Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning User Preferences in Non-Stationary Environments

Recommendation systems often use online collaborative filtering (CF) algorithms to identify items a given user likes over time, based on ratings that this user and a large number of other users have provided in the past. This problem has been studied extensively when users' preferences do not change over time (static case); an assumption that is often violated in practical settings. In this paper, we introduce a novel model for online non-stationary recommendation systems which allows for temporal uncertainties in the users' preferences. For this model, we propose a user-based CF algorithm, and provide a theoretical analysis of its achievable reward. Compared to related non-stationary multi-armed bandit literature, the main fundamental difficulty in our model lies in the fact that variations in the preferences of a certain user may affect the recommendations for other users severely. We also test our algorithm over real-world datasets, showing its effectiveness in real-world applications. One of the main surprising observations in our experiments is the fact our algorithm outperforms other static algorithms even when preferences do not change over time. This hints toward the general conclusion that in practice, dynamic algorithms, such as the one we propose, might be beneficial even in stationary environments.
A Survey of Complex-Valued Neural Networks

Artificial neural networks (ANNs) based machine learning models and especially deep learning models have been widely applied in computer vision, signal processing, wireless communications, and many other domains, where complex numbers occur either naturally or by design. However, most of the current implementations of ANNs and machine learning frameworks are using real numbers rather than complex numbers. There are growing interests in building ANNs using complex numbers, and exploring the potential advantages of the so-called complex-valued neural networks (CVNNs) over their real-valued counterparts. In this paper, we discuss the recent development of CVNNs by performing a survey of the works on CVNNs in the literature. Specifically, a detailed review of various CVNNs in terms of activation function, learning and optimization, input and output representations, and their applications in tasks such as signal processing and computer vision are provided, followed by a discussion on some pertinent challenges and future research directions.
5G Network Slicing for Wi-Fi Networks

Future networks will pave the way for a myriad of applications with different requirements and Wi-Fi will play an important role in local area networks. This is why network slicing is proposed by 5G networks, allowing to offer multiple logical networks tailored to the different user requirements, over a common infrastructure. However, this is not supported by current Wi-Fi networks. In this paper, we propose a standard-compliant network slicing approach for the radio access segment of Wi-Fi by defining multiple Service Set Identifiers (SSIDs) per Access Point (AP). We present two algorithms, one that assigns resources according to the requirements of slices in a static way, and another that dynamically configures the slices according to the network’s conditions and relevant Key Performance Indicators (KPIs). The proposed algorithms were validated through extensive simulations, conducted in the ns-3 network simulator, and complemented by theoretical assessments. The obtained results reveal that the two proposed slicing approaches outperform today’s Wi-Fi access technique, reaching lower error probability for bandwidth intensive slices and lower latency for time-critical slices. Simultaneously, the proposed approach is up to 32 times more energy efficient, when considering slices tailored for low-power and low-bandwidth devices, while increasing the overall spectrum efficiency.
D3dlo: Deep 3d Lidar Odometry

LiDAR odometry (LO) describes the task of finding an alignment of subsequent LiDAR point clouds. This alignment can be used to estimate the motion of the platform where the LiDAR sensor is mounted on. Currently, on the well-known KITTI Vision Benchmark Suite state-of-the-art algorithms are non-learning approaches. We propose a network architecture that learns LO by directly processing 3D point clouds. It is trained on the KITTI dataset in an end-to-end manner without the necessity of pre-defining corresponding pairs of points. An evaluation on the KITTI Vision Benchmark Suite shows similar performance to a previously published work, DeepCLR [1], even though our model uses only around 3.56% of the number of network parameters thereof. Furthermore, a plane point extraction is applied which leads to a marginal performance decrease while simultaneously reducing the input size by up to 50%.
DigitalExposome: Quantifying the Urban Environment Influence on Wellbeing based on Real-Time Multi-Sensor Fusion and Deep Belief Network

