Combining a Locally Restricted Anisotropic Spatial Kernel with an ETAS Incomplete Model for Better Forecasts of the 2019 Ridgecrest Sequence

Strong earthquakes cause aftershock sequences that are clustered in time according to a power decay law, and in space along their extended rupture, shaping a typically elongate pattern of aftershock locations. A widely used approach to model seismic clustering is the Epidemic Type Aftershock Sequence (ETAS) model, that shows three major biases: First, the conventional ETAS approach assumes isotropic spatial triggering, which stands in conflict with observations and geophysical arguments for strong earthquakes. Second, the spatial kernel has unlimited extent, allowing smaller events to exert disproportionate trigger potential over an unrealistically large area. Third, the ETAS model assumes complete event records and neglects inevitable short-term aftershock incompleteness as a consequence of overlapping coda waves. These three effects can substantially bias the parameter estimation and particularly lead to underestimated cluster sizes. In this article, we combine the approach of Grimm (2021), which introduced a generalized anisotropic and locally restricted spatial kernel, with the ETAS-Incomplete (ETASI) time model of Hainzl (2021), to define an ETASI space-time model with flexible spatial kernel that solves the abovementioned shortcomings. We apply different model versions to a triad of forecasting experiments of the 2019 Ridgecrest sequence, and evaluate the prediction quality with respect to cluster size, largest aftershock magnitude and spatial distribution. The new model provides the potential of more realistic simulations of on-going aftershock activity, e.g.~allowing better predictions of the probability and location of a strong, damaging aftershock, which might be beneficial for short term risk assessment and desaster response.

How does the legacy of colonialism in Southeast Asia manifest itself in contemporary regional attitudes and practices towards looting and collection?

<p>Cultural property trafficking continues to be and is growing as an issue despite increased legislation, international agreements and public interest, particularly since the seminal 1970 UNESCO Convention. For criminology, the challenge is to take into account the distinct and complex characteristics of cultural property trade and trafficking in order to aid in controlling, regulating and preventing crime in a way that resonates with those it seeks to target. However, the mainstream approach to this issue relies uncritically upon a dominant and simplistic narrative of transnational cultural property movement from Global South sources to Global North markets, which renders significant regional and processes invisible and creates an incomplete model of reality. By incorporating a postcolonial framework and interviewing market actors in the cultural property world, this thesis aims to fill a gap in the discipline by examining how colonial narratives, frameworks and structures still inform modern attitudes to cultural property trade and trafficking, which has emerged from the same history. As a rich source region with a healthy cultural property market, Southeast Asia is the chosen case study; however, though the conclusions drawn originate in this specific context, the methodology used is applicable beyond this scope. The findings indicate that though cultural property collection is accompanied by a shadow of illicitness, market actors are able to justify their activities by not only relying on familiar colonial tropes and narratives of custodianship and education, but also pragmatically referring to the moralities and identities of a post-colonial age. Additionally, the social structures fundamental to the cultural property world are also, to some extent, the product of a certain history, and identity formation and projection through cultural capital are key concepts in understanding the impetus for collection. Ultimately, actors’ understandings of an authentic object as one that is of a particular style and, critically, of a particular age and condition, is synonymous with colonially influenced attitudes, and is inherently linked to the damage that anti-trafficking legislation seeks to mitigate.</p>

How does the legacy of colonialism in Southeast Asia manifest itself in contemporary regional attitudes and practices towards looting and collection?

<p>Cultural property trafficking continues to be and is growing as an issue despite increased legislation, international agreements and public interest, particularly since the seminal 1970 UNESCO Convention. For criminology, the challenge is to take into account the distinct and complex characteristics of cultural property trade and trafficking in order to aid in controlling, regulating and preventing crime in a way that resonates with those it seeks to target. However, the mainstream approach to this issue relies uncritically upon a dominant and simplistic narrative of transnational cultural property movement from Global South sources to Global North markets, which renders significant regional and processes invisible and creates an incomplete model of reality. By incorporating a postcolonial framework and interviewing market actors in the cultural property world, this thesis aims to fill a gap in the discipline by examining how colonial narratives, frameworks and structures still inform modern attitudes to cultural property trade and trafficking, which has emerged from the same history. As a rich source region with a healthy cultural property market, Southeast Asia is the chosen case study; however, though the conclusions drawn originate in this specific context, the methodology used is applicable beyond this scope. The findings indicate that though cultural property collection is accompanied by a shadow of illicitness, market actors are able to justify their activities by not only relying on familiar colonial tropes and narratives of custodianship and education, but also pragmatically referring to the moralities and identities of a post-colonial age. Additionally, the social structures fundamental to the cultural property world are also, to some extent, the product of a certain history, and identity formation and projection through cultural capital are key concepts in understanding the impetus for collection. Ultimately, actors’ understandings of an authentic object as one that is of a particular style and, critically, of a particular age and condition, is synonymous with colonially influenced attitudes, and is inherently linked to the damage that anti-trafficking legislation seeks to mitigate.</p>

