Unobserved Behavior Research Articles

PSYCHOLOGICAL METHODOLOGY OF CRIME DETECTION (ZIGMUND FREUD ON CRIME)

Sigmund Freud is recognized worldwide as the original psychoanalyst founded to form theories and concepts surrounding the existence of mental illness and its nature in relation to human behavior. Throughout his psychological and psychoanalytic studies, Freud concluded that behavior can be explained through the investigation of one's experiences and traumas by accounting for the motivation of a person's actions. Interpretation of his findings concluded that a person can adapt his/her behavior from childhood experiences to become part of the hidden state of consciousness He studied unobservable behaviors, parts of personality that are not clearly visible within one's nature and at a basic level, cannot be explained. In 1923, Freud created a psychological idea or theory that the human mind had three elements that conceptually make up the "Psyche". The Id (instincts), Ego (reality), and Superego (morality) are not tangible physical areas within the brain, but rather entities that Freud concluded make up the human personality. What Freud called the "psychic apparatus," three elements of the human personality are now established as the unconscious level that dictates biological desires and instincts. Psychoanalytic criminology is a method of studying crime and criminal behavior that is based on Freudian psychoanalysis. This theory or school of thought examines the personality and psyche (especially the unconscious) for motive in crime. Other areas of interest are fear of crime and the act of punishment. Criminal behavior is attributed to maladjustment and dysfunctional personality. According to Buhagiar, "psychoanalytic criminologists were not against the principle of solitary confinement and often favored increased punishment."

MCProj: metacell projection for interpretable and quantitative use of transcriptional atlases

We describe MCProj—an algorithm for analyzing query scRNA-seq data by projections over reference single-cell atlases. We represent the reference as a manifold of annotated metacell gene expression distributions. We then interpret query metacells as mixtures of atlas distributions while correcting for technology-specific gene biases. This approach distinguishes and tags query cells that are consistent with atlas states from unobserved (novel or artifactual) behaviors. It also identifies expression differences observed in successfully mapped query states. We showcase MCProj functionality by projecting scRNA-seq data on a blood cell atlas, deriving precise, quantitative, and interpretable results across technologies and datasets.

Identification of unobservable behavior in stochastic discrete event systems with a low number of sensors

Dynamic discrete event systems (DDES) are systems that evolve from the asynchronous occurrence of discrete events. Their versatility has become a critical modeling tool in different applications. Finding models that define the behavior of DES is a topic that has been addressed from different approaches, depending on the type of system to be modeled and the model's objective. This article focuses on the identification of timed models for stochastic discrete event systems. The identified model includes both observable and unobservable behavior. The objective of the method is achieved through the following steps:•Identifying the sequences of events observed at different time instances during the closed-loop operation of the system (observed language),•Inferring the stochastic behavior of time between events and modeling the observable behavior as a stochastic timed Interpreted Petri Net (st-IPN),•and finally, inferring the non-observable behavior using the language projection operation between the observed language and the language generated by the st-IPN.This method has novel aspects because it uses timed events, can be applied to systems with a low number of sensors and can infer unobservable behavior for any sequence of events.

Collaborative filtering with sequential implicit feedback via learning users’ preferences over item-sets

Nowadays, the sequential information, i.e., the ordering of the recorded feedback, is one of the most frequently used auxiliary information for developing recommendation algorithms. However, there are still some challenges in collaborative filtering with sequential implicit feedback. For one thing, there may be some co-occurrence and “leap-occurrence” phenomena in users’ behavior sequences. For another, a weak point of utilizing implicit feedback is that the unobserved behaviors do not always indicate “dislike”. In this paper, we focus on these two fundamental challenges in users’ preference learning, i.e., the uncertainty of the ordering of the next items and the uneven quality of the negative samples, and propose a novel solution named COFIS (short for collaborative filtering with sequential implicit feedback via learning users’ preferences over item-sets). Specifically, our COFIS incorporates two learning paradigms (i.e., pairwise and pointwise) and two expression strategies (i.e., “MOO” and “MOS”, representing two different ways to treat the negative item-sets when compared with the positive item-sets). Our COFIS can be applied to existing methods such as many factorization- and deep learning-baseed models. We derive four specific algorithms of our COFIS based on Fossil (denoted as COFIS(-,-) for short) in particular, and further show the results of our COFIS-improved methods (including the factorization-based COFIS, and the deep learning-based SASRec(COFIS) and FMLP-Rec(COFIS)). Notice that we treat the seminal works and their pointwise version as special cases of our COFIS. Empirical studies on four real-world datasets show the superiority of our proposed COFIS compared with the state-of-the-art baselines. Moreover, we also study the impact of some key parameters in our COFIS.

