Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Self-Attention for Recommender Systems

Recommender systems (RS), which have been an essential part in a wide range of applications, can be formulated as a matrix completion (MC) problem. To boost the performance of MC, matrix completion with side information, called inductive matrix completion (IMC), was further proposed. In real applications, the factorized version of IMC is more favored due to its efficiency of optimization and implementation. Regarding the factorized version, traditional IMC method can be interpreted as learning an individual representation for each feature, which is independent from each other. Moreover, representations for the same features are shared across all users/items. However, the independent characteristic for features and shared characteristic for the same features across all users/items may limit the expressiveness of the model. The limitation also exists in variants of IMC, such as deep learning based IMC models. To break the limitation, we generalize recent advances of self-attention mechanism to IMC and propose a context-aware model called collaborative self-attention (CSA), which can jointly learn context-aware representations for features and perform inductive matrix completion process. Extensive experiments on three large-scale datasets from real RS applications demonstrate effectiveness of CSA.
Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering

Neurons which fire in a fixed temporal pattern (i.e., “cell assemblies”) are hypothesized to be a fundamental unit of neural information processing. Several methods are available for the detection of cell assemblies without a time structure. However, the systematic detection of cell assemblies with time structure has been challenging, especially in large datasets, due to the lack of efficient methods for handling the time structure. Here, we show a method to detect a variety of cell-assembly activity patterns, recurring in noisy neural population activities at multiple timescales. The key innovation is the use of a computer science method to comparing strings (“edit similarity”), to group spikes into assemblies. We validated the method using artificial data and experimental data, which were previously recorded from the hippocampus of male Long-Evans rats and the prefrontal cortex of male Brown Norway/Fisher hybrid rats. From the hippocampus, we could simultaneously extract place-cell sequences occurring on different timescales during navigation and awake replay. From the prefrontal cortex, we could discover multiple spike sequences of neurons encoding different segments of a goal-directed task. Unlike conventional event-driven statistical approaches, our method detects cell assemblies without creating event-locked averages. Thus, the method offers a novel analytical tool for deciphering the neural code during arbitrary behavioral and mental processes.
Long optical lattice vibrations and dielectric function of monolayer hexagonal boron nitride

Using two pairs of lattice equations deduced from a microscopic dipole lattice model taking into account electronic polarization (EP) of ions and local field effects (LFEs) self-consistently, in-plane and out-of-plane optical vibrations in two-dimensional (2D) hexagonal BN are studied theoretically. The three mutually independent coefficients of either pair of lattice equations are determined by a set of three generally known quantities such as the 2D electronic and static susceptibilities and phonon frequency, making the lattice equations very useful for calculating the lattice dynamical properties. Explicit expressions are obtained for lattice vibrational energy density, and phonon dispersion, group velocity and density of states. The transparent phonon dispersion relations describe the previous numerical calculations very well, and the longitudinal optical (LO) phonon dispersion relation is identical to the analytical expression of Sohier {\it et al}. [Nano Lett. {\bf 17}, 3758 (2017)]. The out-of-plane phonon frequency is finite owing to ionic EP. A 2D lattice dielectric function $\epsilon(k,\omega)$ is derived--due solely to the LO vibrations--which also allows the LO phonon dispersion to be rederived simply from $\epsilon(k,\omega)=0$, similar to the bulk case. A 2D Lyddane--Sachs--Teller relation and a frequency--susceptibility relation are obtained for the in-plane and out-of-plane vibrations, respectively, connecting the phonon frequencies to the 2D dielectric functions or susceptibilities. Using three first-principles calculated parameters, the lattice dynamical properties are studied comprehensively, particular attention being paid to the EP and LFEs. The ionic EP and LFEs should be included simultaneously, but otherwise neglecting either or both causes large discrepancies to the calculated dynamical properties.
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes

In the analysis of sequential data, the detection of abrupt changes is important in predicting future events. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified thresholds for the tests, and use them to improve Bayesian Online Change Point Detection (BOCPD) by confirming statistically significant changes and non-changes. Our Confirmatory BOCPD (CBOCPD) algorithm finds multiple structural breaks in GPs even when hyperparameters are not tuned precisely. We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD. Experimental results on synthetic and real-world datasets show that our proposed algorithm outperforms existing methods for the prediction of nonstationarity in terms of both regression error and log-likelihood.
A Mentalization-Informed Staging Approach to Clinical High Risk for Psychosis

