Activity Sequence Research Articles

Trustworthy and Context-Aware Detection of Social Isolation in the Elderly from Daily Activity Sequences

Trustworthy and Context-Aware Detection of Social Isolation in the Elderly from Daily Activity Sequences

Explainable process trace classification: An application to stroke

Medical process trace classification exploits the activity sequences logged by an healthcare organization to classify traces themselves on the basis of some performance properties; this information can be used for quality assessment. State-of-the-art process trace classification resorts to deep learning, a very powerful technique which however suffers from the lack of explainability.In this paper we aim at addressing this issue, motivated by a relevant application, i.e., the classification of process traces for quality assessment in stroke management. To this end we introduce the novel concept of trace saliency maps, an instrument able to highlight what trace activities are particularly significant for the classification task. Through trace saliency maps we justify the output of the deep learning architecture, and make it more easily interpretable to medical users. The good results in our use case have shown the feasibility of the approach, and let us make the hypothesis that it might be translated to other application settings and to other black box learners as well.

Intermittent Growth of a Newly-Born Volcanic Island and Its Feeding System Revealed by Geological and Geochemical Monitoring 2013–2020, Nishinoshima, Ogasawara, Japan

The island-forming Nishinoshima eruptions in the Ogasawara Islands, Japan, provide a rare opportunity to examine how the terrestrial part of Earth’s surface increases via volcanism. Here, the sequence of recent eruptive activity of Nishinoshima is described based on long-term geological and geochemical monitoring of eruptive products. Processes of island growth and temporal changes in the magma chemistry are discussed. The growth of Nishinoshima was sustained by the effusion of low-viscosity andesite lava flows since 2013. The lava flows spread radially with numerous branches, resulting in compound lava flows. Lava flows form the coherent base of the new volcanic edifice; however, pyroclastic eruptions further developed the subaerial volcanic edifice. The duration of three consecutive eruptive episodes decreased from 2 years to a week through the entire eruptive sequence, with a decreasing eruptive volume and discharge rate through time. However, the latest, fourth episode was the most intense and largest, with a magma discharge rate on the order of 106 m3/day. The temporal change in the chemical composition of the magma indicates that more mafic magma was involved in the later episodes. The initial andesite magma with ∼60 wt% SiO2 changed to basaltic andesite magma with ∼55 wt% SiO2, including olivine phenocryst, during the last episode. The eruptive behavior and geochemical characteristics suggest that the 2013–2020 Nishinoshima eruption was fueled by magma resulting from the mixing of silicic and mafic components in a shallow reservoir and by magma episodically supplied from deeper reservoirs. The lava effusion and the occasional explosive eruptions, sustained by the discharge of magma caused by the interactions of these multiple magma reservoirs at different depths, contributed to the formation and growth of the new Nishinoshima volcanic island since 2013. Comparisons with several examples of island-forming eruptions in shallow seas indicate that a long-lasting voluminous lava effusion with a discharge rate on the order of at least 104 m3/day (annual average) to 105 m3/day (monthly average) is required for the formation and growth of a new volcanic island with a diameter on km-scale that can survive sea-wave erosion over the years.

Activity Sequence Generation Using Universal Mobility Patterns

Previous work has established that rank ordered single-day activity sequences from various study areas exhibit a universal power law distribution called Zipf’s law. By analyzing datasets from across the world, evidence was provided that it is in fact a universal distribution. This study focuses on a potential mechanism that leads to the power law distribution that was previously discovered. It makes use of 15 household travel survey (HTS) datasets from study areas all over the world to demonstrate that reasonably accurate sets of activity sequences (or “schedules”) can be generated with extremely little information required; the model requires no input data and contains few tunable parameters. The activity sequence generation mechanism is based on sequential sampling from two universal distributions: (i) the distributions of the number of activities (trips) and (ii) the activity types (trip purposes). This paper also attempts to demonstrate the universal nature of these distributions by fitting several equations to the 15 HTS datasets. The lightweight activity sequence generation model can be implemented in any (lightweight) transportation model to create a basic set of activity sequences, saving effort and cost in data collection and in model development and calibration.

