Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase space formalism for quantum estimation of Gaussian states

We formulate, with full generality, the asymptotic estimation theory for Gaussian states in terms of their first and second moments. By expressing the quantum Fisher information (QFI) and the elusive symmetric logarithmic derivative (SLD) in terms of the state's moments (and their derivatives) we are able to obtain the noncommutative extension of the well known expression for the Fisher information of a Gaussian probability distribution. Focusing on models with fixed first moments and identical Williamson 'diagonal' states --which include pure state models--, we obtain their SLD and QFI, and elucidate what features of the Wigner function are fundamentally accessible, and at what rates. In addition, we find the optimal homodyne detection scheme for all such models, and show that for pure state models they attain the fundamental limit.
Research on SDN Fingerprint Attack Defense Mechanism Based on Dynamic Disturbance and Information Entropy Detection

As an emerging type of network architecture, SDN is widespread used and security issues have also received more and more attention. Fingerprint attacks represent one of the most significant threats to network security. Attackers obtain key fingerprint information of the target network, which lays the foundation for subsequent more threatening attacks. Currently, research on domestic and international SDN fingerprint attacks focuses on how to attack, and less research is being done on how to defend against fingerprinting attacks. This paper proposes a mechanism for defending fingerprint attacks that combines dynamic disturbance and information entropy detection. This mechanism adopts the principle of fingerprint attack, combined with a moving average algorithm, Bloom Filter, and packet delay tool, to confuse opponents by disturbing a small number of packets, simultaneously, combined with the information entropy detection to make real-time processing feedback to the network. The experimental results show that this mechanism works effectively to defend SDNs against fingerprint attacks without affecting the normal network communication.
Aquatic islands in the sky: 100 years of research on water‐filled tree holes

Abstract Water‐filled tree holes are unique ecosystems that may occur high up in tree crowns and are essentially aquatic islands in the sky. Insect larvae, mesofauna, and other organisms colonize the waterbodies and feed on the accumulating detritus. Water‐filled tree holes are not only important habitats for these species but have been used as model systems in ecology. Here, we review more than 100 years of research on tree‐hole inhabiting organisms and show that most studies focus on selected or even single species (most of which are mosquitoes), whereas only few studies examine groups other than insects, especially in the tropics. Using a vote counting of results and a meta‐analysis of community studies, we show that the effects of tree‐hole size and resources on abundance and richness were investigated most frequently. Both were found to have a positive effect, but effect sizes were modulated by site‐specific environmental variables such as temperature or precipitation. We also show that parameters such as the height of the tree holes above ground, tree‐hole density, predation, and detritus type can be important drivers of organism abundance or richness but are less often tested. We identify several important research gaps and potential avenues for future research. Specifically, future studies should investigate the structure, functions, and temporal dynamics of tree‐hole food webs and their cross‐system interactions, for example, with terrestrial predators that act as a connection to their terrestrial surroundings in meta‐ecosystems. Global observational or experimental tree‐hole studies could contribute pivotal information on spatial variation of community structure and environmental drivers of community assembly. With a better understanding of these unique aquatic habitats in terrestrial ecosystems, natural and artificial tree holes can not only serve as model systems for addressing fundamental ecological questions but also serve as indicator systems of the impacts of environmental change on ecosystems.
Online learning stochastic model predictive control of linear uncertain systems

This article presents an online learning stochastic model predictive control method for linear uncertain systems with state‐dependent additive uncertainties, where the uncertainty is modeled as Gaussian process. The proposed scheme utilizes the probabilistic reachable sets as time‐varying tubes, which are formulated by forecasting the variance propagation of uncertainty via Gaussian process regression, to embody the chance constraints. The proposed learning based stochastic model predictive control algorithm is designed by refining the active data dictionary to train the Gaussian process model of uncertainty online. In particular, the data points in the active data dictionary are selected from the raw data around the predicted optimal nominal trajectories, which reduces the computational load as well as preserves the control performance. Then the algorithm feasibility and closed‐loop stability of the developed algorithm are analyzed. Finally, the efficacy and superiority over existing methods are verified by simulation studies.
In silico molecular docking study of phytochemicals obtained from Holigarna caustica (Dennst.) for cancer treatment

Holigarna caustica, commonly known in Bangladesh as "Katebel," is used in traditional medicine to treat tumors, malignancies, skin problems, obesity, inflammation, eye irritation, and arthritis. The goal of this work was to use computational models like molecular docking to find the bioactive phytochemicals in this plant that are responsible for the anticancer potential. The molecular docking experiment was carried out using Glide of Schrödinger Maestro (version 10.1). Our computational research revealed that a total of eighteen phytocompounds may be responsible for the plant's anticancer properties, which should be further studied in experimental models.
Indirect buffer-based laser pretreatment method for enhancing the adhesive bonding of carbon fiber-reinforced plastic

