Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Concentration of Azadirachtin Has the Same Toxic Effect as Imidacloprid + Lambda-Cyhalothrin in Workers of Two Species of Leaf-Cutter Ants

Abstract. Leaf-cutter ants of the genus Atta are pests controlled by broad-spectrum insecticides that adversely affect the environment and human health. Toxicity of 20 concentrations of chemical imidacloprid + λ-cyhalothrin and biological insecticides azadirachtin and spinetoram were evaluated on workers of A. mexicana and A. cephalotes ants. Initial evaluation determined optimal concentrations to kill 10-90%. Mean lethal concentrations of imidacloprid and azadirachtin were determined, and mean lethal times were through 3 × 10-1 µl/liter concentration of each insecticide. Azadirachtin and imidacloprid + λ-cyhalothrin at 3 × 10-1 µl/liter concentration killed 100% of both species. LC50 values of imidacloprid + λ-cyhalothrin and azadirachtin were similar against both species. But, the TL50 of imidacloprid + λ-cyhalothrin was less (28-29 hours) in both species compared to the TL50 by azadirachtin that was 36 to 41 hours. Insecticide based on extracts of neem (azadirachtin) was as toxic as imidacloprid + λ-cyhalothrin against A. mexicana and A. cephalotes worker ants. Azadirachtin is an environmentally friendly alternative to manage the two ant species, and probably other species of Atta and other ant pests.
Morphological Reanalysis of the Pedicularis sudetica Complex of Arctic North America: Character Trimming with Occam's Razor

Abstract Pedicularis sudetica can be a difficult complex to identify and classify using herbarium specimens. Colors fade, rendering corolla patterns indistinct. Especially problematic is the presence of spots on the lobes of the lower lip and color patterns of the galea. Spots are either clearly visible or so faded that only a few spots are visible on one or two flowers in the inflorescence. Galeas are either unequivocally bicolored, solid, or indistinctly diffuse. Other characters have been inconsistently applied by taxonomists and/or are difficult to discern. All these factors combined only serve to confound taxon identity and classification, and members of the complex have been classified as species, subspecies, or even varieties by different taxonomists. Multiple correspondence analysis (MCA) of qualitative traits and principal component analysis (PCA) of quantitative traits were employed to explore the character and taxonomic structure of the group. MCA revealed that only three characters are necessary to classify the four taxa of the complex. No taxon structure was evident with PCA. Abundance of intermediate specimens over a broad geographic range indicates hybridization is extensive among members of the complex and suggests that a subspecific classification of the group is appropriate.
Molecular Systematics, Species Concepts, and Myrmecophytism in Cecropia (Cecropieae: Urticaceae): Insights from Restriction-Site Associated DNA

Abstract Cecropia is a group of fast-growing pioneer trees that are important in forest regeneration and a common ant-plant mutualism in the Neotropics. To investigate the evolution of mutualism between Cecropia and associated ants, a phylogenetic framework is necessary. Cecropia species are difficult to distinguish morphologically and conventional genetic markers are insufficiently variable to resolve the phylogenetic relationships among species. Our study aimed to compare the phylogenetic utility of restriction site associated DNA (RAD) sequencing to prior work based on commonly sequenced gene regions. RAD sequence data resolved and supported species-level relationships better than previous studies. We identified a deeply divergent non-myrmecophytic clade including C. sciadophylla and African Musanga. Results from geographically widespread and morphologically heterogenous C. obtusifolia and C. angustifolia suggest that current synonymy has lumped phylogenetically divergent lineages. Reconstruction of ant associations on the highly supported Cecropia phylogeny inferred equal probability of the ancestor of Cecropia being myrmecophytic or not. More intensive genetic study is needed to refine species concepts in Cecropia.
Non-invasively Imageable Tibia-tumor-fragment Implantation Experimental-bone-metastasis Mouse Model of GFP-expressing Prostate Cancer

Background/Aim: Although the 5-year survival rate for localized prostate cancer is nearly 100%, prognosis for patients with metastases, of which the bone is the most common site, is poor. In order to evaluate efficacy of treatments against metastatic prostate cancer, experimental tibia-bone-metastasis mouse models of prostate cancer have been previously established. In the present study, we used a novel procedure for establishment of an experimental tibiabone metastasis mouse model, with human PC-3 prostate cancer expressing green fluorescent protein (GFP), that more closely matches prostate cancer growing in the bone. Materials and Methods: PC-3 human prostate cancer cells, labeled with GFP, were initially subcutaneously injected into the flank of five male nude mice to obtain tumor tissues. Once the tumor tissue grew larger than 10 mm in diameter, the tumor tissue was harvested and minced into fragments of 1 mm3. A 1-mm hole was made in the proximal left tibia of eight male nude mice, using the tip of a 5-mm blade, and a tumor fragment was implanted into the hole for an exact fit. Tumor size was measured once a week, by non-invasive imaging of GFP fluorescence. The mice were sacrificed four weeks after tumor implantation. Results: Tumors grew in 8 out of 8 mice (100%). All tumors were non-invasively detectable with GFP fluorescence, through the skin. Increased tumor growth in the tibia was observed every week. Conclusion: The establishment in the tibia of the novel experimental bone-metastatic mouse model of human prostate cancer enables facile screening, in a clinically-relevant system, of improved therapeutics for this recalcitrant disease.
A Relative Information Gain-based Query Performance Prediction Framework with Generated Query Variants