Remaining Activity Sequence Prediction for Ongoing Process Instances

Remaining activity sequence prediction (i.e. activity suffix prediction) aims at recommending the most likely future behaviors for ongoing process instances (i.e. traces), which enables process managers to rationally allocate resources and detect process deviations in advance. Recently, techniques of neural networks have found promising applications in activity suffix prediction by training a prediction model for next activity and iteratively performing the model to achieve the whole sequence prediction. However, the iterative prediction accumulates the deviations of each iteration and the result also lacks interpretability. In this paper, we propose a novel method to predict activity suffixes from the perspective of control flow and data flow for ongoing traces, where process discovery and trace replay techniques are employed to simulate executions of traces under real conditions and Long Short-Term Memory (LSTM) is applied to characterize the correlation between executed information and future execution. Sequence matching between historical prefix traces and ongoing traces is performed based on the above information to select the optimal-matched (i.e. most similar) activity suffix for ongoing process instances. Experiments on real-life datasets demonstrate that the proposed method outperforms other methods.

Scheduling activities with time-dependent durations and resource consumptions

In this paper we study the problem of scheduling a given sequence of activities where each activity consumes a resource with limited availability. Activity durations as well as resource consumptions are assumed to be time-dependent. Because of the interaction of time-dependent activity durations and resource consumptions, scheduling policies based on starting each activity in the sequence as early as possible may fail due to unnecessarily high resource consumptions exceeding the limited availability of the resource. We propose a dynamic discretization discovery algorithm that generates a partially time-expanded network during the search. We propose an acceleration technique allowing to significantly reduce the computational effort if the approach is embedded in an iterative solution procedure that frequently evaluates activity sequences which start with the same activities. Furthermore, we extend our approach to the case where resources can be replenished between subsequent activities. We evaluate our approach as a route evaluation method for the case of routing a fleet of electric vehicles in which travel durations and the energy consumed when travelling from one location to another depend on the time of the day.

Strain Diversity and Spatial Distribution Are Linked to Epidemic Dynamics in Host Populations.

AbstractThe inherently variable nature of epidemics renders predictions of when and where infection is expected to occur challenging. Differences in pathogen strain composition, diversity, fitness, and spatial distribution are generally ignored in epidemiological modeling and are rarely studied in natural populations, yet they may be important drivers of epidemic trajectories. To examine how these factors are linked to epidemics in natural host populations, we collected epidemiological and genetic data from 15 populations of the powdery mildew fungus, Podosphaera plantaginis, on Plantago lanceolata in the Åland Islands, Finland. In each population, we tracked spatiotemporal disease progression throughout one epidemic season and coupled our survey of infection with intensive field sampling of the pathogen. We found that strain composition varied greatly among populations in the landscape. Within populations, strain composition was driven by the sequence of strain activity: early-active strains reached higher abundances, leading to consistent strain compositions over time. Co-occurring strains also varied in their contribution to the growth of the local epidemic, and these fitness inequalities were linked to epidemic dynamics: a higher proportion of hosts became infected in populations containing strains that were more similar in fitness. Epidemic trajectories in the populations were also linked to strain diversity and spatial dynamics: higher infection rates occurred in populations containing higher strain diversity, while spatially clustered epidemics experienced lower infection rates. Together, our results suggest that spatial and/or temporal variation in the strain composition, diversity, and fitness of pathogen populations are important factors generating variation in epidemiological trajectories among infected host populations.

Periaqueductal gray neurons encode the sequential motor program in hunting behavior of mice

Sequential encoding of motor programs is essential for behavior generation. However, whether it is critical for instinctive behavior is still largely unknown. Mouse hunting behavior typically contains a sequential motor program, including the prey search, chase, attack, and consumption. Here, we reveal that the neuronal activity in the lateral periaqueductal gray (LPAG) follows a sequential pattern and is time-locked to different hunting actions. Optrode recordings and photoinhibition demonstrate that LPAGVgat neurons are required for the prey detection, chase and attack, while LPAGVglut2 neurons are selectively required for the attack. Ablation of inputs that could trigger hunting, including the central amygdala, the lateral hypothalamus, and the zona incerta, interrupts the activity sequence pattern and substantially impairs hunting actions. Therefore, our findings reveal that periaqueductal gray neuronal ensembles encode the sequential hunting motor program, which might provide a framework for decoding complex instinctive behaviors.

