Subtle Errors Research Articles

Lost in translation: Barbie and subtitle discrepancies in China

I examine the subtle yet impactful subtitle translation errors in the Chinese version of Barbie (2023). These translation missteps undermine the film's feminist narratives and representations of gender, which reveals the translations’ capacity to distort the original English screenplay. Employing an interdisciplinary approach that integrates film studies, feminist theory and translation studies, I emphasise the instrumental role of subtitles as not just linguistic tools but narrative constructs that significantly influence the shaping and affirmation of gender identities. The analysis illuminates the pivotal role of subtitles in reflecting and shaping societal norms, offering insights for developing more culturally sensitive and empowered translation methodologies in the realm of international cinema.

When long jumps fall short: control-flow tracking and misuse detection for nonlocal jumps in C

The C programming language offers setjmp/ longjmp as a mechanism for nonlocal control flow. This mechanism has complicated semantics. As most developers do not encounter it day-to-day, they may be unfamiliar with all its intricacies – leading to subtle programming errors. At the same time, most static analyzers lack proper support, implying that otherwise sound tools miss whole classes of program deficiencies. We propose a concrete semantics of a subset of C with setjmp/ longjmp, where interprocedural longjmps are performed directly, as well as an equivalent formulation where such jumps are implemented via stack-unwinding at the call-sites. Reflecting this semantic equivalence, we propose an approach for lifting existing interprocedural analyses to support setjmp/ longjmp and to flag their misuse. To deal with the nonlocal semantics, our approach leverages side-effecting transfer functions, which, when executed, may additionally trigger contributions for program points that are not static control-flow successors. We showcase our analysis on a real-world example and propose a set of litmus tests for other analyzers.

Data Validation Utilizing Expert Knowledge and Shape Constraints

Data validation is a primary concern in any data-driven application, as undetected data errors may negatively affect machine learning models and lead to suboptimal decisions. Data quality issues are usually detected manually by experts, which becomes infeasible and uneconomical for large volumes of data. To enable automated data validation, we propose “shape constraint-based data validation,” a novel approach based on machine learning that incorporates expert knowledge in the form of shape constraints. Shape constraints can be used to describe expected (multivariate and nonlinear) patterns in valid data and enable the detection of invalid data that deviates from these expected patterns. Our approach can be divided into two steps: (1) shape-constrained prediction models are trained on data, and (2) their training error is analyzed to identify invalid data. The training error can be used as an indicator for invalid data because shape-constrained models can fit valid data better than invalid data. We evaluate the approach on a benchmark suite consisting of synthetic datasets, which we have published for benchmarking similar data validation approaches. Additionally, we demonstrate the capabilities of the proposed approach with a real-world dataset consisting of measurements from a friction test bench in an industrial setting. Our approach detects subtle data errors that are difficult to identify even for domain experts.

Bard, ChatGPT and 3DGPT: a scientometric analysis of generative AI tools and assessment of implications for mechanical engineering education

PurposeFollowing the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.Design/methodology/approachAs part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.FindingsThe study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).Originality/valueTo the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

Navigating the Labyrinth: Unraveling Security Threats and Vulnerabilities in Network Infrastructures

As our reliance on interconnected digital networks continues to grow, so does the complexity of security challenges. This paper delves into the intricate landscape of network security, exploring the evolving threats and vulnerabilities that jeopardize the integrity and confidentiality of information. From sophisticated cyber-attacks to subtle human errors, the spectrum of risks is vast. Through a comprehensive analysis, as a researcher aim to provide insights into emerging threats, assess vulnerabilities in network infrastructures, and propose strategies to fortify defenses. By understanding the ever-changing landscape of network security, organizations can proactively safeguard their digital assets and maintain the trust of users in an era of persistent and dynamic threats.

