Switching Probability Research Articles

Time-aware cross-domain point-of-interest recommendation in social networks

Point-of-Interest recommendation within the single domain is quite easy compared to the cross-domain recommendation, as there is an acute dearth of check-in records for the target regions, aggravating the cold start problem. We propose a self-ensembled contextual Thompson sampling for cross-domain Point-of-Interest recommendation to solve this. This approach utilizes user preference transfer and user preference drift in the target domain for enhanced recommendation by deploying enhanced contextual sampling. As the user-context pairs of the target domain are not labeled for the given user, domain adaptation is highly sought. The approach has four major steps: i) Mining Point-of-Interests based on the long-term preferences of the target user and the user with a similar trajectory in the source domain, ii) Computing rewards for the Point-of-Interests in the source domain using multi-layer perceptron, iii) Estimate the rewards for unlabeled Point-of-Interests in the target domain and iv) Form the ensemble of rewards that are used to decide the final arm pulls. The rewards obtained for Point-of-Interests in the source and target domain are combined to form an ensemble of rewards with the help of self ensembling domain adaptation technique. Each Point-of-Interest in the ensemble rewards is termed an arm of action. We use this ensemble of rewards to control the diversity measure and the switching probability of the various arms, potential Point-of-Interests, in the contextual Thompson Sampling. Contextual Thompson sampling is modified to incorporate exploitation-exploration tradeoffs using this reward ensemble. The implicit weight measure of the different arms decides the probability of exploitation or exploration. The final arm pulls results in the final Point-of-Interest recommendation. For experimentation, we have used two real-world datasets, namely, Gowalla and Foursquare, and extracted the data for seven domains. We have obtained an accuracy of approximately 65% for Point-of-Interest recommendations on cold-start users.

Correlations and Fractality in Sentence-Level Sentiment Analysis Based on VADER for Literary Texts

We perform a sentence-level sentiment analysis study of different literary texts in English language. Each text is converted into a series in which the data points are the sentiment value of each sentence obtained using the sentiment analysis tool (VADER). By applying the Detrended Fluctuation Analysis (DFA) and the Higuchi Fractal Dimension (HFD) methods to these sentiment series, we find that they are monofractal with long-term correlations, which can be explained by the fact that the writing process has memory by construction, with a sentiment evolution that is self-similar. Furthermore, we discretize these series by applying a classification approach which transforms the series into a one on which each data point has only three possible values, corresponding to positive, neutral or negative sentiments. We map these three-states series to a Markov chain and investigate the transitions of sentiment from one sentence to the next, obtaining a state transition matrix for each book that provides information on the probability of transitioning between sentiments from one sentence to the next. This approach shows that there are biases towards increasing the probability of switching to neutral or positive sentences. The two approaches supplement each other, since the long-term correlation approach allows a global assessment of the sentiment of the book, while the state transition matrix approach provides local information about the sentiment evolution along the text.

Automatic Apple Detection and Counting with AD-YOLO and MR-SORT.

In the production management of agriculture, accurate fruit counting plays a vital role in the orchard yield estimation and appropriate production decisions. Although recent tracking-by-detection algorithms have emerged as a promising fruit-counting method, they still cannot completely avoid fruit occlusion and light variations in complex orchard environments, and it is difficult to realize automatic and accurate apple counting. In this paper, a video-based multiple-object tracking method, MR-SORT (Multiple Rematching SORT), is proposed based on the improved YOLOv8 and BoT-SORT. First, we propose the AD-YOLO model, which aims to reduce the number of incorrect detections during object tracking. In the YOLOv8s backbone network, an Omni-dimensional Dynamic Convolution (ODConv) module is used to extract local feature information and enhance the model's ability better; a Global Attention Mechanism (GAM) is introduced to improve the detection ability of a foreground object (apple) in the whole image; a Soft Spatial Pyramid Pooling Layer (SSPPL) is designed to reduce the feature information dispersion and increase the sensory field of the network. Then, the improved BoT-SORT algorithm is proposed by fusing the verification mechanism, SURF feature descriptors, and the Vector of Local Aggregate Descriptors (VLAD) algorithm, which can match apples more accurately in adjacent video frames and reduce the probability of ID switching in the tracking process. The results show that the mAP metrics of the proposed AD-YOLO model are 3.1% higher than those of the YOLOv8 model, reaching 96.4%. The improved tracking algorithm has 297 fewer ID switches, which is 35.6% less than the original algorithm. The multiple-object tracking accuracy of the improved algorithm reached 85.6%, and the average counting error was reduced to 0.07. The coefficient of determination R2 between the ground truth and the predicted value reached 0.98. The above metrics show that our method can give more accurate counting results for apples and even other types of fruit.