Gender Determinant on Multidimensional Poverty Index: Evidence from Indonesia

Poverty measurement from a non-monetary aspect is needed as low-income individuals are not always multidimensionally poor, and vice versa. The focus should also be on the gender determinant potentially related to the inequality in wage, labour market, and the return of education, which can influence the household’s ability to achieve a higher standard of living and alleviate poverty. This paper discovers the contribution of gender determinants to multidimensional poverty conditions in Indonesia. This paper used logit estimation using National Socioeconomics Survey (Susenas) 2018. The data show that approximately 10% of the Indonesian population is considered vulnerably poor, and severely poor is 3%. The vulnerably and severely poor individuals are mostly measured from years of schooling, health insurance ownership, and assets ownership. Moreover, we find that variables of household size, dependency ratio, and household head age are the better explanators of poverty’s vulnerability. However, those variables cannot explain severe poverty among female- and male-headed households, even though female-headed households are more prone to falling into poverty situations. Then, the decomposition results show that our selected variables explain the probability of being vulnerable poor. However, the probability of being severely poor is largely determined by unobservable behaviour domination not included in the study.

Vaccination under pessimistic expectations in clinical trials and immunization campaigns.

We provide one of the first formalizations of a vaccination campaign in a decision‐theoretic framework. We analyze a model where an ambiguity‐averse individual must decide how much effort to invest into prevention in the context of a rampant disease. We study how ambiguity aversion affects the effort and the estimation of the vaccine efficacy in clinical trials and immunization campaigns. We find that the behaviors of individuals participating in a clinical trial differ from individuals not participating. Individuals who are more optimistic toward vaccination participate more in trials. Their behaviors and efforts are also affected. As a result, because vaccine efficacy depends on unobserved behaviors and efforts, the biological effect of the vaccine becomes difficult to evaluate. During the scale‐up phase of a vaccination campaign, provided that vaccine efficacy is established, we show that vaccine hesitancy may still be rational.

Comparing observed and unobserved fishing characteristics in the drift gillnet fishery for swordfish

Central to the success of partial coverage observer programs is the assumption that observed fishing behaviors are representative of unobserved fishing behaviors. Traditionally, this was a difficult assumption to test beyond comparing catch compositions for observed and unobserved trips. With the proliferation of vessel monitoring systems (VMS) however, a suite of trip- and set-level fishing characteristics can be engineered from vessel locations that facilitate quantitative comparisons between observed and unobserved behaviors. The U.S. West Coast drift gillnet (DGN) fishery for swordfish (Xiphias gladius) is a small fishery with a long history of bycatch concerns and an observer coverage rate around 20–30 %. We developed an integrated data set composed of VMS, logbook, landings, and observer data for the DGN fishery from 2013 to 2019. We used a suite of machine learning models to classify unobserved fishing behaviors and we characterized these fishing behaviors at the trip- and set-level. Analyses of the trip- and set-level metrics (e.g., trip duration, fishing depth, sea surface temperatures, distance from port, and catch per unit effort) revealed that operational characteristics of the fishery were largely similar for observer and unobserved trips and vessels, but also highly variable. In the fall season, observed trip distances were an average of 53 % (std. error 20 %) longer than unobserved trips for the same vessels. These longer trips occurred in water that was, on average, about 39 % (std. error 16 %) deeper. However, characteristics like amounts of fishing effort, revenue, and water temperatures were generally similar between observed and unobserved trips. We present a methodical workflow for data integration and feature engineering and a simple approach for assessing differences across fleet behaviors in space and time that could be applied to many fisheries with limited observer coverage. This approach could also be used to target specific strata within fisheries that may benefit from increases or shifts in observer coverage.

