Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks

The autoregressive neural networks are emerging as a powerful computational tool to solve relevant problems in classical and quantum mechanics. One of their appealing functionalities is that, after they have learned a probability distribution from a dataset, they allow exact and efficient sampling of typical system configurations. Here we employ a neural autoregressive distribution estimator (NADE) to boost Markov chain Monte Carlo (MCMC) simulations of a paradigmatic classical model of spin-glass theory, namely the two-dimensional Edwards-Anderson Hamiltonian. We show that a NADE can be trained to accurately mimic the Boltzmann distribution using unsupervised learning from system configurations generated using standard MCMC algorithms. The trained NADE is then employed as smart proposal distribution for the Metropolis-Hastings algorithm. This allows us to perform efficient MCMC simulations, which provide unbiased results even if the expectation value corresponding to the probability distribution learned by the NADE is not exact. Notably, we implement a sequential tempering procedure, whereby a NADE trained at a higher temperature is iteratively employed as proposal distribution in a MCMC simulation run at a slightly lower temperature. This allows one to efficiently simulate the spin-glass model even in the low-temperature regime, avoiding the divergent correlation times that plague MCMC simulations driven by local-update algorithms. Furthermore, we show that the NADE-driven simulations quickly sample ground-state configurations, paving the way to their future utilization to tackle binary optimization problems.Comment: 13 pages, 14 figure

Sarcopenic Obesity and Depression: A Systematic Review

BACKGROUND: Sarcopenic obesity (SO) is a condition combining two important public health issues commonly seen amongst older individuals, obesity and sarcopenia. Depressive symptoms are common among older people, whose population is increasing worldwide. Obesity and sarcopenia alone, are clearly associated with depression while the coexistence of these two conditions (SO) upon depressive disorders is currently unclear. We aimed to systematically review the association between primary SO and depressive disorders. METHODS: Searches were run on MEDLINE, EMBASE, PsycINFO, and CINAHL (inception to June 2019). One reviewer screened titles, abstracts, and full-texts, with 10% checked independently by a second reviewer. Cohort and cross-sectional studies were included. Two reviewers independently assessed risk of bias using the Mixed Methods Appraisal Tool. Results were narratively synthesised. RESULTS: Out of the 7 studies eligible for inclusion, evidence of sarcopenic obesity as a predictor of depressive symptoms was found in two studies. The main observed trend was that diagnosing sarcopenia using muscle strength led to significant associations between sarcopenic obesity and depressive symptoms. Two cross-sectional studies found a significant association between SO and depressive symptoms, whilst three others found no statistically significant associations. All possessed some methodological limitations. DISCUSSION: This is the first review to systematically examine a potential relationship between sarcopenic obesity and depressive disorders. Currently, the results are heterogeneous due to the large variability in assessment methods and outcome measurements. Future longitudinal studies would achieve greater confidence in the provisional conclusion that sarcopenic obesity, when measured using muscle strength, is associated with depressive symptoms

Coordination networks incorporating halogen-bond donor sites and azobenzene groups

Two Zn coordination networks, [Zn(1)(Py)2]2(2-propanol)n (3) and [Zn(1)2(Bipy)2](DMF)2n (4), incorporating halogen-bond (XB) donor sites and azobenzene groups have been synthesized and fully characterized. Obtaining 3 and 4 confirms that it is possible to use a ligand wherein its coordination bond acceptor sites and XB donor sites are on the same molecular scaffold (i.e., an aromatic ring) without interfering with each other. We demonstrate that XBs play a fundamental role in the architectures and properties of the obtained coordination networks. In 3, XBs promote the formation of 2D supramolecular layers, which, by overlapping each other, allow the incorporation of 2-propanol as a guest molecule. In 4, XBs support the connection of the layers and are essential to firmly pin DMF solvent molecules through I⋯O contacts, thus increasing the stability of the solvated systems

Condensate deformation and quantum depletion of Bose-Einstein condensates in external potentials

The one-body density matrix of weakly interacting, condensed bosons in external potentials is calculated using inhomogeneous Bogoliubov theory. We determine the condensate deformation caused by weak external potentials on the mean-field level. The momentum distribution of quantum fluctuations around the deformed ground state is obtained analytically, and finally the resulting quantum depletion is calculated. The depletion due to the external potential, or potential depletion for short, is a small correction to the homogeneous depletion, validating our inhomogeneous Bogoliubov theory. Analytical results are derived for weak lattices and spatially correlated random potentials, with simple, universal results in the Thomas-Fermi limit of very smooth potentials.Comment: 17 pages, 4 figures. v2: published version, minor change

Incidence of mild cognitive impairment and dementia in Parkinson's disease: The Parkinson's disease cognitive impairment study

