Low-complexity and low-latency equalization technique - probabilistic noise cancellation.

Both inside data centers (DCs) and in short optical links between data centers (DC campuses), intensity-modulation and direct-detection (IMDD) systems using four-level pulse amplitude modulation (PAM4) will dominate this decade due to low transceiver price and power consumption. The next DC transceiver generation based on 100 Gbaud PAM4 will require advanced digital signal processing (DSP) algorithms and more powerful forward error correction (FEC) codes. Because of bandwidth limitations, the conventional DC DSP based on a few-tap linear feed-forward equalizer (FFE) is likely to be upgraded to more complex but still low-complexity Volterra equalizers followed by a noise whitening filter and either a maximum likelihood sequence estimation (MLSE) or a maximum a posteriori probability (MAP) algorithm. However, stringent power consumption and latency requirements may limit the use of complex algorithms such as decision feedback equalizer (DFE) or MLSE/MAP in DC networks (DCN). In this paper, we introduce a low-complexity, low-latency algorithm based on a feedforward structure, yielding a performance between DFE and MLSE. We call the novel equalization algorithm probabilistic noise cancellation (PNC), since it weights noise patterns based on their probabilities in the presence of bandwidth limitations. The probabilistic weighting is efficiently exploited in correcting correlated errors caused by noise coloring in the FFE.

Bioink Formulation and Machine Learning-Empowered Bioprinting Optimization.

Bioprinting enables the fabrication of complex, heterogeneous tissues through robotically-controlled placement of cells and biomaterials. It has been rapidly developing into a powerful and versatile tool for tissue engineering. Recent advances in bioprinting modalities and biofabrication strategies as well as new materials and chemistries have led to improved mimicry and development of physiologically relevant tissue architectures constituted with multiple cell types and heterogeneous spatial material properties. Machine learning (ML) has been applied to accelerate these processes. It is a new paradigm for bioprinting. In this review, we explore current trends in bioink formulation and how ML has been used to accelerate optimization and enable real-time error detection as well as to reduce the iterative steps necessary for bioink formulation. We examined how rheometric properties, including shear storage, loss moduli, viscosity, shear-thinning property of biomaterials affect the printability of a bioink. Furthermore, we scrutinized the interplays between yield shear stress and the printability of a bioink. Moreover, we systematically surveyed the application of ML in precision in situ surgical site bioprinting, closed-loop AI printing, and post-printing optimization.

Experimental demonstration of advanced modulation formats for data center networks on 200 Gb/s lane rate IMDD links.

This work contributes experimental demonstrations and comprehensive comparisons of various modulation and coding techniques for 200 Gb/s intensity modulation and direct detection links including four-level pulse amplitude modulation (PAM-4), PAM-6, trellis-coded modulation (TCM) over PAM and discrete multi-tone (DMT) transmission. Both C-band Mach-Zehnder modulator and O-band electro-absorption modulated laser transmitters were examined for intra-data center applications based on state-of-the-art commercial components.

Health and social impacts of COPD and the problem of under-diagnosis.

This article deals with the prevalence and the possible reasons of COPD underestimation in the population and gives suggestions on how to overcome the obstacles and make the correct diagnosis in order to provide the patients with the appropriate therapy. COPD is diagnosed in later or very advanced stages. In Italy the rate of COPD under-diagnosis ranges between 25 and 50% and, as a consequence, the patient does not consult his doctor until the symptoms have worsened, mainly due to exacerbations. A missed diagnosis influences the timing of therapeutic intervention, thus contributing to the evolution into more severe stages of the illness. An incisive intervention to limit under-diagnosis cannot act only in remittance (passive diagnosis), but must be the promoter for a series of preventive actions: primary, secondary and rehabilitative. To reduce under-diagnosis, some actions need to be taken, such as screening programs for smokers subjects, use of questionnaires aimed to qualify and monitor the disease severity, spirometry, early diagnosis. There is a consensus regarding diagnoses based on screening of at-risk subjects and symptoms, rather than screening of the general population. In practice, all individuals over 40 years of age with risk factors should make a spirometry test. Screening actions on a national scale can be the following: compilation of questionnaires in waiting rooms of doctor's offices or performing simple maneuvers to evaluate the expiratory force at pharmacies. It is now widely recognized that COPD is a complex syndrome with several pulmonary and extrapulmonary components; as a result, the airway obstruction as assessed by FEV1 by itself does not adequately describe the complexity of the disease and FEV1 cannot be used alone for the optimal diagnosis, assessment, and management of the disease. The identification and subsequent grouping of key elements of the COPD syndrome into clinically meaningful and useful subgroups (phenotypes) can guide therapy more effectively. In conclusion, we firmly believe that an early and correct diagnosis can influence positively the progress of the disease (lowering the lung function impairment), decrease the risk of exacerbations, relieve symptoms and increase the patients' quality of life leading also to a decrease in costs associated to the exacerbations and hospitalization of the patient.