A satellite-linked tag for the long-term monitoring of diving behavior in large whales

Despite spending most time underwater, the technology in use to track whales over large geographic ranges via satellite has been largely limited to locational data, with most applications focusing on characterizing their horizontal movements. We describe the development of the RDW tag, a new Argos-based satellite telemetry device that incorporates sensors for monitoring the movements and dive behavior of large whales over several months without requiring recovery. Based on an implantable design, the tag features a saltwater conductivity switch, a tri-axial accelerometer, and an optional pressure transducer, along with onboard software for data processing and detection of behavioral events or activities of interest for transmission. We configured the software to detect dives and create per-dive summaries describing behavioral events associated with feeding activities in rorqual whales. We conducted a validation by proxy of the dive summary and event detection algorithms using field data from a medium-duration archival tag. We also conducted a simulation exercise to examine how the expected data recovery would vary under different dive behavior scenarios and compared those results to empirical values from field deployments of the RDW tag on blue (Balaenoptera musculus) and humpback (Megaptera novaeangliae) whales. The dive summary algorithm accurately reported dive depth and duration, while the accuracy of the lunge-feeding event detection algorithm was dependent on the precision of the accelerometer data that was used, with a predicted accuracy of 0.74 for correctly classifying feeding dives from 1/64-G precision data and 0.95 from 1-mG precision data. Simulated data recovery was lower with sparser transmission schedules, shorter mean dive durations, and lower rates of successfully received transmissions. Empirical data recovery was lower than expected from the simulation, suggesting the effect of additional factors, such as data gaps. By measuring key aspects of the per-dive behavior of large whales over multi-month timescales of movement, the RDW tags provide the ability to monitor previously unobservable behaviors across entire geographic ranges, extending the applications of satellite telemetry devices to new areas of whale physiology, behavior, ecology, and conservation.

A Bi-Anti-Ambipolar Field Effect Transistor.

Multistate logic is recognized as a promising approach to increase the device density of microelectronics, but current approaches are offset by limited performance and large circuit complexity. We here demonstrate a route toward increased integration density that is enabled by a mechanically tunable device concept. Bi-anti-ambipolar transistors (bi-AATs) exhibit two distinct peaks in their transconductance and can be realized by a single 2D-material heterojunction-based solid-state device. Dynamic deformation of the device reveals the co-occurrence of two conduction pathways to be the origin of this previously unobserved behavior. Initially, carrier conduction proceeds through the junction edge, but illumination and application of strain can increase the recombination rate in the junction sufficiently to support an alternative carrier conduction path through the junction area. Optical characterization reveals a tunable emission pattern and increased optoelectronic responsivity that corroborates our model. Strain control permits the optimization of the conduction efficiency through both pathways and can be employed in quaternary inverters for future multilogic applications.

Impacts of farmers' adaptation to drought and salinity intrusion on rice yield in Vietnam's Mekong Delta

PurposeThe Vietnamese Mekong River Delta (VMD) is one of the most affected deltas by climate change in the world. Several studies have investigated factors influencing farmers' climate change adaptation behaviors in the region; however, little is known about the effectiveness of such measures. This paper examines the determinants of adaptation strategies among VMD rice farmers and assesses the impacts of such practices on rice yield.Design/methodology/approachEndogenous switching regressions were employed using a survey data of 300 rice-producing households in An Giang and Tra Vinh provinces in 2016.FindingsThe results show that farmers receiving early disaster warnings are more likely to adopt adaptation measures to climate change. If nonadaptors had chosen to respond, their rice yield would have increased by 0.932 tons/ha/season.Research limitations/implicationsThe data sample is small and collected from two provinces in the VMD only; therefore, the results may be specific for the study sites. However, future research can adopt the proposed method for other regions.Originality/valueThe study estimates the production impacts of farmers' decisions on whether or not to adapt to extreme climate events. The proposed approach allows for capturing both observed and unobserved behaviors.