Temporal Evolution Pathway and Forecasting of Non‐Fossil Energy Consumption and Carbon Emission Under China's Carbon Peak Target: A Markov Switching AR and RNN Approach

ABSTRACTTo fulfil the commitments of the Paris Agreement, China will strive to achieve carbon peak (CP) by 2030. It is necessary to identify the evolution characteristics of China's carbon emissions and provide a scientific path prediction for the formulation of reasonable emission reduction policies and measures. This study summarises and predicts the pathway of China's carbon peak (CP) using carbon emission intensity (CEI) and the percentage of non‐fossil energy consumption (NEC) as indicators, and combining MSIH(3)‐AR(2) model and recurrent neural network. The results show that: (1) China's CEI experiences a ‘low decline regime’ (LDR), a ‘medium decline regime’ (MDR) and a ‘high decline regime’ (HDR), while the share of NEC goes through a ‘low fluctuation regime’ (LFR), a ‘medium growth regime’ (MGR) and a ‘high growth regime’ (HGR). (2) For CEI, the switching probability from MDR to the HDR is 74.88%, illustrating a substantial improvement. For NEC, the switching probability from MGR to HGR is 28.92%, but the probability of returning to MGR is 61.76%, indicating an adjustment. (3) By 2030, CEI will reach 0.9896 tons/100 million CNY, decreased by 66.35% compared with 2005. While the percentage of NEC will rise to 26.61%. Based on these, policy suggestions such as strengthening the top‐level design, upgrading energy mix and accelerating green technological changes are proposed to break the bottlenecks of reaching CP and further zero carbon goal. This study is expected to provide theoretical support and empirical evidence for the achievement CP in China, and to provide empirical references for promoting the ‘dual carbon’ process in other countries.

Corporate bond coupon prediction based on deep learning

As the second largest bond market in the world, China's bond market has attracted extensive attention in recent years. Given its importance in facilitating financing arrangements and informing investment decisions, accurate bond coupon prediction is valuable. This paper proposes an ensemble model combining TabNet, DeepFM, and XGBoost for predicting the coupons of investment-grade corporate bonds. Specifically, to optimize the hyperparameters of the proposed model, an improved butterfly optimization algorithm incorporating the concepts of good point sets, refraction opposition-based learning, switching probability adjustment, and Solis & Wets search strategies is developed. Extensive experiments using data on China's investment-grade corporate bonds demonstrate the superior performance of the proposed model in the accuracy of bond coupon predictions. Additionally, the importance of various features has been discussed. The results show that the base interest rate for valuation and term to maturity are important to bond coupon predictions obtained by the proposed model.

Evaluation of Consumers’ Behaviour on Brand Loyaltyand Switching of Competitive Soda Products

This study examines the brand loyalty principle in three (3) bottling soda drinks on the basis of consumers’ choice based on cost, product quality and availability determinant factors. The data collected from respondents through a questionnaire were analyzed on the basis of ChapmanKolmogorov transition probability matrix for Markov processes for 1st ,2nd and 3rd steps.. The results revealed varying retention and switching patterns as both the upward and downward diagonals decreases as the step increases though Pepsi has the highest retention likelihood under cost and product quality determinant factors with a differential margin of about 14% to Coke while Limca takes the least. Pepsi maintains a margin of close to 30% ahead of Coke while Limca assumes the least under consumers’ choice based on product quality. The retention is higher in Coke based on availability with a margin of approximately 8% to Pepsi which is 4% higher than Limca.It was also observed that probability of switching from Pepsi to coke is higher than switching to Limca at the various steps under all the choice determinants factors. The computation of the markets share for the brands based on the initial probabilities under choice determinants factors revealed Pepsi as the dominant brand under cost and product quality considerations while Coke dominates under availability factor while Limca constantly maintains the rare both in terms of the retention and switching as well as market shares