Organization-driven business process configurable modeling for spatial variability

Nowadays, enterprises need to continually adjust their business processes to adapt to the changes of business environments, especially when one business needs to be deployed in different application scenarios, which is called spatial variability in this paper. In the field of BPM (Business Process Management), configurable business process models have demonstrated their effectiveness in aspects of process modeling and model reuse. Yet, we found that the existing techniques lead to complex configurable models, and are inadequate for model reuse especially for the spatial variability issue because they neglect the root impact of organizations on control flow. S-BPM (Subject-oriented Business Process Management) models provide a solid foundation for dealing with complex applications and help to bridge the gap between business and IT for process execution. In this paper, we propose an organization-driven business process configurable modeling approach for spatial variability by integrating both restriction and extension operations based on the S-BPM paradigm, in which business objects are also included. Our approach is validated with a general business process developed for the Real Estate Administration (REA) in a certain province of China. The resulting configurable modeling framework can express the heterogeneous activity sequences for one business and has the potential to generate process models for uncertain environments in a new organization structure.

Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences.

The necessity of caring for elderly people is increasing. Great efforts are being made to enable the elderly population to remain independent for as long as possible. Technologies are being developed to monitor the daily activities of a person to detect their state. Approaches that recognize activities from simple environment sensors have been shown to perform well. It is also important to know the habits of a resident to distinguish between common and uncommon behavior. In this paper, we propose a novel approach to discover a person’s common daily routines. The approach consists of sequence comparison and a clustering method to obtain partitions of daily routines. Such partitions are the basis to detect unusual sequences of activities in a person’s day. Two types of partitions are examined. The first partition type is based on daily activity vectors, and the second type is based on sensor data. We show that daily activity vectors are needed to obtain reasonable results. We also show that partitions obtained with generalized Hamming distance for sequence comparison are better than partitions obtained with the Levenshtein distance. Experiments are performed with two publicly available datasets.

The environmental context of Early Neolithic cultural transformation in the Targowisko settlement region

The aim of this article is to provide information on environmental changes in the Targowisko region in the Early Neolithic as a natural response to settlement and economic activity of the human population in that area. The discussion is based on lithological, geochemical, and palynological analyses, as well as the analysis of Cladocera within strata inside the TRG (Targowisko) core, located in a small wetland in the immediate vicinity of the eastern edge of the Neolithic settlement in the Targowisko region. Settlement analysis points to the absence of stable microregions and to the mobility of human groups. This is confirmed by the sequence of settlement episodes and economic activity, reflected in the stratigraphy of the core sediments, where episodes of significant human interference are followed by phases of almost complete regeneration of the environment. No differences have been noticed between the Linienbandkeramik and Malice culture communities as regards their impact on the environment.

Sleep replay reveals premotor circuit structure for a skilled behavior

Neural circuits often exhibit sequences of activity, but the contribution of local networks to their generation remains unclear. In the zebra finch, song-related premotor sequences within HVC may result from some combination of local connectivity and long-range thalamic inputs from nucleus uvaeformis (Uva). Because lesions to either structure abolish song, we examine "sleep replay" using high-density recording methods to reconstruct precise song-related events. Replay activity persists after the upstream nucleus interfacialis of the nidopallium is lesioned and slows when HVC is cooled, demonstrating that HVC provides temporal structure for these events. To further gauge the importance of intra-HVC connectivity for shaping network dynamics, we lesion Uva during sleep and find that residual replay sequences could span syllable boundaries, supporting a model in which HVC can propagate sequences throughout the duration of the song. Our results highlight the power of studying offline activity to investigate behaviorally relevant circuit organization.