Query performance prediction (QPP) methods, which aim to predict the performance of a query, often rely on evidences in the form of different characteristic patterns in the distribution of Retrieval Status Values (RSVs). However, for neural IR models, it is usually observed that the RSVs are often less reliable for QPP because they are bounded within short intervals, different from the situation for statistical models. To address this limitation, we propose a model-agnostic QPP framework that gathers additional evidences by leveraging information from the characteristic patterns of RSV distributions computed over a set of automatically generated query variants, relative to that of the current query. Specifically, the idea behind our proposed method—Weighted Relative Information Gain (WRIG), is that a substantial relative decrease or increase in the standard deviation of the RSVs of the query variants is likely to be a relative indicator of how easy or difficult the original query is. To cater for the absence of human-annotated query variants in real-world scenarios, we further propose an automatic query variant generation method. This can produce variants in a controlled manner by substituting terms from the original query with new ones sampled from a weighted distribution, constructed either via a relevance model or with the help of an embedded representation of query terms. Our experiments on the TREC-Robust, ClueWeb09B, and MS MARCO datasets show that WRIG, by the use of this relative changes in QPP estimate, leads to significantly better results than a state-of-the-art baseline method that leverages information from (manually created) query variants by the application of additive smoothing [64]. The results also show that our approach can improve the QPP effectiveness of neural retrieval approaches in particular.
Survey on Aspect Category Detection

In recent years, aspect category detection has become popular due to the rapid growth in customer reviews data on e-commerce and other online platforms. Aspect Category Detection, a sub-task of Aspect-based Sentiment Analysis, categorizes the reviews based on the features of a product such as a laptop’s display or an aspect of an entity such as the restaurant’s ambiance. Various methods have been proposed to deal with such a problem. In this article, we first introduce several datasets in the community that deal with this task and take a closer look at them by providing some exploratory analysis. Then, we review a number of representative methods for aspect category detection and classify them into two main groups: (1) supervised learning and (2) unsupervised learning. Next, we discuss the strengths and weaknesses of different kinds of methods, which are expected to benefit both practical applications and future research. Finally, we discuss the challenges, open problems, and future research directions.
Acting emotions: physiological correlates of emotional valence and arousal dynamics in theatre

Professional theatre actors are highly specialized in controlling their own expressive behaviour and non-verbal emotional expressiveness, so they are of particular interest in fields of study such as affective computing. We present Acting Emotions, an experimental protocol to investigate the physiological correlates of emotional valence and arousal within professional theatre actors. Ultimately, our protocol examines the physiological agreement of valence and arousal amongst several actors. Our main contribution lies in the open selection of the emotional set by the participants, based on a set of four categorical emotions, which are self-assessed at the end of each experiment. The experiment protocol was validated by analyzing the inter-rater agreement (> 0.261 arousal, > 0.560 valence), the continuous annotation trajectories, and comparing the box plots for different emotion categories. Results show that the participants successfully induced the expected emotion set to a significant statistical level of distinct valence and arousal distributions.
Occupant-oriented economic model predictive control for demand response in buildings

The present paper develops an Economic Model Predictive Control (EMPC) framework to provide Demand-Response (DR) for supporting the power grid stability while also maintaining Occupants' Thermal Satisfaction (OTS) in buildings. Our controller combines economic and occupant-oriented aspects by simultaneously optimizing two conflicting control goals, namely grid stability and OTS in buildings. We represent grid stability with Grid Costs (GC) based on a real-world dynamic electricity price and OTS with a reference indoor temperature, respectively. In the literature, there exists no study about occupant-oriented DR where the Model Predictive Control (MPC) is based on Resistor-Capacitor (RC) models identified from real measurements that also includes an attendance schedule for DR. For this, the EMPC uses a grey-box thermal building model that is designed, identified, and validated with real-world measurement data. For evaluation, we compare the EMPC with a well-tuned conventional Proportional-Integral (PI) controller. The results show that the EMPC significantly outperforms the PI controller in terms of GC, while it respects OTS.
POET: towards power-system-aware e-taxi coordination under dynamic passenger mobility