Molecular switching by proton-coupled electron transport drives giant negative differential resistance

To develop new types of dynamic molecular devices with atomic-scale control over electronic function, new types of molecular switches are needed with time-dependent switching probabilities. We report such a molecular switch based on proton-coupled electron transfer (PCET) reaction with giant hysteric negative differential resistance (NDR) with peak-to-valley ratios of 120 ± 6.6 and memory on/off ratios of (2.4 ± 0.6) × 103. The switching dynamics probabilities are modulated by bias voltage sweep rate and can also be controlled by pH and relative humidity, confirmed by kinetic isotope effect measurements. The demonstrated dynamical and environment-specific modulation of giant NDR and memory effects provide new opportunities for bioelectronics and artificial neural networks.

On the motivation for pharmaceutical manufacturer coupons: Brand loyalty or customer acquisition?

To generate evidence regarding the offensive (customer acquisition) versus defensive (customer retention) motivation for pharmaceutical manufacturer coupons. Retail prescriptions from IQVIA's Formulary Impact Analyzer data between 2017 and 2019. Ordinary least squares regression models with person, therapeutic class, drug, and time-fixed effects to measure the association between switching medications and coupon usage as well as the association between patient out-of-pocket spending and switching to a drug and using a coupon. To study switching type heterogeneity, reanalysis of associations for any type of switch, generic-brand switches, and brand-brand switches. Reestimation of baseline analyses for sodium-glucose cotransporter-2 inhibitors, anticoagulants, and inhaled corticosteroids/long-acting beta2-agonists to assess heterogeneity by drug class and market maturity. 1,167,132 privately insured patients that utilized at least one coupon between 2017 and 2019 for one or more prescriptions. Coupon usage was associated with a 1.0 percentage point reduction in any kind of drug switch in the full sample and by 0.65-2.9 percentage points for the drug class subgroups. However, these estimates are governed by market dynamics; the probability of switching increased by 40% on the first coupon usage before declining by more than 50% on subsequent coupons. Switching after the first coupon use may be explained by systematic savings implied by coupon use; we find coupons reduced patient out-of-pocket spending by $45.00 (i.e., the majority of patient out-of-pocket costs). In subgroup analyses, coupon savings were $6.43 larger than average for anticoagulants, characterized by the highest levels of brand and generic competition among the considered therapeutic classes. Pharmaceutical manufacturers may be using coupons to acquire customers and then build brand loyalty, especially in markets with more generic competition. Antitrust authorities and other regulators should scrutinize the impact of coupons on market competitiveness and drug spending.

Treatment switching and associated economic outcomes in patients with plaque psoriasis treated with biologics: A retrospective analysis of German claims data 2016−2021

AbstractBackgroundBiologics are therapeutic options for the management of moderate−severe plaque psoriasis. Some patients need to switch biologic treatment to achieve satisfactory outcomes, which might have a considerable economic impact.ObjectivesWe assessed the characteristics and switch rates of patients with plaque psoriasis initiating biologic treatment and compared healthcare resource utilization (HCRU) and associated costs for switching and non‐switching.MethodsThis study was a retrospective claims‐based analysis comprising a 182‐day baseline period to identify patient characteristics and a 365‐day follow‐up to assess switch rates, HCRU and associated costs. Data covering claims activity from 2016 to 2021 in a representative sample of four million individuals with statutory insurance in Germany was used.ResultsWe identified 2565 patients with psoriasis initiating biologic treatment with anti‐IL‐17 (n = 1037), anti‐IL‐23 (n = 704), anti‐TNF‐α (n = 583) and anti‐IL‐12/23 (n = 241) agents. A total of 9.2% of patients switched therapy to another biologic during follow‐up, ranging from 4.9% (secukinumab) to 16.5% (etanercept). The probability of treatment switching was significantly lower in patients treated with risankizumab (p < 0.05) than in patients treated with other biologics except guselkumab (p = 0.14). HCRU and associated costs during the follow‐up were generally higher with a therapy switch (all‐cause: 32,263 ± 15,381€) than without (25,041 ± 12,090€). This applied to direct costs (outpatient services, hospitalization, drug treatment) and indirect costs (sickness benefits). Drug treatment accounted for the largest share of costs.ConclusionsTreatment switching is frequent in patients with moderate−severe plaque psoriasis initiating biologic therapy and is associated with increased HCRU and associated costs. As the probability that a switch occurs within 365 days after treatment initiation widely differs between biologic agents, further research is warranted to determine the underlying reasons for switching to help establish clinically and economically sound therapy sequences.