The practice of diagnosis is fundamentally designed to orient treatment. In the case of early diagnosis for schizophrenia spectrum disorders (SSP) risk, the empirical base for such a practice is still young, and many clinical questions arise in the everyday clinical application of risk algorithms and ensuing therapeutic options. One of the key questions that we will focus on is the following: in cases of SSP where symptoms are successfully treated, why does residual social functioning impairment remain the most serious obstacle to remission and reinsertion in society? We will present the evidence suggesting that the roots of residual social functioning impairment may, in many cases, come from thwarted or arrested development in the specialization of social cognition during adolescence and early adulthood. We will review the evidence suggesting that both during the premorbid phase and clinical high-risk phase, attenuated psychotic symptoms may impede the maturation of key social cognitive processes, particularly the suite of reflective thinking processes coming under the term of mentalization. From this evidence base, we will adapt the staging model of SSP progression in function of our mentalization-informed model, tailored to provide a coherent framework of care addressing the key clinical needs at every stage of psychosis progression.
Prediction of the Antioxidant Response Elements' Response of Compound by Deep Learning

The antioxidant response elements (AREs) play a significant role in occurrence of oxidative stress and may cause multitudinous toxicity effects in the pathogenesis of a variety of diseases. Determining if one compound can activate AREs is crucial for the assessment of potential risk of compound. Here, a series of predictive models by applying multiple deep learning algorithms including deep neural networks (DNN), convolution neural networks (CNN), recurrent neural networks (RNN), and highway networks (HN) were constructed and validated based on Tox21 challenge dataset and applied to predict whether the compounds are the activators or inactivators of AREs. The built models were evaluated by various of statistical parameters, such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and receiver operating characteristic (ROC) curve. The DNN prediction model based on fingerprint features has best prediction ability, with accuracy of 0.992, 0.914, and 0.917 for the training set, test set, and validation set, respectively. Consequently, these robust models can be adopted to predict the ARE response of molecules fast and accurately, which is of great significance for the evaluation of safety of compounds in the process of drug discovery and development.
Antithrombotic Therapy in Transcatheter Aortic Valve Replacement

Transcatheter aortic valve replacement (TAVR) has recently emerged as an effective alternative to medical treatment or surgical aortic valve replacement in all symptomatic patients with severe aortic stenosis and high or prohibitive risk and in intermediate risk when transfemoral access is feasible. Patients undergoing TAVR are often at high risk for either bleeding or cerebrovascular complications, or both, so adjuvant antithrombotic therapies are commonly used before, during and after the procedure. Today, there is no clear evidence on the best antithrombotic regimen in this context. In this review, we will try to go through the mechanisms involved in bleeding and embolic complications and we will discuss the current points of antithrombotic treatment in patients during and after TAVR, with or without oral anticoagulation (OAC) indication.
Confining the Concept of Vascular Depression to Late-Onset Depression: A Meta-Analysis of MRI-Defined Hyperintensity Burden in Major Depressive Disorder and Bipolar Disorder

Background: The vascular depression hypothesis emphasizes the significance of vascular lesions in late-life depression. At present, no meta-analytic model has investigated whether a difference in hyperintensity burden compared to controls between late-life and late-onset depression is evident. By including a substantial number of studies, focusing on a meaningful outcome measure, and considering several moderating and control variables, the present meta-analysis investigates the severity of hyperintensity burden in major depressive disorder (MDD) and bipolar disorder (BD). A major focus of the present meta-analysis refers to the role of age at illness onset. It is analyzed whether late-onset rather than late-life depression characterizes vascular depression. Method: In total, 68 studies were included in the meta-analysis and a multilevel random effects model was calculated using Hedges' g as the effect size measure. Results: The severity of hyperintensity burden was significantly greater in the patient group compared to the control group. This effect was evident regarding the whole patient group (g = 0.229) as well as both depression subgroups, with a significantly greater effect in BD (g = 0.374) compared to MDD (g = 0.189). Hyperintensity burden was more pronounced in late-onset depression than in early-onset depression or late-life depression. A considerable heterogeneity between the included studies was observed, which is reflected by the large variability in effects sizes. Conclusion: In conclusion, the present meta-analysis underscores the association of hyperintensities with MDD and BD. Especially late-onset depression is associated with an increased hyperintensity burden, which is in line with the vascular depression hypothesis. The results suggest that it might be more feasible to confine the concept of vascular depression specifically to late-onset depression as opposed to late-life depression. Further research is needed to understand the causal mechanisms that might underlie the relation between hyperintensity burden and depression.
Somewhere Around That Number: An Interview Study of How Spreadsheet Users Manage Uncertainty