Discrete Element Modelling of Sedimentation and Tectonics: Implications for the Growth of Thrust Faults and Thrust Wedges in Space and Time, and the Interpretation of Syn-Tectonic (Growth) Strata

Thrust faults, and thrust wedges, are an important part of the surface morphology and structure of many contractional mountain belts. Analogue models of thrust wedges typically provide a map- and/or side-view of their evolution but give limited insight into their dynamic development. Numerical modelling studies, both kinematic and mechanical, have produced much insight into the various controls on thrust wedge development and fault propagation. However, in many studies, syn-tectonic sediments or “growth strata” have been modelled solely as passive markers and thus have no effect on, or do not feedback into, the evolving system. To address these issues, we present a high-resolution, 2D, discrete element model of thrust fault and wedge formation and the influence that coeval sedimentation may have on their evolution. We use frictional-cohesive assemblies, with flexural-slip between pre-defined layers, to represent probable cover rheologies. The syn-tectonic strata added during contraction are frictional-cohesive and we can think of them as “mechanical growth strata” as they interact with, and influence, the growing thrust wedge. In experiments of thrust wedge development without syn-tectonic sedimentation, a forward-breaking sequence is seen: producing a typical thrust-wedge geometry, consistent with analogue and numerical models. In general, the inclusion of syn-tectonic sedimentation produces thrust wedges composed of fewer major forward-vergent thrusts and with only minor thrust activity in the foreland. In most of these models the sequence of thrust activity is complex and not simply forward-breaking. With increasing sedimentation, the frontal thrust has much greater displacement and overrides a much thicker package of earlier syn-tectonic sediments. Very high syn-tectonic sedimentation results in the formation of a single basin-bounding thrust fault and no thrust-wedge per se. At the local (outcrop) scale of individual fault-related folds, high syn-tectonic sedimentation alters fault-fold evolution by producing steeper ramps, whereas low syn-tectonic sedimentation allows shallower ramps that may flatten and propagate into the syn-tectonic strata. Implications of these results for the interpretation of thrust faults and wedges and their interaction with associated growth strata are discussed.

Change and Diversity in Neolithic Mortuary Practices on the Isle of Man

While the Early Neolithic chambered tombs of the Isle of Man are well known and the Late Neolithic has been clearly defined with reference to a distinctive suite of artefacts, little is known about the Middle Neolithic. This article reports on 17 new Neolithic radiocarbon dates from cremated human remains from the Isle of Man. These identify five burials in cists as Middle Neolithic and indicate new sequences of activity at cemeteries starting in the Middle Neolithic. Each of these sites is examined in detail. The dates also spur a reconsideration of the development of Ronaldsway pottery and the integration of Grooved Ware pottery and motifs into early 3rd millennium practice on the island. The paper ends with a consideration of the changing effects of mortuary practices throughout the Neolithic on the Isle of Man and a discussion of connections with Middle and Late Neolithic activity in Ireland and Britain.

Real-Time Learning and Recognition of Assembly Activities Based on Virtual Reality Demonstration.

Teaching robots to learn through human demonstrations is a natural and direct method, and virtual reality technology is an effective way to achieve fast and realistic demonstrations. In this paper, we construct a virtual reality demonstration system that uses virtual reality equipment for assembly activities demonstration, and using the motion data of the virtual demonstration system, the human demonstration is deduced into an activity sequence that can be performed by the robot. Through experimentation, the virtual reality demonstration system in this paper can achieve a 95% correct rate of activity recognition. We also created a simulated ur5 robotic arm grasping system to reproduce the inferred activity sequence.

Creating household occupancy and energy behavioural profiles using national time use survey data

Occupants’ interactions with building components determine the timing and magnitude of energy demand. Therefore a more realistic representation of energy-related occupant behaviours in energy simulations will enhance the accuracy of predicted building energy demand. The aim of this paper was to assess occupancy and behavioural patterns in Australian households based on a detailed time use survey, which collected daily activities from 6,902 persons in 3,626 households. Profiles of occupancy state and energy-related activities were analysed for different seasons, day of the week, and household compositions. The activity sequences were further aggregated to reproduce the total time use for the activities and occupancy states. The results showed that occupancy and the timing of energy related activities were time dependent and were also affected by season, day of week, and household composition. The outcomes can be used as input to simulation tools where realistic occupant behaviour should be taken into account.