Changing patterns of cigarette and ENDS transitions in the USA: a multistate transition analysis of adults in the PATH Study in 2017–2019 vs 2019–2021

IntroductionThe use of cigarettes and electronic nicotine delivery system (ENDS) has likely changed since 2019 with the rise of pods and disposables, the lung injuries outbreak, flavour bans, Tobacco 21...

Tunable Stochastic State Switching in 2D MoS2 Nanomechanical Resonators with Nonlinear Mode Coupling and Internal Resonance.

Coupled nanomechanical resonators have unveiled fascinating physical phenomena, including phonon-cavity coupling, coupled energy decay pathway, avoided crossing, and internal resonance. Despite these discoveries, the mechanisms and control techniques of nonlinear mode coupling phenomena with internal resonances require further exploration. Here, we report on the observation of stochastic switching between the two resonance states with coupled 1:1 internal resonance, for resonant two-dimensional (2D) molybdenum disulfide (MoS2) nanoelectromechanical systems (NEMS), which is directly driven to the critical coupling regime without parametric pumping. We further demonstrate that the probability of state switching is linearly tunable from ∼0% to ∼100% by varying the driving voltage. Furthermore, we gradually increase the white noise amplitude and show that the probability of obtaining the higher-energy state decreases, and the stochastic switching phenomenon eventually disappears. The results provide insights into the dynamics of coupled NEMS resonators and open up new possibilities for sensing and stochastic computing.

The nonlinear impacts of macroeconomic shocks on the US regional housing markets

ABSTRACT This paper examines the asymmetrical impacts of aggregate macroeconomic shocks on the US regional housing markets by estimating nonlinear Vector Autoregressive models. We show how aggregate output, monetary, inflation, and credit shocks result in heterogenous impulse responses of the housing market volatilities across growing and declining housing market regimes in nine census divisions and 19 metropolitan cities. We find that output and inflation shocks cause qualitatively asymmetrical responses across regimes in majority of regions. A positive output shock and a negative inflation shock reduce (increase) housing price uncertainties when housing markets are in a declining (booming) regime in most regions. The responses of regional housing price volatilities to monetary and credit shocks are quantitatively asymmetrical across regimes in most regions. We also find that aggregate shocks have larger contributions on changing the probabilities of regime switching when a regional housing market is initially expanding than declining. Among aggregate shocks, inflation shock has the most prominent contributions across most regions. It also presents the strongest asymmetrical effects across positive and negative shocks. Our results can help regional decision makers utilize fiscal policies, regulatory measures, and regional planning to mitigate the destabilizing effects of macroeconomic shocks on regional housing markets.

Medical image segmentation approach based on hybrid adaptive differential evolution and crayfish optimizer

Image segmentation plays a pivotal role in medical image analysis, particularly for accurately isolating tumors and lesions. Effective segmentation improves diagnostic precision and facilitates quantitative analysis, which is vital for medical professionals. However, traditional segmentation methods often struggle with multilevel thresholding due to the associated computational complexity. Therefore, determining the optimal threshold set is an NP-hard problem, highlighting the pressing need for efficient optimization strategies to overcome these challenges. This paper introduces a multi-threshold image segmentation (MTIS) method that integrates a hybrid approach combining Differential Evolution (DE) and the Crayfish Optimization Algorithm (COA), known as HADECO. Utilizing two-dimensional (2D) Kapur’s entropy and a 2D histogram, this method aims to enhance the efficiency and accuracy of subsequent image analysis and diagnosis. HADECO is a hybrid algorithm that combines DE and COA by exchanging information based on predefined rules, leveraging the strengths of both for superior optimization results. It employs Latin Hypercube Sampling (LHS) to generate a high-quality initial population. HADECO introduces an improved DE algorithm (IDE) with adaptive and dynamic adjustments to key DE parameters and new mutation strategies to enhance its search capability. In addition, it incorporates an adaptive COA (ACOA) with dynamic adjustments to the switching probability parameter, effectively balancing exploration and exploitation. To evaluate the effectiveness of HADECO, its performance is initially assessed using CEC’22 benchmark functions. HADECO is evaluated against several contemporary algorithms using the Wilcoxon signed rank test (WSRT) and the Friedman test (FT) to integrate the results. The findings highlight HADECO’s superior optimization abilities, demonstrated by its lowest average Friedman ranking of 1.08. Furthermore, the HADECO-based MTIS method is evaluated using MRI images for knee and CT scans for brain intracranial hemorrhage (ICH). Quantitative results in brain hemorrhage image segmentation show that the proposed method achieves a superior average peak signal-to-noise ratio (PSNR) and feature similarity index (FSIM) of 1.5 and 1.7 at the 6-level threshold. In knee image segmentation, it attains an average PSNR and FSIM of 1.3 and 1.2 at the 5-level threshold, demonstrating the method’s effectiveness in solving image segmentation problems.