Spreadsheet users regularly deal with uncertainty in their data, for example due to errors and estimates. While an insight into data uncertainty can help in making better informed decisions, prior research suggests that people often use informal heuristics to reason with probabilities, which leads to incorrect conclusions. Moreover, people often ignore or simplify uncertainty. To understand how people currently encounter and deal with uncertainty in spreadsheets, we conducted an interview study with 11 spreadsheet users from a range of domains. We found that how people deal with uncertainty is influenced by the role the spreadsheet plays in people's work and the user's aims. Spreadsheets are used as a database, template, calculation tool, notepad and exploration tool. In doing so, participants' aims were to compute and compare different scenarios, understand something about the nature of the uncertainty in their situation, and translate the complexity of data uncertainty into simplified presentations to other people, usually decision-makers. Spreadsheets currently provide limited tools to support these aims, and participants had various workarounds.
Revealing the Star–Disk–Jet Connection in GM Aur Using Multiwavelength Variability

We analyze the first simultaneous X-ray, ultraviolet, optical, infrared, and centimeter observations of a T Tauri star (TTS). We present three epochs of simultaneous Spitzer and Very Large Array data of GM Aur separated by ∼1 week. These data are compared to previously published Hubble Space Telescope and Chandra observations from which mass accretion rates ( ) and X-ray luminosities, respectively, were measured. The mid-infrared (MIR) emission increases along with , and we conclude that this is due to an increase in the mass in the inner disk. The centimeter emission, which probes the jet, also appears to increase as increases, and the changes in the centimeter flux are consistent with the variability in assuming the mass-loss rate is ∼10% . The 3 cm emission morphology also appears changed compared with observations taken three years previously, suggesting that for the first time, we may be tracking changes in the jet morphology of a TTS. The X-ray luminosity is constant throughout the three epochs, ruling out variable high-energy stellar radiation as the cause for the increases in the MIR or centimeter emission. Tying together the multiwavelength variability observed, we conclude that an increase in the surface density in the inner disk resulted in more mass loading onto the star and therefore a higher , which led to a higher mass-loss rate in the jet. These results stress the importance of coordinated multiwavelength work to better understand the star–disk–jet connection.
A Longitudinal Study of Utilization at Internet Interconnection Points

The increase in high-volume traffic flows due to applications such as video streaming draw new attention on utilization at the interconnections between the Internet’s independently operated networks. This paper surveys the findings from nearly two years of Internet utilization data provided by seven participating ISPs—Bright House Networks, Comcast, Cox, Mediacom, Midco, Suddenlink, and Time Warner Cable—whose access networks represent about 50% of all U.S. broadband subscribers. The dataset spans 18 months and includes about 97% of the paid peering, settlement-free peering, and ISPpaid transit links of each of the participating ISPs. Analysis of the data—which comprises more than 1,000 link groups, representing the diverse and substitutable available routes— suggests that many interconnects have significant spare capacity, that this spare capacity exists both across ISPs in each region and in aggregate for any individual ISP, and that the aggregate utilization across interconnects is roughly 50% during peak periods.
Distribution of GABAergic Neurons and VGluT1 and VGAT Immunoreactive Boutons in the Ferret (Mustela putorius) Piriform Cortex and Endopiriform Nucleus. Comparison With Visual Areas 17, 18 and 19

We studied the cellular organization of the piriform network [comprising the piriform cortex (PC) and endopiriform nucleus (EP)] of the ferret (Mustela putorius)—a highly excitable region prone to seizures—and, more specifically, the distribution and morphology of different types of gamma-aminobutyric acid (GABA)ergic neurons, and the distribution and ratio of glutamatergic and GABAergic boutons, and we compared our findings to those in primary visual area 17, and secondary areas 18 and 19. We accomplished this by using cytochrome oxidase and immunohistochemistry for mature neuronal nuclei (NeuN), GABAergic neurons [glutamic acid decarboxylase-67 (GAD67), calretinin (CR) and parvalbumin (PV)], and for excitatory (vesicular glutamate transporter 1; VGluT1) and inhibitory (vesicular GABA transporter; VGAT) boutons. In the ferret, the cellular organization of the piriform network is similar to that described in other species such as cats, rats and opossums although some differences also exist. GABAergic immunolabeling showed similarities between cortical layers I–III of the PC and visual areas, such as the relative distribution of GABAergic neurons and the density and area of VGluT1- and VGAT-immunoreactive boutons. However, multiple differences between the piriform network and visual areas (layers I–VI) were found, such as the percentage of GABAergic neurons with respect to the total number of neurons and the ratio of VGluT1- and VGAT-immunoreactive boutons. These findings are relevant to better understand the high excitability of the piriform network.
Hierarchical modeling of diabetes – a pilot study