Responsiveness to change over time and test-retest reliability of the PROMIS and Neuro-QoL mental health measures in persons with Huntington disease (HD).

The majority of persons with Huntington disease (HD) experience mental health symptoms. Patient-reported outcome (PRO) measures are capable of capturing unobservable behaviors and feelings relating to mental health. The current study aimed to test the reliability and responsiveness to self-reported and clinician-rated change over time of Neuro-QoL and PROMIS mental health PROs over the course of a 24-month period. At baseline, 12-months, and 24-months, 362 participants with premanifest or manifest HD completed the Neuro-QoL Depression computer adaptive test (CAT), PROMIS Depression short form (SF), Neuro-QoL Anxiety CAT, PROMIS Anxiety SF, PROMIS Anger CAT and SF, Neuro-QoL Emotional/Behavioral Dyscontrol CAT and SF, Neuro-QoL Positive Affect and Well-Being CAT and SF, and Neuro-QoL Stigma CAT and SF. Participants completed several clinician-administered measures at each time point, as well as several global ratings of change at 12- and 24-months. Reliability (test-retest reliability and measurement error) and responsiveness (using standardized response means and general linear models) were assessed. Test-retest reliability and measurement error were excellent for all PROs (all ICC ≥ .90 for test-retest reliability and all SEM percentages ≤ 6.82%). In addition, 12- and 24-month responsiveness were generally supported for the Neuro-QoL and PROMIS mental health PROs; findings relative to clinician-rated anchors of change (e.g., SRMs for the group with declines ranged from .38 to .91 for 24-month change and .09 to .45, with the majority above .25 for 12-month change) were generally more robust than those relative to self-reported anchors of change (e.g., SRMs for the group with declines ranged from .02 to .75, with the majority above .39 for 24-month change and .09 to .45, with the majority above .16 for 12-month change). The Neuro-QoL and PROMIS mental health PROs demonstrated strong psychometric reliability, as well as responsiveness to self-reported and clinician-rated change over time in people with HD.

Data-Driven Inference Reveals Distinct and Conserved Dynamic Pathways of Tool Use Emergence across Animal Taxa.

SummaryTool use is a striking aspect of animal behavior, but it is hard to infer how the capacity for different types of tool use emerged across animal taxa. Here we address this question with HyperTraPS, a statistical approach that uses contemporary observations to infer the likely orderings in which different types of tool use (digging, reaching, and more) were historically acquired. Strikingly, despite differences linked to environment and family, many similarities in these appear across animal taxa, suggesting some universality in the process of tool use acquisition across different animals and environments. Four broad classes of tool use are supported, progressing from simple object manipulations (acquired relatively early) to more complex interactions and abstractions (acquired relatively late or not at all). This data-driven, comparative approach supports existing and suggests new mechanistic hypotheses, predicts future and possible unobserved behaviors, and sheds light on patterns of tool use emergence across animals.

The role of nocturnal omnivorous lemurs as seed dispersers in Malagasy rain forests

AbstractFruit‐eating animals play important roles as seed dispersal agents in terrestrial systems. Yet, the extent to which seed dispersal by nocturnal omnivores may facilitate germination and the recruitment of plant communities has rarely been investigated. Characterizing their roles in seed dispersal is necessary to provide a more complete picture of how seed dispersal processes affect ecosystem functioning. We investigated the roles and impacts of two species of nocturnal omnivorous lemur species, Microcebus jollyae and M. rufus, on seed dispersal in Madagascar's rain forests, through analysis of fecal samples and germination experiments. Data show that these lemur species, which are among the world's smallest primates, dispersed 22 plant species from various forest strata and that the defecated seeds germinated faster and at higher rates than control seeds for the eight plant species we tested. Even though mouse lemurs dispersed both native and non‐native plant species, non‐native plant species represented a relatively small proportion (17%). These results demonstrate that overlooked nocturnal omnivores can act as important seed dispersers, which may have critical implications for forest regeneration and the maintenance of plant diversity in fragmented/degraded forests. Finally, we provide critical insights into the previously unobserved behavior and diet of endangered nocturnal lemurs for their effective conservation.Abstract in Malagasy is available with online material.

