Implicit Assumption Research Articles

Modification of the RUSLE slope length factor based on a multiple flow algorithm considering vertical leakage at karst landscapes

Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing area. The existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope length. The objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length, and to evaluate its predictive capability at different resolution DEMs. Transfer grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be zero. Using the multiple flow direction algorithm united with the transfer grid (MFDTG), the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope lengths. The effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern China. The simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5% showed that the relationship between surface-runoff intensity and slope length was a negative power function. Estimated by the proposed modified L-equation ((alx(b+1)/22.13)m), the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1, 5, 25 and 90 m resolutions respectively. This study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst landscapes. Particular attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.

Protracted displacement and housing systems in intermediary cities: the case of Syrians in Torbalı, Türkiye

This article investigates the dynamics of complex housing systems within the context of large-scale protracted displacement in Turkey/Türkiye. It presents new empirical findings from a qualitative study conducted in Torbalı, a rapidly growing intermediate city with a significant population of Syrian displaced people. Drawing on theoretical and conceptual insights from housing studies, urban studies and migration studies, the article assesses the ways in which displacement materialises in place through housing and contributes to city-making and urbanisation processes informally, incrementally, and in locally and historically contingent manners. We argue that the forms and dynamics of emerging housing exhibit both continuity but most markedly significant disjuncture from past housing trajectories in Torbalı. This challenges the implicit assumption of legal uniformity of self-builders common in incremental housing debates and suggests that the notion of incremental housing has limited relevance in contexts of protracted urban displacement. Furthermore, findings underline the significance of legal dimensions in energising housing informalities; in grading socio-legal statuses of resident populations; in bounding displaced people’s mobilities; in demarcating labour flows; in moulding rental markets; and in directing the flows of housing materials. These in turn shape current and future urban built environments and mould the ways in which the urbanisation of refuge manifests.

Non-isothermal consolidation: A systematic evaluation of two implementations based on multiphase and Richards equations

In this paper, the governing equations of non-isothermal two-phase flow in unsaturated, deformable porous media are presented based on different representations of the gaseous phase along with their implementations in the open-source FEM code OpenGeoSys 6. As one implementation utilises the Richards equation to represent the unsaturated system whereas the second implementation relies on a two-component two-phase flow formulation, the impact of a free gaseous phase on model predictions is illustrated by a series of tests of increasing complexity, motivated by similar investigations in Task C of the DECOVALEX 2023 project. We demonstrate that the governing equations of both implementations collapse and become identical in fully saturated regions and that the numerical implementations remain stable in the special cases. Furthermore, the implicit assumptions made in the Richards equation are explicitly applied to the physically more complex and comprehensive TH2M model. Thus, the impact of a constant residual gas pressure on the model predictions is investigated. Finally, the examples chosen for this work feature thermal consolidation and pore fluid pressurisation effects in the Opalinus Clay due to heat release from a nuclear waste canister as well as dry-out with subsequent resaturation of a bentonite buffer around the heat source. All test cases are evaluated on the level of the governing equations of each model as well as on the level of results obtained by each model for a discussion of conceptual model uncertainty.

Breaking and Fixing Garbled Circuits When a Gate has Duplicate Input Wires

Garbled circuits are a fundamental cryptographic primitive that allows two or more parties to securely evaluate an arbitrary Boolean circuit without revealing any information beyond the output using a constant number of communication rounds. Garbled circuits have been introduced by Yao (FOCS’86) and generalized to the multi-party setting by Beaver, Micali and Rogaway (STOC’90). Since then, several works have improved their efficiency by providing different garbling schemes and several implementations exist. Starting with the seminal Fairplay compiler (USENIX Security’04), several implementation frameworks decoupled the task of compiling the function to be evaluated into a Boolean circuit from the engine that securely evaluates that circuit, e.g., using a secure two-party computation protocol based on garbled circuits. In this paper, we show that this decoupling of circuit generation and evaluation allows a subtle attack on several prominent garbling schemes. It occurs when violating the implicit assumption on the circuit that gates have different input wires which is most often not explicitly specified in the respective papers. The affected garbling schemes use separate calls to a deterministic encryption function for the left and right input wire of a gate to derive pseudo-random encryption pads that are XORed together. When a circuit contains a gate where the left and right input wire are the same, these two per-wire encryption pads cancel out and we demonstrate that this can result in a complete break of privacy. We show how the vulnerable garbling schemes can be fixed easily.