In this paper, we define the term 'DigitalExposome' as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodel mobile sensing technology. Specifically, we simultaneously collected (for the first time) multi-sensor data including urban environmental factors (e.g. air pollution including: PM1, PM2.5, PM10, Oxidised, Reduced, NH3 and Noise, People Count in the vicinity), body reaction (physiological reactions including: EDA, HR, HRV, Body Temperature, BVP and movement) and individuals' perceived responses (e.g. self-reported valence) in urban settings. Our users followed a pre-specified urban path and collected the data using a comprehensive sensing edge devices. The data is instantly fused, time-stamped and geo-tagged at the point of collection. A range of multivariate statistical analysis techniques have been applied including Principle Component Analysis, Regression and spatial visualisations to unravel the relationship between the variables. Results showed that EDA and Heart Rate Variability HRV are noticeably impacted by the level of Particulate Matters (PM) in the environment well with the environmental variables. Furthermore, we adopted Deep Belief Network to extract features from the multimodel data feed which outperformed Convolutional Neural Network and achieved up to (a=80.8%, {\sigma}=0.001) accuracy.
Galaxy Clusters from the DESI Legacy Imaging Surveys. I. Cluster Detection

Based on the photometric redshift catalog of Zou et al., we apply a fast clustering algorithm to identify 540,432 galaxy clusters at z ≲ 1 in the DESI legacy imaging surveys, which cover a sky area of about 20,000 deg2. Monte Carlo simulations indicate that the false-detection rate of our detecting method is about 3.1%. The total masses of galaxy clusters are derived using a calibrated richness–mass relation that is based on the observations of X-ray emission and the Sunyaev and Zel’dovich effect. The median redshift and mass of our detected clusters are about 0.53 and 1.23 × 1014 M ⊙, respectively. Comparing with previous clusters identified using the data of the Sloan Digital Sky Survey; we can recognize most of them, especially those with high richness. Our catalog will be used for further statistical studies on galaxy clusters and environmental effects on galaxy evolution, etc.
A new communication paradigm: from bit accuracy to semantic fidelity

Wireless communication has achieved great success in the past several decades. The challenge is of improving bandwidth with limited spectrum and power consumption, which however has gradually become a bottleneck with evolution going on. The intrinsic problem is that communication is modeled as a message transportation from sender to receiver and pursues for an exact message replication in Shannon's information theory, which certainly leads to large bandwidth and power requirements with data explosion. However, the goal for communication among intelligent agents, entities with intelligence including humans, is to understand the meaning or semantics underlying data, not an accurate recovery of the transmitted messages. The separate first transmission and then understanding is a waste on bandwidth. In this article, we deploy semantics to solve the spectrum and power bottleneck and propose a first understanding and then transmission framework with high semantic fidelity. We first give a brief introduction of semantics covering the definition and properties to show the insights and scope of this paper. Then the proposed communication towards semantic fidelity framework is introduced, which takes the above mentioned properties into account to further improve efficiency. Specially, a semantic transformation is introduced to transform the input into semantic symbols. Different from the conventional transformations in signal processing area, for example discrete cosine transform, the transformation is with data loss, which is also the reason that the proposed framework can achieve large bandwidth saving with high semantic fidelity. Besides, we also discuss semantic noise and performance measurement. To evaluate the effectiveness, a case study of audio transmission is carried out. Finally, we discuss the typical applications and open challenges.
A Companion Curve Tracing Method for Rank-deficient Polynomial Systems