Novel Insights and New Developments Regarding Coagulation Revealed by Studies of the Anti-Factor IXa (Activated Factor IX)/Factor X Bispecific Antibody, Emicizumab.

Emicizumab is a humanized anti-FIXa/FX (factor IXa/X) bispecific monoclonal antibody that mimics FVIIIa (activated factor VIII) cofactor function. The hemostatic efficacy of emicizumab has been confirmed in clinical studies of patients with hemophilia A, irrespective of the presence of FVIII inhibitors. Emicizumab differs in some properties from FVIIIa molecule. Emicizumab requires no activation by thrombin and is not inactivated by activated protein C, but emicizumab-mediated coagulation is regulatable and maintains hemostasis. A small amount of FIXa (activated factor IX) is required to initiate emicizumab-mediated hemostasis, whereas tissue factor/FVIIa (activated factor VII)-mediated FXa (activated factor X) and thrombin activation initiates FVIIIa-mediated hemostasis. Fibrin formation, followed by fibrinolysis, appears to be similar between emicizumab- and FVIIIa-mediated hemostasis. These results suggest possible future uses of emicizumab for treating hemorrhagic diseases other than hemophilia A and reveal previously unobservable behaviors of procoagulation and anticoagulation factors in conventional hemostasis. Here, we have reviewed novel insights and new developments regarding coagulation highlighted by emicizumab.

A Unified Sparse Optimization Framework to Learn Parsimonious Physics-Informed Models From Data

Machine learning (ML) is redefining what is possible in data-intensive fields of science and engineering. However, applying ML to problems in the physical sciences comes with a unique set of challenges: scientists want physically interpretable models that can (i) generalize to predict previously unobserved behaviors, (ii) provide effective forecasting predictions (extrapolation), and (iii) be certifiable. Autonomous systems will necessarily interact with changing and uncertain environments, motivating the need for models that can accurately extrapolate based on physical principles (e.g. Newton's universal second law for classical mechanics, F = ma). Standard ML approaches have shown impressive performance for predicting dynamics in an interpolatory regime, but the resulting models often lack interpretability and fail to generalize. We build on a sparse regression framework that discovers governing dynamical systems models from data, selecting relevant terms in the dynamics from a library of possible functions. Our critically enabling innovation introduces a relaxed version of a sparse optimization framework that allows the use of non-convex sparsity promoting regularization functions and addresses three open challenges in scientific problems and data sets: (i) robust handling of outliers and corrupt data within noisy sensor measurements, (ii) parametric dependencies in candidate library functions, and (iii) the imposition of physical constraints. By explicitly addressing these open challenges, the integrated and unified algorithm developed provides a significant advancement over current state-of-the-art sparse model discovery methods. We show that the approach discovers parsimonious dynamical models on several example systems. This flexible approach can be tailored to the unique challenges associated with a wide range of applications and data sets, providing a powerful ML-based framework for learning governing models for physical systems from data.

Adversarial System Variant Approximation to Quantify Process Model Generalization

In process mining, process models are extracted from event logs using process discovery algorithms and are commonly assessed using multiple quality dimensions. While the metrics that measure the relationship of an extracted process model to its event log are well-studied, quantifying the level by which a process model can describe the unobserved behavior of its underlying system falls short in the literature. In this paper, a novel deep learning-based methodology called Adversarial System Variant Approximation (AVATAR) is proposed to overcome this issue. Sequence Generative Adversarial Networks are trained on the variants contained in an event log with the intention to approximate the underlying variant distribution of the system behavior. Unobserved realistic variants are sampled either directly from the Sequence Generative Adversarial Network or by leveraging the Metropolis-Hastings algorithm. The degree by which a process model relates to its underlying unknown system behavior is then quantified based on the realistic observed and estimated unobserved variants using established process model quality metrics. Significant performance improvements in revealing realistic unobserved variants are demonstrated in a controlled experiment on 15 ground truth systems. Additionally, the proposed methodology is experimentally tested and evaluated to quantify the generalization of 60 discovered process models with respect to their systems.