QuanDA: GPU Accelerated Quantitative Deep Neural Network Analysis

Over the past years, numerous studies demonstrated the vulnerability of deep neural networks (DNNs) to make correct classifications in the presence of small noise. This motivated the formal analysis of DNNs to ensure they delineate acceptable behavior. However, in case the DNN’s behavior is unacceptable for the desired application, these qualitative approaches are ill-equipped to determine the precise degree to which the DNN behaves unacceptably. Towards this, we propose a novel quantitative DNN analysis framework, QuanDA, which does not only check if the DNN delineates certain behavior, but also provides the estimated probability of the DNN to delineate this particular behavior. Unlike the (few) available quantitative DNN analysis frameworks, QuanDA does not use any implicit assumptions on the probability distribution of the hidden nodes, which enables the framework to propagate close to real probability distributions of the hidden node values to each proceeding DNN layer. Furthermore, our framework leverages CUDA to parallelize the analysis, enabling high-speed GPU implementation for fast analysis. The applicability of the framework is demonstrated using the ACAS Xu benchmark, to provide reachability probability estimates for all network nodes. Moreover, this paper also provides potential applications of QuanDA for the analysis of the DNN safety properties.

Saturation-Tolerant Prescribed Control for Nonlinear Time-Delay Systems

This paper studies the problem of Saturation-tolerant Prescribed Control (SPC) for a class of nonlinear time-delay systems with unknown control directions. We propose a unified Finite-time Tunnel Prescribed Performance (FTPP), which not only provides more tight allowable set leading to smaller overshoot, but also drives the tracking error into the prescribed set within a known time. To remove the implicit assumption that both input and performance constraints need to be satisfied simultaneously, an auxiliary system is developed to establish a balance between these two constraints instead of ignoring their interconnection and conflict. With aid of its generated nonnegative signals, the developed Saturation-tolerant Prescribed Performance (SPP) possesses flexible performance. Namely, SPP can temporarily enlarge the performance boundaries to guarantee both constraints when input saturation occurs, and fast recover back to the prescribed boundaries when input saturation disappears. Consequently, a low-complexity SPC for uncertain nonlinear systems is developed, which can always guarantee both input and performance constraints even under unknown time delay and unknown control directions. Finally, comparative simulation is provided to illustrate the merits of the presented control strategy.

The slow rate of working memory consolidationfrom vision is a structural limit.

The speed with which information from vision is transformed into working memory (WM) representations that resist interference from ongoing perception and cognition is the subject of conflicting results. Using distinct paradigms, researchers have arrived at estimates of the consolidation time course ranging from 25 ms to 1 s - a range of more than an order of magnitude. However, comparisons of consolidation duration across very different estimation paradigms rely on the implicit assumption that WM consolidation speed is a stable, structural constraint of the WM system. The extremely large variation in WM consolidation speed estimates across measurement approaches motivated the current work's goal of determining whether consolidation speed truly is a stable structural constraint of WM encoding, or instead might be under strategic control as suggested by some accounts. By manipulating the relative task priority of WM encoding and a subsequent sensorimotor decision in a dual-task paradigm, the current experiments demonstrate that the long duration of WM consolidation does not change as a result of task-specific strategies. These results allow comparison of WM consolidation across estimation approaches, are consistent with recent multi-phase WM consolidation models, and are consistent with consolidation duration being an inflexible structural limit.