We propose a method for tracing implicit real algebraic curves defined by polynomials with rank-deficient Jacobians. For a given curve $f^{-1}(0)$, it first utilizes a regularization technique to compute at least one witness point per connected component of the curve. We improve this step by establishing a sufficient condition for testing the emptiness of $f^{-1}(0)$. We also analyze the convergence rate and carry out an error analysis for refining the witness points. The witness points are obtained by computing the minimum distance of a random point to a smooth manifold embedding the curve while at the same time penalizing the residual of $f$ at the local minima. To trace the curve starting from these witness points, we prove that if one drags the random point along a trajectory inside a tubular neighborhood of the embedded manifold of the curve, the projection of the trajectory on the manifold is unique and can be computed by numerical continuation. We then show how to choose such a trajectory to approximate the curve by computing eigenvectors of certain matrices. Effectiveness of the method is illustrated by examples.
ORGANIC HETEROJUNCTION PHOTOVOLTAIC CELLS MADE OF DISCOTIC, MESOGENIC MATERIALS

We made photovoltaic cells containing two organic layers. The hole-conducting layer consists of a hexa-peri-hexabenzocoronene (HBC) derivative. The electron-conducting component is an alkylester of perylene tetracarboxylic acid. Both compounds form columnar mesophases which can provide 1D pathways for transport. The current/voltage-characteristics of the two-layer devices indicate a photovoltaic effect with an open circuit voltage of Voc ≈ 0.8 V, a short circuit current of Isc ≈ 3.9 μA/cm2, and a filling factor of FF ≤ 31%. The spectral distribution of the photocurrent of different samples indicates that excitations of the perylene derivative or of the HBC derivative both can lead to a photovoltaic effect.
Time-of-flight neutron transmission diffraction

The positions of Bragg edges in neutron transmission experiments can be defined with high accuracy using the time-of-flight (TOF) technique on pulsed neutron sources. A new dedicated transmission instrument has been developed at ISIS, the UK spallation source, which provides a precision of Δd/d ≃ 10−5 in the determination of interplanar distances. This is achieved by fitting a theoretical three-parameter expression to the normalized Bragg edges appearing in the TOF transmission spectra. The technique is demonstrated by experiments performed on iron, niobium and nickel powders. The applicability of using the instrument for the determination of lattice strains in materials has been investigated using a simple in situ loading experiment. Details of the calibration process are presented and the dependence of the resolution and the experimental times required by the transmission geometry on the instrumental variables are studied. Finally, the requirements for a Rietveld-type refinement of transmission data and the advantages and limitations over traditional neutron diffraction peak analysis are discussed.
FTIR and NMR study of the adsorbed water on nanocrystalline anatase

A hydrated anatase sample with crystal size of 11 nm, prepared by a method involving a calcination treatment, has been studied by FTIR, 1H MAS NMR, and TPD techniques, to determine the characteristics of the hydroxyls and water adsorbed on its surface. The 1H NMR and FTIR spectra of the sample, hydrated and evacuated at increasing temperature, show that most of the hydrated anatase water, weakly adsorbed and highly mobile, is forming the multilayer of water molecular arrangements (MAs). The easy desorption of this water at room-temperature facilitates the observation of less mobile adsorbed water, whose removal originates two overlapped water desorption peaks, with maxima at 353 and 373 K, observed in the TPD profile. Most of this less weakly adsorbed water is desorbed from the MAs second layer, originating the low-temperature maximum. On the other hand, the removal of some water bound to bridging O2- anions, interacting with terminal hydroxyls, contributes to the maximum at 373 K. The TPD peak with maxim.
COVID-19 Prevention Beliefs and Practices in College Students