The Perception Level of Thinking Models in General and Vocational Education

The revised Bloom’s taxonomy consists of six level which are remember (C1), understand (C2), apply (C3), analyze (C4), evaluate (C5) and create (C6). Every level of qualification sometimes applies different level of taxonomy, either complete model (C1 to C6) or incomplete model (C1 to C3, C2 to C4, C4 to C6). Thus, this research aimed to find the proportion of the complete and incomplete level of thinking models and their suitability in primary and secondary education. This research was a descriptive research using the ex-post facto approach. from six Educational Institutions for Educators in Java, Indonesia with 82 educational experts. The data was collected using survey questionnaire and was analyzed using descriptive and inferential statistics. The results showed that: (1) for the complete level of thinking model, elementary school covered the C1–C6 level of thinking with the most substantial proportion was C1; in junior high school was C2; and senior/vocational high school was C3; (2) for the incomplete level of thinking, elementary school covered the C1–C3 level of thinking with the most significant proportion was C1; junior high school covered the C1–C4 levels and the most substantial proportion was the C2; and senior/vocational high school included the C1–C5 levels with the most significant portion of the C3; (3) the suitable level of thinking was the complete model whereas the current practice referred to the incomplete model.

Upgrading Behavioral Models for the Design of Digital Predistorters

This work presents a strategy to upgrade models for power amplifier (PA) behavioral modeling and digital predistortion (DPD). These incomplete structures are the consequence of nonlinear order and memory depth model truncation with the purpose of reducing the demand of the limited computational resources available in standard processors. On the other hand, the alternative use of model structures pruned a priori does not guarantee that every significant term is included. To improve the limited performance of an incomplete model, a general procedure to augment its structure by incorporating significant terms is demonstrated. The sparse nature of the problem allows a successive search incorporating additional terms with higher nonlinear order and memory depth. This approach is investigated in the modeling and linearization of a commercial class AB PA operating at a compression point of about 6 dB, and a class J PA operating near saturation. Results highlight the capabilities of this upgrading procedure in the improvement of linearization capabilities of DPDs.

Attribute-Associated Neuron Modeling and Missing Value Imputation for Incomplete Data

The imputation of missing values is an important research content in incomplete data analysis. Based on the auto associative neural network (AANN), this paper conducts regression modeling for incomplete data and imputes missing values. Since the AANN can estimate missing values in multiple missingness patterns efficiently, we introduce incomplete records into the modeling process and propose an attribute cross fitting model (ACFM) based on AANN. ACFM reconstructs the path of data transmission between output and input neurons and optimizes the model parameters by training errors of existing data, thereby improving its own ability to fit relations between attributes of incomplete data. Besides, for the problem of incomplete model input, this paper proposes a model training scheme, which sets missing values as variables and makes missing value variables update with model parameters iteratively. The method of local learning and global approximation increases the precision of model fitting and the imputation accuracy of missing values. Finally, experiments based on several datasets verify the effectiveness of the proposed method.

Adaptive Supervision of Patterns in Discrete Event Systems: Application to Crisis Management

Crisis management is currently an important challenge for medical service and research. This motivates the development of new decision system approaches to assist (or to guide) the decision makers. A crisis management is a special type of collaboration involving several actors. The context and characteristics of crisis such as extent of actors and their roles make the crisis management more difficult in order to take decision. In this paper, we propose to model the interaction between different actors involved in crisis management. For this purpose we use finite state automaton in order to optimize the emergency response to the crisis and to reduce the disastrous consequences on people and environment. Thus, an adaptive supervision method is proposed. Therefore, we address the problem of diagnosis and prediction (prognostic) given an incomplete model of the discrete event systems of a crisis situation. When the model is incomplete, we introduce learning into the diagnoser (diagnosis module) construction.

Robust Contract Designs: Linear Contracts and Moral Hazard

Linear contracts and their variants are quite popular in practice, for example, salesforce incentives and chief executive officer compensation. However, agency theory typically stipulates complex contract forms. Yimin Yu and Xiangyin Kong provide an alternative explanation for the popularity of linear contracts: the robustness to model uncertainty renders the linear or generalized linear forms of the contracts under moral hazard. They adopt the worst-case decision criterion, and robust incentive compatibility to ensure that the agent always behaves. The results are robust to general effort-contingent distributions and the risk-averse agent. These findings also shed light on how to design robust contracts when firms are facing model uncertainty or incomplete model information.

Extracting safe thread schedules from incomplete model checking results

Model checkers frequently fail to completely verify a concurrent program, even if partial-order reduction is applied. The verification engineer is left in doubt whether the program is safe and the effort toward verifying the program is wasted. We present a technique that uses the results of such incomplete verification attempts to construct a (fair) scheduler that allows the safe execution of the partially verified concurrent program. This scheduler restricts the execution to schedules that have been proven safe (and prevents executions that were found to be erroneous). We evaluate the performance of our technique and show how it can be improved using partial-order reduction. While constraining the scheduler results in a considerable performance penalty in general, we show that in some cases our approach—somewhat surprisingly—even leads to faster executions.

Discovery of Incomplete Diagnostic Model based on Learning

The model-based diagnosis uses the common reasoning of offline model and online observation to obtain whether and why faults occur. However, the diagnosis is based on the premise of complete model. Once there are unknown behaviors in the diagnosis process, the diagnosis results will not be obtained. In this paper, a method of incomplete model discovery based on online diagnosis process is proposed: In the online diagnosis process, the data of the complete model are learned and the model is trained and adjusted. When the incomplete behavior is found, the nature of the incomplete behavior is determined according to the historical diagnostic data and online observation data, and the corresponding transition/state/event is generated and added to the model to further obtain the definite diagnosis results.