GA-Net: A geographical attention neural network for the segmentation of body torso tissue composition

PurposeBody composition analysis (BCA) of the body torso plays a vital role in the study of physical health and pathology and provides biomarkers that facilitate the diagnosis and treatment of many diseases, such as type 2 diabetes mellitus, cardiovascular disease, obstructive sleep apnea, and osteoarthritis. In this work, we propose a body composition tissue segmentation method that can automatically delineate those key tissues, including subcutaneous adipose tissue, skeleton, skeletal muscle tissue, and visceral adipose tissue, on positron emission tomography/computed tomography scans of the body torso. MethodsTo provide appropriate and precise semantic and spatial information that is strongly related to body composition tissues for the deep neural network, first we introduce a new concept of the body area and integrate it into our proposed segmentation network called Geographical Attention Network (GA-Net). The body areas are defined following anatomical principles such that the whole body torso region is partitioned into three non-overlapping body areas. Each body composition tissue of interest is fully contained in exactly one specific minimal body area. Secondly, the proposed GA-Net has a novel dual-decoder schema that is composed of a tissue decoder and an area decoder. The tissue decoder segments the body composition tissues, while the area decoder segments the body areas as an auxiliary task. The features of body areas and body composition tissues are fused through a soft attention mechanism to gain geographical attention relevant to the body tissues. Thirdly, we propose a body composition tissue annotation approach that takes the body area labels as the region of interest, which significantly improves the reproducibility, precision, and efficiency of delineating body composition tissues. ResultsOur evaluations on 50 low-dose unenhanced CT images indicate that GA-Net outperforms other architectures statistically significantly based on the Dice metric. GA-Net also shows improvements for the 95% Hausdorff Distance metric in most comparisons. Notably, GA-Net exhibits more sensitivity to subtle boundary information and produces more reliable and robust predictions for such structures, which are the most challenging parts to manually mend in practice, with potentially significant time-savings in the post hoc correction of these subtle boundary placement errors. Due to the prior knowledge provided from body areas, GA-Net achieves competitive performance with less training data. Our extension of the dual-decoder schema to TransUNet and 3D U-Net demonstrates that the new schema significantly improves the performance of these classical neural networks as well. Heatmaps obtained from attention gate layers further illustrate the geographical guidance function of body areas for identifying body tissues. Conclusions(i) Prior anatomic knowledge supplied in the form of appropriately designed anatomic container objects significantly improves the segmentation of bodily tissues. (ii) Of particular note are the improvements achieved in the delineation of subtle boundary features which otherwise would take much effort for manual correction. (iii) The method can be easily extended to existing networks to improve their accuracy for this application.

Dynamic Modeling of Motorized Spindle System with Unbalanced Mass and Spindle Inclination

The high-speed motorized spindle has been wildly used in the field of aerospace processing, due to its advantages such as high speed, high precision, and high efficiency. CNC machine tools used for processing aerospace products require high machining accuracy, and once the spindle fails, it will seriously affect the quality of product processing. Thus, it is important to study the faults of the spindle, especially the faults caused by subtle errors. In this work, a dynamic model of a spindle with unbalanced mass fault and spindle inclination fault is established, and the natural frequencies and mode shapes of the motorized spindle are calculated by using the whole transfer matrix method (WTMM). The deflections of the spindle initial end in the different situations are discussed when the two faults happen independently. The results show that the spindle end deflection of the same fault has different sensitivity at different speeds. At the third order of natural frequencies, the deflection of a motorized spindle is greatest regardless of the fault that occurs. Although the motorized spindle rotates at the same speed, different faults could cause different mode shapes. At the lower speed, when the unbalanced mass fault happens, the mode shape is in an arched shape, and while the spindle inclination fault happens, the mode shape is in a concave shape.