Searches for new physics with boosted top quarks in the MadAnalysis 5 and Rivet frameworks

High-momentum top quarks are a natural physical system in collider experiments for testing models of new physics, and jet substructure methods are key both to exploiting their largest decay mode and to assuaging resolution difficulties as the boosted system becomes increasingly collimated in the detector. To be used in new-physics interpretation studies, it is crucial that related methods get implemented in analysis frameworks allowing for the reinterpretation of the results of the LHC such as MadAnalysis 5 and Rivet. We describe the implementation of the HEPTopTagger algorithm in these two frameworks, and we exemplify the usage of the resulting functionalities to explore the sensitivity of boosted top reconstruction performance to new physics contributions from the Standard Model Effective Field Theory. The results of this study lead to important conclusions about the implicit assumption of Standard-Model-like top quark decays in associated collider analyses, and for the prospects to constrain the Standard Model Effective Field Theory via kinematic observables built from boosted semi-leptonic tbar{t} events selected using HEPTopTagger.

Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-contrast Imaging in the Presence of Non-Gaussian Noise

Over the past decade, hundreds of nights have been spent on the world’s largest telescopes to search for and directly detect new exoplanets using high-contrast imaging (HCI). Thereby, two scientific goals are of central interest: first, to study the characteristics of the underlying planet population and distinguish between different planet formation and evolution theories. Second, to find and characterize planets in our immediate solar neighborhood. Both goals heavily rely on the metric used to quantify planet detections and nondetections. Current standards often rely on several explicit or implicit assumptions about noise. For example, it is often assumed that the residual noise after data postprocessing is Gaussian. While being an inseparable part of the metric, these assumptions are rarely verified. This is problematic as any violation of these assumptions can lead to systematic biases. This makes it hard, if not impossible, to compare results across data sets or instruments with different noise characteristics. We revisit the fundamental question of how to quantify detection limits in HCI. We focus our analysis on the error budget resulting from violated assumptions. To this end, we propose a new metric based on bootstrapping that generalizes current standards to non-Gaussian noise. We apply our method to archival HCI data from the NACO instrument at the Very Large Telescope and derive detection limits for different types of noise. Our analysis shows that current standards tend to give detection limits that are about one magnitude too optimistic in the speckle-dominated regime. That is, HCI surveys may have excluded planets that can still exist.

GenKL: An Iterative Framework for Resolving Label Ambiguity and Label Non-conformity in Web Images Via a New Generalized KL Divergence

Web image datasets curated online inherently contain ambiguous in-distribution instances and out-of-distribution instances, which we collectively call non-conforming (NC) instances. In many recent approaches for mitigating the negative effects of NC instances, the core implicit assumption is that the NC instances can be found via entropy maximization. For “entropy” to be well-defined, we are interpreting the output prediction vector of an instance as the parameter vector of a multinomial random variable, with respect to some trained model with a softmax output layer. Hence, entropy maximization is based on the idealized assumption that NC instances have predictions that are “almost” uniformly distributed. However, in real-world web image datasets, there are numerous NC instances whose predictions are far from being uniformly distributed. To tackle the limitation of entropy maximization, we propose (alpha , beta )-generalized KL divergence, {mathcal {D}}_{text {KL}}^{alpha , beta }(pVert q), which can be used to identify significantly more NC instances. Theoretical properties of {mathcal {D}}_{text {KL}}^{alpha , beta }(pVert q) are proven, and we also show empirically that a simple use of {mathcal {D}}_{text {KL}}^{alpha , beta }(pVert q) outperforms all baselines on the NC instance identification task. Building upon (alpha ,beta )-generalized KL divergence, we also introduce a new iterative training framework, GenKL, that identifies and relabels NC instances. When evaluated on three web image datasets, Clothing1M, Food101/Food101N, and mini WebVision 1.0, we achieved new state-of-the-art classification accuracies: 81.34%, 85.73% and 78.99%/92.54% (top-1/top-5), respectively.

What conditions favor high-potential entrepreneurship? Unpacking the nexus between the industrial structure and startup typologies

In this paper, we question the implicit assumption that more entrepreneurship drives more innovation and growth, asserting that specific typologies of entrepreneurship are responsible for these phenomena. A growing number of studies has analyzed this relationship while focusing on the overall level of entrepreneurship. This paper exploits recent advancements in measuring the sophistication and connectedness of economic systems, brought by the notions of economic complexity and relatedness, to study the nexus of industrial structure and high-potential forms of entrepreneurship. The present study uses a panel dataset for Italy for the period 2015–2019. The results show a differentiated pattern among the high-potential startups considered, with relatedness and complexity having a positive effect for innovative startups, a negative one for high-growth startups, and no effect for pioneers. These results inform potential entrepreneurs of the importance of analyzing how external conditions can have distinctive effects on the process of opportunity identification among different typologies of high-potential startups.