Abstract. The bonding technology of carbon fiber-reinforced plastic (CFRP) is a key technology in its production. Laser pretreatment is a common method for improving the bonding strength. However, the use of laser pretreatment without damaging the carbon fibers has always been challenging. A laser pretreatment method is proposed to improve the bond strength of CFRP without damaging the carbon fibers. The proposed method increases the tensile shear strength of CFRP from 2448 to 4851 N; 9.79 to 19.41 MPa. The naked carbon fiber treated by buffer laser pretreatment provides additional strength to the glue. Scanning electron microscopy was used to image the surfaces of the samples. Failure modes were also investigated to explain the increase in bond strength. The benefits of this method can be explained using a thermal model, and this method can be used as a reference for the laser processing of CFRPs.
Stabilizability of SISO control systems under constraints of channel capacities

In this paper, we deal with control systems where plants and controllers are connected with transmission channels of signals, and analyze their stabilizability under the constraints of the channel capacity. We show that this problem is resolved with a minimization problem of a kind of entropy of the related systems under some asymptotic conditions, and the stabilizability can be represented by the unstable poles and real zeros of the plants. This result corresponds to a synthesis version of Bode's theorem and an extended version of the strong stabilizability of plants. We also show a design method of the optimal controller.
Mean Square Consensus of General Linear Multiagent Systems with Communication Noises under Switching Topologies

This paper investigates the distributed consensus problem of general linear multiagent systems (MASs) with communication noises under fixed and Markovian switching topologies, respectively. Each agent can obtain full state of itself and receive its neighbors’ state with noises, where intensities of noises are vector functions of relative states of agents. Bearing in mind the above constrains, a consensus protocol is proposed, where the gain matrix is obtained by the algebraic Riccati equation and the coupling strength is restricted in a given interval. By using the stochastic stability theorem, we show that mean square consensus is achieved in fixed topology case and switching topologies case, respectively. Furthermore, an estimation of the exponential convergence rate of consensus is given explicitly. Finally, simulation examples are given to show the correctness of the proposed results.
Data-Driven Decision Making In Large Scale Production Planning

Production planning is a very important part for a company in making the right decisions before carrying out production activities in order to obtain maximum profit with a minimum level of production costs. Production planning is defined as a process in producing goods and services within a certain period by considering resources such as labor, materials, machinery and etc. In this research, a production planning model is produced based on several variables and parameters that can assist in making production decisions
Predicting responses of V1 neurons to natural movies

Response properties of visual cortical neurons have been extensively studied using a variety of stimuli, ranging from moving bars of light to images of complex natural scenes. One approach has been to characterize the conditional probability distribution that a neuron spikes given the presentation of a particular stimulus X , P (spike|X = x). One functional class for this distribution is the linear-nonlinear Poisson (LNP) cascade receptive field (RF) model where P (spike|x) = f(Kx) with kernel K ∈ Rm×n, stimulus x ∈ Rn, and output nonlinearity f : R → [0, 1]. Here it is assumed m n with kernel K projecting the stimulus space onto a much smaller ‘relevant’ subspace1. This model was used to estimate simple and complex cell spatiotemporal RFs using artificial stimuli oriented in the preferred direction of the cell2,3 and spatial RFs using random frames taken from natural movies4.
The Role of Empathy, Customer Orientation and Work Engagement in the Relationship Between Servant Leadership and Customer-Oriented Organizational Citizenship Behaviors

This work explores the relationship between servant leadership in the service context and the extent to which employees engage in customer-oriented organizational citizenship behavior (OCB). Data were collected at two time points from 158 full-time employees, working in16 branches of a large bank in Israel. The results show that there is a positive relationship between servant leadership and customer-oriented OCB and that this relationship is mediated by employees’ levels of customer orientation and work engagement. Likewise, the relationship between servant leadership and customer orientation is stronger among employees with low levels rather than high levels of empathy.
Peak Rain Rate Sensitivity to Observed Cloud Condensation Nuclei and Turbulence in Continental Warm Shallow Clouds During CACTI