On the Relation Between Variability and Creativity: A Summary of Early Work and Advancements from Applied Research

Creativity, or the generation of previously unobserved behavior, is a topic of interest to scientists and the general public alike. Researchers outside behavior analysis have focused on creativity as a trait of individuals. Behavior analysts, however, have focused on the variables that give rise to novel responses. Initial conceptual work examined specific environmental variables that may promote creativity. Subsequent conceptual work examined the role of the response class and the selective nature of reinforcement. As this conceptual work progressed, researchers began evaluating variables that affect response variability, a potentially critical component of variability. Most basic researchers, and some applied researchers, have focused on response sequences. Although this work has proven fruitful in understanding response variability as an operant dimension, these limited response classes have prevented a more robust understanding of variability and novelty. Following the development of the lag schedule, applied researchers began using lag schedules to increase complex behavior behavior such as responses to questions, social skills, and martial arts skills. This applied work has provided intriguing evidence that variability, and environmental influence of variability, is a key contributor to novel responding, thus bringing creativity well into our scientific understanding of behavior.

Cooperative networks with robust private monitoring

Social networks support cooperative behavior in a variety of social and economic settings. We study cooperative networks that can be formed when payoff and information asymmetries imply that cooperation relies on unverifiable third-party punishments. Under private monitoring, equilibrium predictions can depend on players' beliefs about unobservable behavior, and our results identify the range of equilibrium network outcomes. In particular, when strategies must be robust to beliefs, we show that network structures satisfy a triadic closure property, providing a strategic rationale for the short paths and high clustering observed in many social and economic networks. We illustrate some of the substantive restrictions identified in our results for risk- and information-sharing networks.

An Optimization-Based Approach to Discover the Unobservable Behavior of a Discrete-Event System Through Interpreted Petri Nets

This article deals with the problem of discovering a Petri net (PN) model of a discrete-event system, starting from the observation of long-event sequences. Precisely, given an interpreted PN (IPN) system modeling the relations between input and output events of the system (i.e., the reactive/observable behavior), the internal state evolutions of the system (i.e., the unobservable behavior) are first discovered and then modeled. The proposed unobservable discovery takes advantage of the novel concept of interpreted sequences, which better characterize the system and model the behavior by considering both observable markings (outputs) and transition firings (inputs). The unobservable modeling is approached as a net synthesis problem. It relies on an optimization-based procedure that identifies the complementary structure; in particular, places only are added to the original model. Note to Practitioners —Black-box identification procedures process an input–output sequence recorded for a long period of time during the functioning of a closed-loop controlled system, and then return a model of the system. However, even if these models simulate well the recorded sequence, they are not very accurate. Indeed, they simulate also other sequences that, in general, are not admitted by the real system. The method proposed here aims to make more accurate these models by discovering the unobservable behavior of a controlled system, related to evolutions of the internal state (and variables) of the system without changing the capability of simulating the observed behavior.

Model Identification of Unobservable Behavior of Discrete Event Systems Using Petri Nets

This paper deals with the problem of identifying a Petri net that models the unobservable behavior of a system from the knowledge of its dynamical evolution. We assume that a partial Petri net model that represents the observable behavior of a system is given in which all the transitions are observable. An identifier monitors the system evolution and records the observed transition sequence (and possible corresponding markings). Some unobservable transitions modeling the unknown system behavior are identified from the transition sequence by formulating and solving integer linear programming problems. These identified unobservable transitions together with the given partial Petri net model characterize the whole system, including observable and unobservable behavior. Two different cases are considered. First, we assume that no place is observable. In such a case, a transition sequence is observed only during the evolution of the system. Second, we assume that a subset of places is observable; i.e., the observation contains not only the transition sequence but the corresponding markings as well. Hence an additional constraint should be imposed on the unobservable transition in the related programming problems according to the observed markings such that a more authentic unobservable transition can be found.