In type 2 diabetes the concentration of glucose in the blood is increased, and tissues like fat and muscle become less sensitive to insulin. These two phenomena are interrelated, but since the glucose-insulin interplay is highly complex, many aspects are still not understood. Here, a model-based approach might help. Nevertheless, also a model-based approach has a limited impact, unless models for the sub-systems can be combined into a model for the whole-body regulation. Such a multi-level, module-based model is referred to as a hierarchical model, and this thesis is a proof-of-principle study for the future development of such models. We have extended one of the best available models for the whole-body regulations, to include a zoomable module for the fat tissue. The first step was to implement the wholebody model in the software MathModelica, which support hierarchical modeling. Second, the originally merged insulin-responding module was sub-divided, so that a fat tissue was singled out. Third, a model for the input-output profile for the fat tissue was developed by combining mechanistic knowledge with existing and novel data from human fat cells. Finally, this detailed model was fitted to the profile of the original fat model, and inserted in the whole-body model, with negligible effect on the whole-body simulations. The resulting model has the ability to translate mechanistically oriented simulations on the biochemical level, which is the level were drugs act, to the whole-body level, which is of clinical interest. This is a quantum leap forward for modeling, and understanding, glucose homeostasis and type 2 diabetes.
Heterogeneity in demand and optimal price conditioning for local rail transport

This paper describes the results of research project on optimal pricing for LLC "Perm Local Rail Company". In this study we propose a regression tree based approach for estimation of demand function for local rail tickets considering high degree of demand heterogeneity by various trip directions and the goals of travel. Employing detailed data on ticket sales for 5 years we estimate the parameters of demand function and reveal the significant variation in price elasticity of demand. While in average the demand is elastic by price, near a quarter of trips is characterized by weakly elastic demand. Lower elasticity of demand is correlated with lower degree of competition with other transport and inflexible frequency of travel.
Tilapia (Oreochromis niloticus) as a Putative Reservoir Host for Survival and Transmission of Vibrio cholerae O1 Biotype El Tor in the Aquatic Environment

Studies have reported the occurrence of Vibrio cholerae in fish but little is known about the interaction between fish and toxigenic V. cholerae as opposed to phytoplankton, which are well-established aquatic reservoirs for V. cholerae. The present study determined the role of tilapia (Oreochromis niloticus) as a reservoir host for survival and transmission of V. cholerae in aquatic environments. Three experiments were performed with one repetition each, where O. niloticus (∼2 g) kept in beakers were inoculated with four V. cholerae strains (5 × 107 cfu/mL). Firstly, infected tilapia were kept in stagnant water and fed live brine shrimp (Artemia salina) larvae daily. Secondly, infected tilapia were kept without feeding and water was changed every 24 h. Thirdly, infected tilapia were fed and water was renewed daily. Infected tilapia and non-infected controls were sacrificed on days 1, 2, 3, 7, and 14 post-inoculation and V. cholerae were enumerated in intestinal content and water. Another experiment assessed the transmission of V. cholerae from infected to non-infected tilapia. The study revealed that El Tor biotype V. cholerae O1 and V. cholerae non-O1 colonized tilapia intestines and persisted at stable concentrations during the second week of the experiment whereas the Classical biotype was undetectable after 1 week. In stagnant water with feeding, V. cholerae counts dropped to 105 cfu/ml in water and from 107 to 104 cfu/intestine in fish after 14 days. When water was renewed, counts in water decreased from 107 to 103 cfu/ml and intestinal counts went from 106 to 102 cfu/intestine regardless of feeding. All strains were transmitted from infected to naïve fish after 24 h of cohabitation. Tilapia like other fish may play an essential role in the survival and dissemination of V. cholerae O1 in aquatic environments, e.g., the seventh pandemic strains mostly. In this study, tilapia were exposed to high concentrations of V. cholerae to ensure initial uptake and follow-up studies with lower doses resembling natural concentrations of V. cholerae in the aquatic environment are needed to confirm our findings.
Uncorrelated configurations and field uniformity in reverberation chambers stirred by reconfigurable metasurfaces

Reverberation chambers are currently used to test electromagnetic compatibility as well as to characterize antenna efficiency, wireless devices, and MIMO systems. The related measurements are based on statistical averages and their fluctuations. We introduce a very efficient mode stirring process based on electronically reconfigurable metasurfaces (ERMs). By locally changing the field boundary conditions, the ERMs allow to generate a humongous number of uncorrelated field realizations even within small reverberation chambers. We fully experimentally characterize this stirring process by determining these uncorrelated realizations via the autocorrelation function of the transmissions. The IEC-standard uniformity criterion parameter $\sigma_{dB}$ is also investigated and reveals the performance of this stirring. The effect of short paths on the two presented quantities is identified. We compare the experimental results on the uniformity criterion parameter with a corresponding model based on random matrix theory and find a good agreement, where the only parameter, the modal overlap, is extracted by the quality factor.