Background: Cognitive impairment in Parkinson's disease (PD) includes a spectrum varying from Mild Cognitive Impairment (PD-MCI) to PD Dementia (PDD). The main aim of the present study is to evaluate the incidence of PD-MCI, its rate of progression to dementia, and to identify demographic and clinical characteristics which predict cognitive impairment in PD patients. Methods: PD patients from a large hospital-based cohort who underwent at least two comprehensive neuropsychological evaluations were retrospectively enrolled in the study. PD-MCI and PDD were diagnosed according to the Movement Disorder Society criteria. Incidence rates of PD-MCI and PDD were estimated. Clinical and demographic factors predicting PD-MCI and dementia were evaluated using Cox proportional hazard model. Results: Out of 139 enrolled PD patients, 84 were classified with normal cognition (PD-NC), while 55 (39.6%) fulfilled the diagnosis of PD-MCI at baseline. At follow-up (mean follow-up 23.5 ± 10.3 months) 28 (33.3%) of the 84 PD-NC at baseline developed MCI and 4 (4.8%) converted to PDD. The incidence rate of PD-MCI was 184.0/1000 pyar (95% CI 124.7-262.3). At multivariate analysis a negative association between education and MCI development at follow-up was observed (HR 0.37, 95% CI 0.15-0.89; p = 0.03). The incidence rate of dementia was 24.3/1000 pyar (95% CI 7.7-58.5). Out of 55 PD-MCI patients at baseline, 14 (25.4%) converted to PDD, giving an incidence rate of 123.5/1000 pyar (95% CI 70.3-202.2). A five time increased risk of PDD was found in PD patients with MCI at baseline (RR 5.09, 95% CI 1.60-21.4). Conclusion: Our study supports the relevant role of PD-MCI in predicting PDD and underlines the importance of education in reducing the risk of cognitive impairment

Copépodos ciclopoideos en la Provincia de La Pampa (Argentina)

The aim of this work are to extend the records of planktonic copepods of La Pampa province and lo give a brief diagnostic description of the main features of each species. I found 5 species quoted for the first time for the province: Acanthocyclops robustus, Mesocyciops meridianus, Metacyciops mendocinus, Microcylops anceps anceps and Tropocyciops prasinus meridionales.Los objetivos del presente trabajo son ampliar la lista de copépodos planctónicos que existen en los Iimnótopos de la provincia de La Pampa y realizar una breve descripción diagnóstica de las especies halladas. Se identificaron 5 especies de ciclopoideos que se citan por primera vez para la provincia: Acanthocyclops robustus, Mesocyclops meridíanus, Metacyclops mendocmus, Microcylops anceps anceps y Tropocyclops prasínus meridionaes

Assembly line balancing and activity scheduling for customised products manufacturing

Nowadays, end customers require personalized products to match their specific needs. Thus, production systems must be extremely flexible. Companies typically exploit assembly lines to manufacture produces in great volumes. The development of assembly lines distinguished by mixed or multi models increases their flexibility concerning the number of product variants able to be manufactured. However, few scientific contributions deal with customizable products, i.e., produces which can be designed and ordered requiring or not a large set of available accessories. This manuscript proposes an original two-step procedure to deal with the multi-manned assembly lines for customized product manufacturing. The first step of the procedure groups the accessories together in clusters according to a specific similarity index. The accessories belonging to a cluster are typically requested together by customers and necessitate a significant mounting time. Thus, this procedure aims to split accessories belonging to the same cluster to different assembly operators avoiding their overloads. The second procedure step consists of an innovative optimization model which defines tasks and accessory assignment to operators. Furthermore, the developed model defines the activity time schedule in compliance with the task precedencies maximizing the operator workload balance. An industrial case study is adopted to test and validate the proposed procedure. The obtained results suggest superior balancing of such assembly lines, with an average worker utilization rate greater than 90%. Furthermore, in the worst case scenario in terms of customer accessories requirement, just 4 line operators out of 16 are distinguished by a maximum workload greater than the cycle time

Machine Learning Scoring Functions for Drug Discoveries from Experimental and Computer-Generated Protein-Ligand Structures: Towards Per-Target Scoring Functions

In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein-ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed.Comment: 22 pages, 8 figure

Predictive maintenance: a novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries

Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals

La videosorveglianza nei luoghi di lavoro fra Convenzione europea dei diritti dell\u2019uomo, diritto dell\u2019UE e diritto italiano

Con la sentenza L\uf3pez Ribalda et al. c. Spagna del 17 ottobre 2019,1 la Grande Camera della Corte europea dei diritti dell\u2019uomo (Corte EDU) ha ribaltato il giudizio reso da una Camera della stessa Corte il 9 gennaio 2018,2 secondo cui la videosorveglianza segreta dei dipendenti di una catena di supermercati spagnola, effettuata a seguito di sospetti di furto, violava il diritto al rispetto della vita privata sancito dall\u2019art. 8 della Convenzione europea dei diritti dell\u2019uomo (CEDU). La pronuncia \ue8 interessante per il bilanciamento che offre fra i diritti dei lavoratori e del datore diritti \u2013 il bilanciamento fra diritti individuali \ue8 sempre opera delicata, lo \ue8 a maggior ragione in materia lavoro. Essa merita di essere annotata per le sue possibili ricadute (auspicabili o meno) nell\u2019ordinamento italiano e, pi\uf9 nello specifico, la sua idoneit\ue0 a condizionare l\u2019applicazione e interpretazione delle disposizioni nazionali che regolano la videosorveglianza nei luoghi di lavoro \u2013 che hanno costituito oggetto di recenti interventi normativi, e sono da sempre tema di dibattito dottrinale e giurisprudenziale