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

We review and reflect on fairness notions proposed in machine learning literature and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We survey dynamic fairness inquiries and further consider the long-term impact induced by current prediction and decision. We present a flowchart that encompasses implicit assumptions and expected outcomes of different fairness inquiries on the data-generating process, the predicted outcome, and the induced impact, respectively. We demonstrate the importance of matching the mission (what kind of fairness to enforce) and the means (which appropriate fairness spectrum to analyze) to fulfill the intended purpose.

Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development.

The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused on randomized clinical trials. The intention of the Early Development Estimand Nexus (EDEN), a task force of the cross-industry Oncology Estimand Working Group (www.oncoestimand.org), is to apply it to single arms Phase 1b or Phase 2 trials designed to detect a treatment-related efficacy signal, typically measured by objective response rate. Key recommendations regarding the estimand attributes include that in a single arm early clinical trial, the treatment attribute should start when the first dose is received by the participant. Focusing on the estimation of an absolute effect, the population-level summary measure should reflect only the property used for the estimation. Another major component introduced in the ICH E9 addendum is the definition of intercurrent events and the associated possible ways to handle them. Different strategies reflect different clinical questions of interest that can be answered based on the journeys an individual subject can take during a trial. We provide detailed strategy recommendations for intercurrent events typically seen in early-stage oncology. We highlight where implicit assumptions should be made transparent as whenever follow-up is suspended, a while-on-treatment strategy is implied.

Unpaired, unsupervised domain adaptation assumes your domains are already similar.

Unsupervised domain adaptation is a popular method in medical image analysis, but it can be tricky to make it work: without labels to link the domains, domains must be matched using feature distributions. If there is no additional information, this often leaves a choice between multiple possibilities to map the data that may be equally likely but not equally correct. In this paper we explore the fundamental problems that may arise in unsupervised domain adaptation, and discuss conditions that might still make it work. Focusing on medical image analysis, we argue that images from different domains may have similar class balance, similar intensities, similar spatial structure, or similar textures. We demonstrate how these implicit conditions can affect domain adaptation performance in experiments with synthetic data, MNIST digits, and medical images. We observe that practical success of unsupervised domain adaptation relies on existing similarities in the data, and is anything but guaranteed in the general case. Understanding these implicit assumptions is a key step in identifying potential problems in domain adaptation and improving the reliability of the results.

Prescribed Time Recovery From State Constraint Violation via Approximation-Free Control Approach

For systems with soft state constraints, initial violation in such constraints is acceptable if no feasible control strategy capable of maintaining such constraints exists or an excessively large amount of energy consumption is required for constraint satisfaction. However, whenever the constraint violation is detected, it is highly desirable that the states be regulated back to satisfy the constraints as soon as possible. In this paper, an approximation-free state feedback control scheme is proposed for unknown second-order nonlinear systems, capable of recovering the states from constraint violation to constraint satisfaction within the prescribed time. Besides, the implicit assumption of compatibility between different constraints in most existing works is removed, and a sufficient condition for compatibility of state constraints is established for unknown second-order systems. The proposed self-recovery control scheme has the following features: 1) the recovery time of the states can be preset by the user regardless of the initial conditions; 2) the proposed controller does not need any explicit information on system model, thus has strong robustness against model uncertainties and disturbances; and 3) no approximation technique (e.g., neural network and fuzzy system) is involved, rendering the proposed control structurally simple and computationally inexpensive.