Background: As college students in the United States return to university campuses, it is important to understand their beliefs and practices on coronavirus disease 2019 (COVID-19) prevention. Purpose: To assess beliefs and practices regarding COVID-19 prevention among college students in the United States Methods: An online, self-administered survey was developed that collected information on COVID-19 preventative practice and beliefs. Survey responses were collected between July 13, 2020 and July 31, 2020. Results: A total of 4,834 college students participated in the survey with a response rate of 22.9%. Compared to males, more female college students practiced COVID-19 preventative measures, including always wearing masks or face coverings in public (52% vs. 44%, p<0.001) and always or often observing social distancing (70% vs. 63%, p<0.001). In contrast to students from larger population areas, fewer college students from rural areas reported practicing prevention measures, such as always wearing a mask (24% rural v. 45% towns vs. 55% cities, p<0.001) and always social distancing (20% rural vs. 21% towns vs. 29% cities, p<0.001). Additionally, more students from rural areas have become much less worried about personally contracting COVID-19 over the last 3 months when compared to students from towns and cities (21% vs. 16% vs. 11%, p<0.001). Fewer white college students compared to other racial groups thought it was very important to wear masks (55% white vs. 76% Black vs. 82% Asian vs. 63% American Indian or Alaskan native (AIAN) & Native Hawaiian or Other Pacific Islander (NHOPI) , p<0.001) and very important to practice social distancing (29% white vs. 50% Black vs. 53% Asian vs. 36% AIAN/NHOPI, p <0.001). Compared to Non-Hispanic students, more Hispanic students thought it was very important to practice preventative measures, including wearing a mask (71% vs. 58%, p<0.001), social distancing (37% vs. 32%, p=0.017), and good hand hygiene (77% vs. 67%, p=0.013). Conclusion: COVID-19 prevention beliefs and practices differ between sexes, the size of town one lives, race, and ethnicity. In general, female students followed Center for Disease Control and Prevention (CDC) COVID-19 prevention guidelines more closely than male students. Students who reside in areas of larger populations have more strict COVID-19 prevention beliefs and practices than students from areas with smaller populations. Asian and Black/African American students adhered closer to CDC COVID-19 prevention guidelines and had stronger beliefs for infection prevention measures than white or AIAN/NHOPI students. Hispanic/Latino students were more stringent in COVID-19 prevention beliefs and practices than non-Hispanic/Latino students.
Caspase‐6‐cleaved tau is relevant in Alzheimer's disease but not in 4‐repeat tauopathies: Diagnostic and therapeutic implications

Tau truncation by active caspase‐6 facilitates tau pathogenesis by producing toxic fragments prone to self‐aggregation. We previously demonstrated that neuronal active caspase‐6 co‐occurs with phosphorylated tau (p‐tau) during the earliest stages of Alzheimer’s disease (AD) and that the percentage of neurons positive for both active caspase‐6 and p‐tau increases as the disease progresses. Although caspase‐6‐mediated cleavage of tau appears to be a key contributor to tau pathology, questions concerning the relationship between caspase‐6 activation, tau cleavage, and the formation of p‐tau aggregates remain.
Development and life history parameters of Typhlodromus recki (Acari: Phytoseiidae) feeding on Tetranychus urticae (Acari: Tetranychidae) at different temperatures

Abstract Typhlodromus recki (Acari: Phytoseiidae) was collected from aubergines, pepper, black nightshade and jimsonweed plants infested with spider mites from a pesticide-free vegetable garden in Denizli, Turkey. The biology and life table parameters for T. recki feeding on Tetranychus urticae (Acari: Tetranychidae) at different temperatures (15, 20, 25, 30 and 35 °C) were determined. The results showed that total preadult periods of T. recki at 15, 20, 25, 30 and 35 °C were 19.6±0.51, 9.4±0.16, 7.6±0.14, 5.7±0.14 and 4.5±0.08 days, respectively. The longest oviposition period was 23.5 days at 20 °C. Total fecundity of T. recki significantly differed between the constant temperatures and the highest was observed at 20 °C (28.6 eggs/female) and 25 °C (23.5 eggs/female). Although females survived for 26 days at 35 °C, only four females laid eggs for 1 day. Thus, 35 °C is not suitable for the reproduction of T. recki. Total longevity of female was the longest at 15 °C (59.5 days) and 20 °C (51.3 days) whereas male total longevity was the longest at 15 °C (53.2 days). The intrinsic rate of increase (r) and finite rate of increase (λ) were numerically the highest at 30 °C (0.17 and 1.18 d-1), but there was no statistical difference observed between 25 and 30 °C for both r and λ values. The net productive rate was significantly highest at 20, 25 and 30 °C. The longest mean generation time occurred at 15 °C (42.6 days) and the shortest was at 35 °C (8.0 days). After some field trials, Typhlodromus recki could potentially be considered in the biological control agent of T. urticae.
Study of the effect of alcohol on single human red blood cells using near‐infrared laser tweezers Raman spectroscopy

