The inexorable growth of online shopping and e-commerce demands scalable and robust machine learning-based solutions to accommodate customer requirements. In the context of automatic tagging classification and multimodal retrieval, prior works either defined a low generalizable supervised learning approach or more reusable CLIP-based techniques while, however, training on closed source data. In this work, we propose OpenFashionCLIP, a vision-and-language contrastive learning method that only adopts open-source fashion data stemming from diverse domains, and characterized by varying degrees of specificity. Our approach is extensively validated across several tasks and benchmarks, and experimental results highlight a significant out-of-domain generalization capability and consistent improvements over state-of-the-art methods both in terms of accuracy and recall. Source code and trained models are publicly available at: https://github.com/aimagelab/open-fashion-clip

Intensive Rehabilitation Treatment in Parkinsonian Patients with Dyskinesias: A Preliminary Study with 6-Month Followup

A major adverse effect of levodopa therapy is the development of dyskinesia, which affects 30–40% of chronically treated Parkinsonian patients. We hypothesized that our rehabilitation protocol might allow a reduction in levodopa dosage without worsening motor performances, thus reducing frequency and severity of dyskinesias. Ten Parkinsonian patients underwent a 4-week intensive rehabilitation treatment (IRT). Patients were evaluated at baseline, at the end of the rehabilitation treatment and at 6-month followup. Outcome measures were the Unified Parkinson's Disease Rating Scale Sections II, III, and IV (UPDRS II, III, IV) and the Abnormal Involuntary Movement Scale (AIMS). At the end of the IRT, levodopa dosage was significantly reduced (P = 0.0035), passing from 1016 ± 327 to 777 ± 333 mg/day. All outcome variables improved significantly (P < 0.0005 all) by the end of IRT. At followup, all variables still maintained better values with respect to admission (P < 0.02 all). In particular AIMS score improved passing from 11.90 ± 6.5 at admission to 3.10 ± 2.3 at discharge and to 4.20 ± 2.7 at followup. Our results suggest that it is possible to act on dyskinesias in Parkinsonian patients with properly designed rehabilitation protocols. Intensive rehabilitation treatment, whose acute beneficial effects are maintained over time, might be considered a valid noninvasive therapeutic support for Parkinsonian patients suffering from diskinesia, allowing a reduction in drugs dosage and related adverse effects

Antiviral therapy in acute viral hepatitis B: why and when

Acute viral hepatitis B is cleared in more than 95% of patients, while the remainder ones may develop either chronic HBV infection or, rarely, fulminant hepatitis