Bloodstain pattern analysis & Bayes: A case report

The findings from a bloodstain pattern analysis (BPA) may assist in formulating or falsifying scenarios that are considered in the investigative stages of a criminal investigation. When a case proceeds to trial the bloodstain pattern expert may be asked about the relevance of their findings given scenarios that are proposed by the prosecution and defense counsel. Such opinions provided by an expert are highly relevant to police investigation or legal proceedings, but the reasoning behind the opinion or implicit assumptions made by the expert may not be transparent.A proper framework for the evaluation of forensic findings has been developed since the late twentieth century, based on the hierarchy of propositions, Bayesian reasoning and a model for case assessment and interpretation. This framework, when implemented in casework, mitigates some of the risks of cognitive biases, and makes the reasoning and scientific basis for the opinion transparent. This framework is broadly used across forensic science disciplines. In this paper we describe its application to the field of BPA using a case example from the Netherlands Forensic Institute (NFI).

Zhuangzi and collaboration in animals: a critical conceptual analysis of shared intentionality.

Shared intentionality is a specific form of shared agency where a group can be understood to have an intention. It has been conjectured that humans are better equipped for collaboration than other animals because humans but not other great apes share intentions. However, exporting shared intentionality from a debate about the ontology of mental state attributions like intentions to groups does not seamlessly lend itself to evolutionary science. To explore and de-center the implicit assumptions of Western conceptions of cooperation, I look at Zhuangzi's philosophy of (in)action. This philosophy treats the actions of individuals as always a form of co-action alongside other agencies to whom one must adapt. Thinking of collaboration as a product of skillful co-action, not shared intention, sidesteps asking about cooperation in "kinds" or levels. Instead, it directs attention to the know-how and behavioral flexibility needed to make our constant coordination adaptive.

Counterfactuals for the Future

Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the structure and stability of exogenous variables in the system being modeled --- an assumption that is reasonable in many settings where counterfactuals are used. In this work, we consider cases where we might reasonably make a different assumption about exogenous variables; namely, that the exogenous noise terms of each unit do exhibit some unit-specific structure and/or stability. This leads us to a different use of counterfactuals --- a forward-looking rather than retrospective counterfactual. We introduce "counterfactual treatment choice," a type of treatment choice problem that motivates using forward-looking counterfactuals. We then explore how mismatches between interventional versus forward-looking counterfactual approaches to treatment choice, consistent with different assumptions about exogenous noise, can lead to counterintuitive results.

Theory and methods of the multiverse: an application for panel-based models

Multiverse analysis involves systematically sampling a vast set of model specifications, known as a multiverse, to estimate the uncertainty surrounding the validity of a scientific claim. By fitting these specifications to a sample of observations, statistics are obtained as analytical results. Examining the variability of these statistics across different groups of model specifications helps to assess the robustness of the claim and gives insights into its underlying assumptions. However, the theoretical premises of multiverse analysis are often implicit and not universally agreed upon. To address this, a new formal categorisation of the analytical choices involved in modelling the set of specifications is proposed. This method of indexing the specification highlights that the sampling structure of the multiversal sample does not conform to a model of independent and identically distributed draws of specifications and that it can be modelled as an information network instead. Hamming’s distance is proposed as a measure of network distance, and, with an application to a panel dataset, it is shown how this approach enhances transparency in procedures and inferred claims and that it facilitates the check of implicit parametric assumptions. In the conclusions, the proposed theory of multiversal sampling is linked to the ongoing debate on how to weigh a multiverse, including the debate on the epistemic value of crowdsourced multiverses.

Prior and posterior checking of implicit causal assumptions.

Causal inference practitioners have increasingly adopted machine learning techniques with the aim of producing principled uncertainty quantification for causal effects while minimizing the risk of model misspecification. Bayesian nonparametric approaches have attracted attention as well, both for their flexibility and their promise of providing natural uncertainty quantification. Priors on high-dimensional or nonparametric spaces, however, can often unintentionally encode prior information that is at odds with substantive knowledge in causal inference-specifically, the regularization required for high-dimensional Bayesian models to work can indirectly imply that the magnitude of the confounding is negligible. In this paper, we explain this problem and provide tools for (i) verifying that the prior distribution does not encode an inductive bias away from confounded models and (ii) verifying that the posterior distribution contains sufficient information to overcome this issue if it exists. We provide a proof-of-concept on simulated data from a high-dimensional probit-ridge regression model, and illustrate on a Bayesian nonparametric decision tree ensemble applied to a large medical expendituresurvey.