Strong Electron-Vibration Signals in Weakly Coupled Molecular Junctions: Activation of Spin-Crossover.

Manipulating individual molecular spin states with electronic current has the potential to revolutionize quantum information devices. However, it is still unclear how a current can cause a spin transition in single-molecule devices. Here, we propose a spin-crossover (SCO) mechanism induced by electron-phonon coupling in an iron(II) phthalocyanine molecule situated on a graphene-decoupled Ir(111) substrate. We performed simulations of both elastic and inelastic electron tunneling spectroscopy (IETS), which reveal current-induced Fe-N vibrations and an underestimation of established electron-vibration signals. Going beyond standard perturbation theory, we examined molecules in various charge and spin states using the Franck-Condon framework. The increased probability of spin switching suggests that notable IETS signals indicate SCO triggered by the inelastic vibrational excitation associated with Fe-N stretching.

Skyrmion-mediated nonvolatile ternary memory

Multistate memory systems have the ability to store and process more data in the same physical space as binary memory systems, making them a potential alternative to existing binary memory systems. In the past, it has been demonstrated that voltage-controlled magnetic anisotropy (VCMA) based writing is highly energy-efficient compared to other writing methods used in non-volatile nano-magnetic binary memory systems. In this study, we introduce a new, VCMA-based and skyrmion-mediated non-volatile ternary memory system using a perpendicular magnetic tunnel junction (p-MTJ) in the presence of room temperature thermal perturbation. We have also shown that ternary states {− 1, 0, + 1} can be implemented with three magnetoresistance values obtained from a p-MTJ corresponding to ferromagnetic up, down, and skyrmion state, with 99% switching probability in the presence of room temperature thermal noise in an energy-efficient way, requiring ~ 2 fJ energy on an average for each switching operation. Additionally, we show that our proposed ternary memory demonstrates an improvement in area and energy by at least 2X and ~ 104X respectively, compared to state-of-the-art spin-transfer torque (STT)-based non-volatile magnetic multistate memories. Furthermore, these three states can be potentially utilized for energy-efficient, high-density in-memory quantized deep neural network implementation.

Teaching Quality: An Unexamined Element of Special Education Teacher Turnover

Little is known about the quality of the special educators who move schools, switch to general education, or leave teaching. In this study, the author examined if turnover (moving schools within a district, moving schools between districts, switching to general education, or leaving teaching in the state) was associated with two common indicators of teacher quality: value-added scores or teaching observation scores. On average, special educators’ probability of leaving teaching in the state declined as their value-added score increased, after accounting for the characteristics of teachers, their students, and their schools. Observation scores were similarly negatively associated with leaving and with moving within or between districts. Eight percent of special educators switched to general education positions, and the probability of switching was not associated with teachers’ value-added or observation scores. The author discusses the implications for ensuring schools retain effective special educators while noting the limitations of current measures of special educator quality used in practice.

An insight of heart-like systems with percolation

We study the signal percolation through heart-like biological system. Starting from an initial distribution of waiting and inactive cells with probabilities p and (1−p) respectively, the signal propagation is observed in terms of active cells. As the signal enters the system from one end, the number of arrival of active sites at the other end is studied and analysis of the system behaviour is made by varying a few important parameters of the system like pswitch (switching probability from inactive to waiting) and pact (switching probability from waiting to active). In this connection, the non-regular heart rhythms are discussed. Fraction of paths percolating through the system shows a transition from 0 to 1 near p=pc. Some other important quantities like tortuosity and cluster distribution are discussed. Several critical exponents have been obtained and compared the exponents of standard percolation.