Warm clouds strongly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to the complexity and covariability of relevant processes influencing warm rain. This work presents a detailed analysis of different factors affecting rain rate peak intensity (RR) in continental warm clouds. Clouds were identified with vertically pointing radar and lidar observations and categorized via a temperature‐based cloud type classification algorithm from which warm clouds were isolated. Observations and retrievals of liquid water path (LWP), cloud condensation nuclei concentration (NCCN), cloud depth, and cloud duration of more than 3,000 separate warm clouds sampled during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are analyzed in this work. Multiple linear regression (MLR) and random forest (RF) models are applied to assess the relative impact of these variables on RR. Overall, RR tends to increase as cloud depth, LWP, and cloud duration increase, or NCCN decreases. Cloud depth affects RR the most while NCCN impacts it the least. When considering over 170 warm clouds observed at least 1 hr in which in‐cloud turbulence is retrieved, the effect of NCCN on RR remains most likely suppressive, but it is not significant at a 75% level for MLR and is highly uncertain for RF. The impact of in‐cloud turbulence depends on the moment and location it is sampled. Cloud base turbulence around the time of RR suppresses RR, while cloud top turbulence effects are inconclusive. Possible difficulties in isolating robust CCN and turbulence effects on RR are discussed.
Moment-to-Moment Observation of Parental Media Use and Parent-Child Interaction: Quality and Media Multitasking

Mobile media proliferation throughout society has infused and complicated environments that formerly were interaction rich (e.g., waiting rooms, restaurants, and playgrounds) with the presence of smart devices. Ethnographic studies have indicated that parental use negatively impacts parent-child interaction quality. The current study reviews and expands on previous research through observing systematically parent-child interaction quality throughout the course of an entire meal (30-140 minutes). Utilizing five-minute intervals, across 93 parent-child dyads, we assessed both within- and between-person moment-to-moment changes in parenting quality (i.e., parental positivity, negativity, and engagement) in the context of parental media use. Between-person, only positivity appeared to decrease when comparing low and high parental media use. Within-person findings indicated that when the parent demonstrated higher than their typical media use, we noted a significant decrease in the quality of engagement and positivity. Differing from ethnographic studies, no change in negativity was identified within-person. Utilizing a lagged interval analysis, we identified a pattern of increased parental engagement with their child following intervals with parental media use, identifying a pattern of parental media multitasking heretofore only observed in ethnographic studies. Implications of findings in the context of previous research and future directions are discussed.
Protocol for systematic review and meta-analysis on the effect of natural products use on blood pressure in Iran

Background: Today, hypertension is a significant public health problem globally, widely recognized as a major risk factor for death. Blood pressure-lowering herbal medicines and natural products have been used for centuries. The protocol aims to determine the effect of natural product use on blood pressure in Iran using data from previously published randomized controlled trials. The current protocol for a systematic review and meta-analysis is designed to determine the effect of natural product use on blood pressure in Iran. Methods: The protocol is developed using PICO (participants, interventions, comparisons, and outcomes) items to assess the effect of natural products on blood pressure reduction in randomized clinical trials (RCTs) in Iran. Specific MESH terms will be used to search Google Scholar, PubMed, and the Cochrane Central Register for randomized controlled clinical trials, as well as national databases such as Barekat Gostar, SID, Magiran, and IranDoc. The I2 index and the Q-test will be used to examine heterogeneity in the effect sizes of individual studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of RCTs will be reported per Cochrane guidelines, and all forms will be based on validated Cochrane templates. Discussion: This protocol will detail the effects of natural products (interventions) in comparison to a placebo or other control group (comparators). Registration: The current protocol was also registered on PROSPERO (ID: CRD42021231837, https://www.crd.york.ac.uk/prospero/display_record.php? RecordID=231837, Date: February 18, 2021).
The Value of Medicaid Long-Term Care: Evidence from the Deficit Reduction Act of 2005

Public long-term care (LTC) insurance helps the elderly protect against financial risks, yet its value is hard to measure. This paper provides a novel answer to this question by employing a quasi-experiment from the Deficit Reduction Act (DRA) that restricts seniors’ Medicaid LTC access. I find that in response to the DRA, single elderly individuals reduced their home equity by $66.75K (12.1%), while increasing non-LTC consumption by $10.5K (22.5%). Using these findings and a two-stage budgeting model, I then estimate that seniors’ willingness to pay for Medicaid LTC is $1.2 per dollar of its net resource cost. This evidence shows that the government’s intention to limit the provision of Medicaid LTC harms social welfare. JEL classification: H41, H53, I13, I18, J14, J18
Quantifying Luhmann: A Semi-Supervised Approach to the Automatic Detection of Social Systems