Pitfalls in applying gravitomagnetism to galactic rotation curve modelling

The flatness of galaxy rotation curves at large radii is generally considered to be a significant piece of evidence in support of the existence of dark matter. Several studies have claimed that post-Newtonian corrections to the Newtonian equations of galaxy dynamics may remove (at least to some degree) the need for dark matter. A few recent studies have examined these claims, and identified errors in their reasoning. We add to this critique by giving what we consider to be particularly simple and transparent description of the errors made in these post-Newtonian calculations, some of which were of a rather technical nature, others more fundamental, e.g. the loss of the correct relativistic scaling, promoting small corrections to order unity changes. Our work reinforces the orthodoxy that post-Newtonian effects are indeed too small to significantly alter galactic rotation curves, and will hopefully serve as a useful guide for others, pointing out subtle errors that one might inadvertently make in such calculations.

Brief temporal perturbations in somatosensory reafference disrupt perceptual and neural attenuation and increase supplementary motor area-cerebellar connectivity.

Intrinsic delays in sensory feedback can be detrimental for motor control. As a compensation strategy, the brain predicts the sensory consequences of movement via a forward model on the basis of a copy of the motor command. Using these predictions, the brain attenuates somatosensory reafference to facilitate the processing of exafferent information. Theoretically, this predictive attenuation is disrupted by (even minimal) temporal errors between the predicted and actual reafference; however, direct evidence of such disruption is lacking since previous neuroimaging studies contrasted nondelayed reafferent input with exafferent input. Here, we combined psychophysics with functional magnetic resonance imaging to test whether subtle perturbations in the timing of somatosensory reafference disrupt its predictive processing. Twenty-eight participants (14 women) generated touches on their left index finger by tapping a sensor with their right index finger. The touches on the left index finger were delivered close to the time of the two fingers' contact or with a temporal perturbation (i.e., 153 ms delay). We found that such a brief temporal perturbation disrupted the attenuation of the somatosensory reafference at both the perceptual and neural levels, leading to greater somatosensory and cerebellar responses and weaker somatosensory connectivity with the cerebellum, proportional to the perceptual changes. We interpret these effects as the failure of the forward model to predictively attenuate the perturbed somatosensory reafference. Moreover, we observed increased connectivity of the supplementary motor area with the cerebellum during the perturbations, which could indicate the return of the temporal prediction error to the motor centers.Significance statementOur brain receives somatosensory feedback from our movements with a delay. To counteract these delays, motor control theories postulate that the brain predicts the timing of somatosensory consequences of our movements and attenuates sensations received at that time. Thus, a self-generated touch feels weaker than an identical external touch. However, how subtle temporal errors between the predicted and actual somatosensory feedback perturb this predictive attenuation remains unknown. We show that such errors make the otherwise attenuated touch feel stronger, elicit stronger somatosensory responses, weaken cerebellar connectivity with somatosensory areas, and increase this connectivity with motor areas. These findings show that motor and cerebellar areas are fundamental in forming temporal predictions about the sensory consequences of our movements.

Verification and benchmarking relativistic electron beam transport through a background gas

It is necessary to establish confidence in high-consequence codes containing an extensive suite of physics algorithms in the regimes of interest. Verification problems allow code developers to assess numerical accuracy and increase confidence that specific sets of model physics were implemented correctly in the code. The two main verification techniques are code verification and solution verification. In this work, we present verification problems that can be used in other codes to increase confidence in simulations of relativistic beam transport. Specifically, we use the general plasma code EMPIRE to model and compare with the analytical solution to the evolution of the outer radial envelope of a relativistic charged particle beam. We also outline a benchmark test of a relativistic beam propagating through a vacuum and pressurized gas cell, and present the results between EMPIRE and the hybrid code GAZEL. Further, we discuss the subtle errors that were caught with these problems and detail lessons learned.