Electric vehicle (EV) fleets, e.g., electric taxis, buses, and trucks, have been increasingly implemented in cities. Compared with internal-combustion vehicles, the operation of EV fleets requires resilient charging infrastructures. Notably, the charging of EV fleets can bring significant reliability and efficiency challenges to the operation of the underlying power system. Based on the real data collected from New York City (NYC), our analysis shows that an increased power load ramping can result from various charging behaviors of electric taxis (e-taxis). Additionally, the increase of the peak load can further overload the local power distribution network. To address these problems, we exploit the possibility of utilizing e-taxi fleets' mobility to improve the reliability and reduce the cost of the power system, while maintaining the taxi service quality. Specifically, we design POET, a POwer-system-aware E-Taxi coordination algorithm, and evaluate the solution with comprehensive datasets for taxis and power distribution systems from NYC. These data include (i) more than 13,000 taxis with more than ten million taxi trips per month, (ii) a city local power distribution network with 38 local area substations and nearly 45 GWh overall power consumption per day, and (iii) deployed EV charging stations. The extensive data-driven evaluation demonstrates that, compared with the existing e-taxi charging solution that focuses on optimizations of taxi service quality, our solution decreases the power load ramping of local regions by 22.3% and reduces the daily peak charging load by 44.2% while achieving almost the same taxi revenue.
Predicting Phishing Victimization: Roles of Protective and Vulnerable Strategies and Decision-Making Styles

Phishing is a common vector for cybercrime and hacking. This research examines participants' personality styles (e.g. decision-making styles, self-control) and the likelihood of falling victim to phishing attacks. Over 300 participants completed an online survey assessing protective and vulnerable strategies, personality styles, trust in people, prior victimization from catphishing or identity theft, and demographics information. Unbeknownst to the participants, 2 to 4 weeks after completing the survey they received a phishing e-mail asking them to click on a link. Individuals with a stronger systematic decision-making style were more likely to have a greater number of protective strategies, and those with greater protective strategies were less likely to be a victim of catphishing and identity theft. Individuals with low avoidant decision-making styles and prior vulnerable strategies were more likely to be phished. These findings suggest that learning protective strategies and not using vulnerable strategies are insufficient to lower substantially the risk of being phished. Training might be improved through considering the match between decision-making styles and the content of the training.
Toward More Efficient Statistical Debugging with Abstraction Refinement

Debugging is known to be a notoriously painstaking and time-consuming task. As one major family of automated debugging, statistical debugging approaches have been well investigated over the past decade, which collect failing and passing executions and apply statistical techniques to identify discriminative elements as potential bug causes. Most of the existing approaches instrument the entire program to produce execution profiles for debugging, thus incurring hefty instrumentation and analysis cost. However, as in fact a major part of the program code is error-free, full-scale program instrumentation is wasteful and unnecessary. This article presents a systematic abstraction refinement-based pruning technique for statistical debugging. Our technique only needs to instrument and analyze the code partially. While guided by a mathematically rigorous analysis, our technique is guaranteed to produce the same debugging results as an exhaustive analysis in deterministic settings. With the help of the effective and safe pruning, our technique greatly saves the cost of failure diagnosis without sacrificing any debugging capability. We apply this technique to two different statistical debugging scenarios: in-house and production-run statistical debugging. The comprehensive evaluations validate that our technique can significantly improve the efficiency of statistical debugging in both scenarios, while without jeopardizing the debugging capability.
Non-intrusive load monitoring: a promising path to the society for responsible energy utilization and sustainability

Responsible use of energy resources, conserving environment and improving well-being of the society are the major goals towards developing smart sustainable cities, and energy management is the inherent part of the smart sustainable cities programme. Due to high demand of energy in various sectors, energy management has become essential to utilize the available energy efficiently and understand energy usage behavior of the users in order to avoid possible energy wastage. In recent years, non-intrusive load monitoring (NILM) technique has become popular and emerging approach to monitor events and energy consumption of appliances/electrical utilities in buildings using single energy meter. The information about energy consumption at appliance level would help consumers to understand their appliance usage behavior and take necessary steps for reducing energy consumption. The recent advancements in smart metering and internet of things (IoT) have made significant progress in real time implementation of non-intrusive load monitoring (NILM) algorithms in residential and industrial environments. The motivation of this presentation is to address the challenges towards developing more accurate NILM systems and its future perspectives for effective energy management in buildings. NILM can also be used for anomaly detection, predictive maintenance, load forecasting, and energy disaggregation of distributed energy resources in buildings. Therefore, creating awareness about the importance of NILM and incorporating its potential applications as the features of NILM system would help achieve responsible energy utilization in residential/industrial environments and conserve energy consumption towards developing smart sustainable cities for the society.
Travel Time Prediction Method Based on Spatial-Feature-based Hierarchical Clustering and Deep Multi-input Gated Recurrent Unit