EXPRESS: Exploring 'phasic' vs. 'tonic' accounts of the effect of switch probability on the auditory attention switch cost.

Task-switching experiments have shown that the 'switch cost' (poorer performance for task switches than for repetitions) is smaller when the probability of a switch is high (e.g., 0.75) than when it is low (e.g., 0.25). Some theoretical accounts explain this effect in terms of top-down control deployed in advance of the task cue ('pre-cue reconfiguration'). We tested such accounts by manipulating the time available before the onset of the cue (the response-cue interval, RCI), reasoning that top-down pre-cue reconfiguration requires time and therefore its effect should increase with RCI. Participants heard a man and a woman simultaneously speaking number words and categorised the number (<5 vs. >5) spoken by the voice specified by a pictorial gender-related cue presented at an RCI of 100 ms or 2200 ms. The target voice switched with a probability of 0.25 or 0.75 (in separate sessions). In Experiment 1 RTs revealed a large effect of switch probability on the switch cost in the short RCI, which did not increase in the long RCI. Errors hinted at such an increase, but it did not receive clear statistical support and was disconfirmed by a direct and better powered replication in Experiment 2, which fully confirmed the RT pattern from Experiment 1. Thus, the effect of switch probability on the switch cost required little/no time following the response to emerge - it was already at full magnitude at a short RCI - challenging accounts that assume 'phasic' deployment of top-down task-set control in advance of the cue.

Latent Profiles and Transitions of Bedtime Procrastination Among Chinese College Students: The Predictive Roles of Anxiety, Depression, Problematic Smartphone Use and Self-Control.

Bedtime procrastination (BP) has become an important factor affecting individual well-being. This study aimed to assess the stability and changes in BP and examine risk and protective factors. The study recruited 1423 respondents. Latent profile analysis was used to identify subgroups of BP and latent transition analysis to determine transition probabilities for each subgroup. Logistic regression examined associations between identified classes and related factors. Three subgroups of BP were identified. In terms of stability and changes, the moderate bedtime procrastination group showed the highest stability (66%), followed by the severe bedtime procrastination group (62.4%), and the mild bedtime procrastination group had a 52% probability of switching to moderate bedtime procrastination. In terms of influencing factors, more problematic phone use (PSU) (OR: 1.08; 95% CI = 1.05-1.12), more depression (OR: 1.17; 95% CI = 1.06-1.29) and anxiety (OR: 1.16; 95% CI = 1.05-1.28) are all factors that aggravate the transition from mild to moderate sleep procrastination. Similarly, PSU (OR: 1.15; 95% CI = 1.12-1.19), anxiety (OR: 1.10; 95% CI = 1.06-1.14), and depression (OR: 1.10; 95% CI = 1.06-1.14) increased the risk of severe bedtime procrastination. Self-control emerged as a protective factor against BP. This study identified three subgroups of BP at two time points and the rule of transition for each subgroup. Our findings indicate that BP were relatively stable, with some changes over time. The results also highlight the important function that PSU, depression, anxiety, and self-control can play in preventing and intervening in BP.

Enhancing thermal stability of optimal magnetization reversal in nanoparticles

Energy-efficient switching of nanoscale magnets requires application of a time-varying magnetic field characterized by microwave frequency. At finite temperatures, even weak thermal fluctuations induce perturbations in the magnetization that can accumulate in time, disrupt the phase locking between the magnetization and the applied field, and eventually compromise magnetization switching. It is demonstrated here that the magnetization reversal is mostly disturbed by unstable perturbations arising in a certain domain of the configuration space of a nanomagnet. The instabilities can be suppressed and the probability of magnetization switching enhanced by applying an additional stimulus such as a weak longitudinal magnetic field that ensures bounded dynamics of the perturbations. Application of the stabilizing longitudinal field to a uniaxial nanomagnet makes it possible to reach a desired probability of magnetization switching even at elevated temperatures. The principle of suppressing instabilities provides a general approach to enhancing thermal stability of magnetization dynamics. Published by the American Physical Society 2024