Variational embedding of a hidden Markov model to generate human activity sequences

Although human trajectory data that are collected passively from location-based services (LBS) are regarded as a substitute for household travel surveys that entail a larger cost, the reality is that the data cannot be utilized directly for transportation planning and policy making without imputing missing qualitative information. Deep learning technologies have been widely used to infer the hidden features of passively collected mobile data. A deep neural network, however, is so deterministic that the probabilistic aspect of activity inference cannot be accommodated. In the present study, a stochastic approach (VAE-HMM) was devised to generate human activity chains by incorporating a variational autoencoder (VAE) with a hidden Markov model (HMM). Whereas an original HMM clusters data in the observational space, the proposed approach conducts clustering in a latent space with a smaller dimension. The VAE contributes by both reducing the input dimensionality and by sidestepping the overfit to sample data. The variational inference (VI) method was used to estimate the parameters of VAE-HMM within a Bayesian framework. Data drawn from spatio-temporal, demographic, socio-economic, and individual-specific sources were chosen as input variables to feed the model. The VAE-HMM can be trained in either a supervised or an unsupervised manner.

Theoretical study of the alkaline hydrogen oxidation reaction on Ni-Based nanocluster Catalysts: Effects of graphene supports and dopants

The Ni-based catalysts are considered to be the most promising non-noble metal catalysts for the hydrogen oxidation reaction (HOR) in alkaline fuel cells (AFCs). However, their further optimization is restricted by the unclear HOR mechanism. In this work, the density functional theory calculations were carried out to study the alkaline HOR on pure Ni13 and Ni13 supported on graphene (Ni13/G) and B- and N-doped graphene (Ni13/BG and Ni13/NG). The calculations indicate that the HOR proceeds preferentially through the Tafel-Volmer mechanism, in which the adsorption of H* plays a dominant role in the HOR activity. The graphene support and B/N dopants can apparently lower the d-band center of Ni13, thereby weakening the adsorption of H*, which leads to the HOR activity sequence of Ni13 < Ni13/G < Ni13/BG < Ni13/NG. Furthermore, compared to pure Ni13, the oxidation resistance of Ni13/G, Ni13/BG and Ni13/NG is also improved via elevating onset potential of the OH* phase and reducing energy barriers of the OH* reduction with H* and H2*. An efficient way to improve the HOR performance is tuning the adsorption of H* to accelerate the HOR rate and regulating the adsorption of OH* to enhance the antioxidant capacity.

As a single atom Pd outperforms Pt as the most active co-catalyst for photocatalytic H2 evolution

SummaryHere, we evaluate three different noble metal co-catalysts (Pd, Pt, and Au) that are present as single atoms (SAs) on the classic benchmark photocatalyst, TiO2. To trap the single atoms on the surface, we introduced controlled surface vacancies (Ti3+-Ov) on anatase TiO2 nanosheets by a thermal reduction treatment. After anchoring identical loadings of single atoms of Pd, Pt, and Au, we measure the photocatalytic H2 generation rate and compare it to the classic nanoparticle co-catalysts on the nanosheets. While nanoparticles yield the well-established the hydrogen evolution reaction activity sequence (Pt > Pd > Au), for the single atom form, Pd radically outperforms Pt and Au. Based on density functional theory (DFT), we ascribe this unusual photocatalytic co-catalyst sequence to the nature of the charge localization on the noble metal SAs embedded in the TiO2 surface.

Characterizing Student Proficiency in Software Modeling in Terms of Functions, Structures, and Behaviors

Software modeling is an integral practice for software engineers, especially as the complexity of software solutions increases. Unified Modeling Language (UML) is the industry standard for software modeling. however, it is often used incorrectly and misunderstood by novice software designers. This study is centered around understanding patterns of student proficiency of abstraction and systems thinking within a software modeling context. The participants of this study (n = 97) belonged to a systems analysis and design course that is primarily taken by second-year university students. The exam solutions to a case study from the course were evaluated for modeling proficiency. As evidence of proficiency in abstract thinking and systems thinking, we evaluated UML activity diagrams, class diagrams, and sequence diagrams and the alignment between these representations in terms of functions, structures, and behaviors. The results suggest students being proficient in modeling the functional aspects of an information system while facing some difficulty in capturing the structural and behavioral aspects of an information system. Clustering analysis revealed two groups within the sample, with one group displaying a significantly higher abstraction and systems thinking ability. Statistically significant correlations were also found between student proficiency of abstraction and their modeling proficiency in terms of functions, structures, and behaviors.