Accurate travel time prediction (TTP) is a significant aspect in the intelligent transportation system (ITS). Travel times of certain road segments explicitly reflect the traffic conditions of those sections. Effective TTP of road segments is instrumental in route planning, traffic control, and traffic management. However, the accuracy of TTP is greatly affected by the intricate topological structure of traffic network and the dynamics of traffic flow over time. This paper develops a TTP method based on the spatial-feature-based hierarchical clustering (SFHC) and deep multi-input gated recurrent unit (DMGRU). The proposed two-stage method is capable of capturing the spatial-temporal features of traffic network. Specifically, the SFHC divides the road segments into several clusters having similar traffic features, and then the clustered data is fed into the DMGRU for TTP. Our experiments conducted on the practical dataset demonstrate that the designed prediction method can achieve the mean absolute percentage error (MAPE) of 3.3109% and mean absolute error (MAE) of 2.5658, which outperform various combinations of baseline clustering algorithms and prediction models.
Beobench: a toolkit for unified access to building simulations for reinforcement learning

Reinforcement learning (RL) is often considered a promising approach for controlling complex building operations. In this context, RL algorithms are typically evaluated using a testing framework that simulates building operations. To make general claims and avoid overfitting, an RL method should be evaluated on a large and diverse set of buildings. Unfortunately, due to the complexity of creating building simulations, none of the existing frameworks provide more than a handful of simulated buildings. Moreover, each framework has its own particularities, which makes it difficult to evaluate the same algorithm on multiple frameworks. To address this, we present Beobench: a Python toolkit1 that provides unified access to building simulations from multiple frameworks using a container-based approach. We demonstrate the power of our approach with an example showing how Beobench can launch RL experiments in any supported framework with a single command.
Analytical uncertainty propagation and storage usage in a high RES Turkish transmission grid scenario

The rapid integration of renewable energy into power systems calls for thorough system analysis, for which optimal power flow methods are used. However, these optimal power flow methods are mostly evaluated on test grids instead of real-world power grids. In this paper, we provide a model of the real-world Turkish transmission grid to allow more realistic modelling and test a newly proposed optimal power flow method, a second-order cone problem formulation, on this Turkish network. Furthermore, we combine the network with real-world wind power data to investigate the influence of storage placement on power flows. Our results show that the proposed optimal power flow method can be used to investigate the optimal placement of storage. On the Turkish transmission grid, storage (as expected) leads to a more stable and predictable generation and is best placed close to demand. Thus storage can play an essential role as a flexibility provider in Turkey.
EECDN: Energy-efficient Cooperative DNN Edge Inference in Wireless Sensor Networks

Multi-access edge computing (MEC) is emerging to improve the quality of experience of mobile devices including internet of things sensors by offloading computing intensive tasks to MEC servers. Existing MEC-enabled cooperative computation offloading works focus on the optimization of total energy consumption but fail to exploit multi-relay diversity and min-max fairness of energy consumption on participated sensors. We explore a typical wireless sensor network with multi-source, multi-relay, and one edge server, where relay nodes can provide both cooperative communication and computation services. We divide the energy efficiency optimization problem into two sub-problems: One is to minimize the weighted average total energy consumption per time slot, and the other is to minimize the maximum weighted energy consumption. For the first sub-problem, we propose an optimal algorithm named as optimal weighted average total energy consumption algorithm (OTCA) based on bipartite matching. For the second sub-problem, greedy algorithm for fairness guarantee (GAF) is proposed with an approximation ratio of (1 + ε), where ε is a small positive constant. Extensive numerical results show that OTCA outperforms the baseline algorithms by 26.7–77.4% on the average total weighted energy consumption while GAF outperforms benchmark algorithms by 30.7–84.4%. NS-3 simulation experiments comply with numerical results.
Peripheral Light Cues as a Naturalistic Measure of Focus

Deeply immersive experiences are intrinsically rewarding; evoking them for another is a cornerstone of success in artistic or design practice. At the same time, modern interfaces have created a state of ’partial continuous attention’, and frequent self-interruption is more common than ever. In this paper, we propose a smart-glasses based interaction to quantify self-interruption dynamics in naturalistic settings, in which a slowly changing peripheral LED is monitored as a secondary task by the user. We demonstrate that this interaction captures useful information about a user’s state of engagement in real-world conditions. These data can provide designers and artists novel, objective insight into the depth of immersive experience evoked in real-world settings.
A GA-BP neural network for nonlinear time-series forecasting and its application in cigarette sales forecast

Abstract Neural network modeling for nonlinear time series predicts modeling speed and computational complexity. An improved method for dynamic modeling and prediction of neural networks is proposed. Simulations of the nonlinear time series are performed, and the idea and theory of optimizing the initial weights and threshold of the GA algorithm are discussed in detail. It has been proved that the use of GA-BP neural network in cigarette sales forecast is 80% higher than before, and this method has higher accuracy and accuracy than the gray system method.