Social systems theory is a sociological framework that di erentiates society based on functional aspects: Each subsystem of society, such as politics, economy, or science, performs a specific and well-delineated function. Primarily developed by German sociologist Niklas Luhmann, it is one of the most influential theories in modern (qualitative) sociology. Yet, it is notorious for its complexity and has rarely been applied in quantitative research thus far. The central question of this thesis therefore is whether it is possible to operationalize Luhmanns’ social systems theory using computational methods to automatically recognize and attribute systems to text data. The proposed solution follows a two-step process: first, a set of descriptive terms is obtained from a corpus of Niklas Luhmann’s primary literature. Each social system is associated with a particular set of twenty terms that semantically describe the nature of a system. Then, a generative semi-supervised classification method, the Seed-guided Multi Topic Model by Zha & C. Li (2019), is applied to categorize documents according to Luhmann’s proposed taxonomy for social systems. A full ablative study investigates the problem at hand on four di erent datasets. This includes a replication of previous work, a novel scalability assessment, and the detection of social systems in two di erent setups: in-domain, i.e., the detection of systems in Luhmann’s own texts, and cross-domain, i.e., the transfer of the seed term set to detect systems in a subset of the German Wikipedia. While the in-domain setup yields excellent results, both in classification performance and in the usability of the model for downstream tasks, demonstrating the validity and suitability of the method, the results in the cross-domain setup are of mixed quality, suggesting future investigations into the behavior of the model for domain transfer.
PERFORMANCE IMPROVEMENTS OF BIGBLUEBUTTON FOR DISTANCE TEACHING

BigBlueButton (BBB) is a web conferencing system designed for online learning. It consists of a set of pre-configured open-source software tools to realize video conferencing functionality primarily for teaching purposes. Due to the COVID-19 pandemic, our university decided to roll out BBB for the university's educational activities in the first nationwide lock-down in early 2020. Based on our experiences in deploying, operating, and using BBB at our university for about 12 months, we present suggestions on how the services provided by BBB can be improved to meet the technical demands identified during online lecturing at our university. Our suggestions include the introduction of simulcast, improvements of encoding and muxing video feeds, and the 'Last -N' algorithm for video feed pagination. To demonstrate the benefits of the presented improvements, we experimentally evaluated most of them based on our own prototypical implementations.
Efficiently Compiling Dynamic Code for Adaptive Query Processing

Query optimization is one of the key challenges in modern database systems. With sophisticated data processing techniques like query compilation or vectorization, a good execution plan is essential for high performance. Yet, finding the optimal implementation for a query upfront is difficult as optimizers must rely on cardinality estimations and coarse cost models for the underlying hardware. Adaptive Query Processing overcomes these limitations: We evaluate different implementations for the same query during execution and choose the best-performing implementation based on accurate runtime measurements. However, compiling database systems cannot easily modify the generated code, and recompiling queries is prohibitively expensive. We propose a novel compilation technique — Dynamic Blocks — that avoids recompilations by embedding code fragments for all variants into the generated code. We integrate the approach into our research system Umbra and implement two dynamic optimizations for reordering joins and adapting selections during execution. Our results show that Adaptive Query Processing improves the runtime of data-centric code by more than 2×.
Returns to Job Search: Experimental Evidence from a Matching Platform *

Very preliminary. Please do not circulate without consulting the authors. Abstract Job search and matching platforms are becoming increasingly common but there is limited evidence about the returns to using them. We randomize whether users of a platform in Pakistan receive information about potential job matches through only text messages or both text messages and phone calls. Calling users raises their job application rate by 1.5 percentage points or 750% of the placebo group mean, relative to only texting them. Returns to these marginal applications, measured by job interview invitations, are roughly equal to returns to inframarginal applications that would be sent regardless of the treatment status. Spillover effects of additional applications on other applicants’ probabilities of getting job interviews are negligible. The effects on job applications and interviews are substantial for all com-binations of gender, baseline employment, and baseline search activity. This combination of findings suggests that search may be suboptimally low in this setting, for participants with a range of baseline levels of labor market engagement. is We discuss to and other in subsequent
Comparative analysis of cost-weighted site ranking using alternate distance-based neighboring structures for spatial crash frequency modeling

ABSTRACT There are significant cost differences between alternate crash outcomes based on severity levels. This study aims to expand existing literature with the inclusion of spatial correlations among a group of intersections to compare alternate distance-based weight matrices for cost-weighted hotspot identification (HSID) purpose. Multivariate-Poisson-lognormal-spatial (MVPLNS) method was employed to develop five crash prediction models (pure-distance and decay) to jointly estimate four severity levels (fatal and severe injury, other visible injury, complaint of pain, and noninjury). Model comparison for assessment of goodness-of-fit indicated that relatively subtle matrix structures assign consistent weights that reduce model complexity and eventually enhances the overall fit. The assessment of predictive accuracy of model estimates indicated that the model fit may be correlated with a superior performance at prediction as witnessed in the case of a pure-distance model that consistently exhibited least discrepancy from actual crash counts. The evaluation of HSID performance was based on rankings obtained from cost-weighted severities. The pure-distance models were overall superior at HSID, but among these models, the more subtle model performed significantly better, which hints at the presence of correlation between model fit and HSID performance as it may be possible that benefits of superior fit transfer to equivalent HSID capabilities.