A conceptual framework for safe object initialization: a principled and mechanized soundness proof of the Celsius model

An object under initialization does not fulfill its class specification yet and can be unsafe to use as it may have uninitialized fields. It can sometimes be useful to call methods on such a partially initialized object in order to compute a complex initial value, or to let the object escape its constructor in order to create mutually recursive objects. However, inadvertent usage of uninitialized fields can lead to run-time crashes. Those subtle programming errors are not statically detected by most modern compilers. While many other features of object-oriented programming languages have been thoroughly studied over the years, object initialization lacks a simple, systematic, and principled treatment. Building on the insights of previous work, we identify a set of four core principles for safe initialization: monotonicity, authority, stackability, and scopability. We capture the essence of the principles with a minimal calculus, Celsius, and show that the principles give rise to a practical initialization system that strikes a balance between expressiveness and simplicity. The meta-theory of the system is entirely mechanized using the Coq proof assistant. We believe that our approach based on well-identified core principles sheds new light on the underlying mechanisms ensuring safety and could serve as a basis for language design when faced with similar challenges.

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing Bayesian differential privacy as the means to achieve a rigorous theoretical guarantee while providing a better privacy-utility trade-off. We demonstrate experimentally that our approach produces higher-fidelity samples compared to prior work, allowing to (1) detect more subtle data errors and biases, and (2) reduce the need for real data labelling by achieving high accuracy when training directly on artificial samples.

Chemical Cartography with APOGEE: Mapping Disk Populations with a 2-process Model and Residual Abundances

We apply a novel statistical analysis to measurements of 16 elemental abundances in 34,410 Milky Way disk stars from the final data release (DR17) of APOGEE-2. Building on recent work, we fit median abundance ratio trends [X/Mg] versus [Mg/H] with a 2-process model, which decomposes abundance patterns into a “prompt” component tracing core-collapse supernovae and a “delayed” component tracing Type Ia supernovae. For each sample star, we fit the amplitudes of these two components, then compute the residuals Δ[X/H] from this two-parameter fit. The rms residuals range from ∼0.01–0.03 dex for the most precisely measured APOGEE abundances to ∼0.1 dex for Na, V, and Ce. The correlations of residuals reveal a complex underlying structure, including a correlated element group comprised of Ca, Na, Al, K, Cr, and Ce and a separate group comprised of Ni, V, Mn, and Co. Selecting stars poorly fit by the 2-process model reveals a rich variety of physical outliers and sometimes subtle measurement errors. Residual abundances allow for the comparison of populations controlled for differences in metallicity and [α/Fe]. Relative to the main disk (R = 3–13 kpc), we find nearly identical abundance patterns in the outer disk (R = 15–17 kpc), 0.05–0.2 dex depressions of multiple elements in LMC and Gaia Sausage/Enceladus stars, and wild deviations (0.4–1 dex) of multiple elements in ω Cen. The residual abundance analysis opens new opportunities for discovering chemically distinctive stars and stellar populations, for empirically constraining nucleosynthetic yields, and for testing chemical evolution models that include stochasticity in the production and redistribution of elements.

Minimizing Subtle Errors in Computing Information of TCAM by Partial “N” Search Key Implementation

The primary memory of the computer gets corrupted due to microparticles that possess a great deal of energy. This causes transient kind of errors and it is called subtle errors. High corruption of data in computer systems has been occurred because of these errors. Major sources of subtle errors include ionic particles, huge radiations say example alpha, beta and gamma rays, protons, neutrons etc., When TCAM configuration changes from 120 nm to 20 nm technology, there was a drastic increase in subtle errors more than multiples of eight. Devices operated in low power regions are more prone to subtle errors. Memory storage devices like secondary memory element called cache, registers are more feasible to subtle errors. 1 Behavior of subtle errors never damage circuit configuration but changes the information content leading to erroneous data. The proposed architecture is called N-SEARCH KEY TCAM, where in this system, it has been predicted that occurrence of subtle errors are very small. This innovative method of detecting and correcting both single and burst type of subtle errors has many modules like TCAM, Multiplexer, control circuit and Error correction code frame. Moreover, the proposed architecture N-SEARCH KEY ternary memory uses a special LUT for matching process. A key named Search key was generated which contains don’t care bits for matching subtle errors. Unlike regular TCAM, this design uses sequential LUT which enhances matching time of order [Formula: see text](wnlog2[Formula: see text]. Initially, proposed scheme have been used to find single bit subtle errors by changing forbearance factor ([Formula: see text] and chances of occurrence of error among TCAM information. The design was enhanced to detect multiple subtle errors and rectified the same. If match not occurs with predetermined information, then IP filter forbids the unauthorized users from using global network resources, thus providing better information security. By the proposed method, number of improper segregation ratio decreases with increase in Forbearance ratio parameter and time required to detect and correct the same is also considerably reduced. By controlling the forbearance factor [Formula: see text], the best duration schemes can be chosen. Through the given architecture, average time consumption for error checking of subtle errors would range from 0.6 to 0.7 [Formula: see text]s.