The effect of alcohol solution on single human red blood Cells (RBCs) was investigated using near-infrared laser tweezers Raman spectroscopy (LTRS). In our system, a low-power diode laser at 785 nm was applied for the trapping of a living cell and the excitation of its Raman spectrum. Such a design could simultaneously reduce the photo-damage to the cell and suppress the interference from the fluorescence on the Raman signal. The denaturation process of single RBCs in 20% alcohol solution was investigated by detecting the time evolution of the Raman spectra at the single-cell level. The vitality of RBCs was characterized by the Raman band at 752 cm(-1), which corresponds to the porphyrin breathing mode. We found that the intensity of this band decreased by 34.1% over a period of 25 min after the administration of alcohol. In a further study of the dependence of denaturation on alcohol concentration, we discovered that the decrease in the intensity of the 752 cm(-1) band became more rapid and more prominent as the alcohol concentration increased. The present LTRS technique may have several potential applications in cell biology and medicine, including probing dynamic cellular processes at the single cell level and diagnosing cell disorders in real time. Copyright (c) 2005 John Wiley T Sons, Ltd.
Modeling side chains in peptides and proteins: Application of the locally enhanced sampling and the simulated annealing methods to find minimum energy conformations

A new optimization protocol is proposed which is based on a combination of a mean field approximation and simulated annealing. Instead of optimizing the energy of the real system the energy of a new mean field system is minimized. The global minimum of the new system and the original system is the same. The mean‐field optimization is advantageous to the optimization of the real system since (a) More statistics are obtained for alternative solutions and (b) the barrier heights separating the minima are reduced compared to the real system. Computational examples are provided for placement of side chains in tetrapeptides and in a small protein BPTI.
Kinetics of Liquid Drainage from Protein‐Stabilized Foams

Drainage of 14 protein-stabilized foams was determined as a function of time. Drainage conformed to the empirical equation v = Vt/(B + t), where v is the volume of drained liquid at time t, V is the maximum drained volume, and B is the time needed to drain V/2. Rate constants and initial rates of drainage could be calculated from parameters V and B. However, B would be the preferred index of foam stability as it was not influenced by the initial volume of liquid in the foam.
Get Green or Die Trying? Carbon Risk Integration into Portfolio Management

Portfolio management is confronting climate change more strongly and rapidly than expected. Risks arising from the transition from a brown, carbon-based to a green, low-carbon economy need to be integrated into portfolio and risk management. The authors show how to quantify these carbon risks by using a capital markets–based approach. Their measure of carbon risk, the carbon beta, can serve as an integral part of portfolio management practices in a more comprehensive way than fundamental carbon risk measures. Apart from other studies, the authors demonstrate that both green and brown stocks are risky per se, but there is no adequate remuneration in the financial market. In addition, carbon risk exposure is correlated with exposures to other common risk factors. This requires due diligence when integrating carbon risk in investment practices. By implementing carbon risk screening and best-in-class approaches, the authors find that investors can gain a desired level of carbon risk exposure, but this does not come without well-hidden costs. TOPICS: Portfolio construction, ESG investing, risk management Key Findings ▪ We describe a capital markets–based approach to measuring carbon risk that stems from the transition from a carbon-based to a low-carbon economy. ▪ Investors can integrate carbon risk into their portfolios using carbon beta but must be aware of the associated portfolio return, risk, and factor exposures. ▪ Different portfolio strategies can achieve a desired level of carbon risk exposure. However, the risk is not remunerated proportionally in the financial market.