An intrinsically-typed solution for the list-machine benchmark

Formal models are important tools in the programming language research community. However, such models are full of intricacies and, due to that, they are subject to subtle errors. Such failures motivated the usage of tools to ensure the correctness of these formalisms. One way to eliminate such errors is to encode models in a dependently-typed language in order to ensure its “correctness-by-construction”. In this paper, we use this idea to build a verified interpreter for the list-machine benchmark in the Agda programming language, comparing the results with formalizations developed by Appel et al.. We formalize the 14 tasks of the benchmark using roughly 14% of LOC compared to a Twelf solution, and 47% of LOC compared to a Coq solution, even without the use of proof automation. We also describe a solution to the second version of the benchmark and compare it with Appel’s et. al. Coq-based solution.

Formal Verification of Blockchain Smart Contracts via ATL Model Checking

A blockchain is a list of data blocks as a publicly distributed ledger, which are linked together using cryptography. By allowing Turing-complete programming languages to implement smart contracts, recent blockchains such as Ethereum can reduce needs in trusted intermediators, arbitrations and enforcement costs. However, subtle errors in smart contracts have induced an enormous financial loss—for examples, the DAO attack, Parity multisignature wallet attacks, and integer underflow/overflow attacks. To identify such errors in smart contracts, various researches are performed, which are based on static analysis and theorem proving. However, they only support inspection for pre-defined error patterns, or they cannot explore the whole searching space exhaustively or be fully automatic. Hence, in this paper, we propose a novel formal verification technique to analyze blockchain smart contracts by using ATL model checking. In our methodology, we represent the interaction between users and smart contracts into a two-player game and verify properties we want to check using MCMAS that is an efficient ATL model checker for multi-agent systems. Moreover, we present three case studies to show that our proposal can successfully identify subtle flaws in real world smart contracts.

An Expository Note on Unit - Gompertz Distribution with Applications

In a recent paper, Mazucheli et al. (2019) introduced the unit-Gompertz (UG) distribution and studied some of its properties. It is a continuous distribution with bounded support, and hence may be useful for modelling life-time phenomena. We present counter-examples to point out some subtle errors in their work, and subsequently correct them. We also look at some other interesting properties of this new distribution. Further, we also study some important reliability measures and consider some stochastic orderings associated with this new distribution.

A quantitative meta-analysis of face recognition deficits in autism: 40 years of research.

The ability to recognize an individual face is essential to human social interaction. Even subtle errors in this process can have huge implications for the way we relate to social partners. Because autism spectrum disorder (ASD) is characterized by deficits in social interaction, researchers have theorized about the potential role of atypical face identity processing to the symptom profile of ASD for more than 40 years. We conducted an empirical meta-analysis of this large literature to determine whether and to what extent face identity processing is atypical in ASD compared to typically developing (TD) individuals. We also tested the hypotheses that the deficit is selective to face identity recognition, not perception, and that methodological variation across studies moderates the magnitude of the estimated deficit. We identified 112 studies (5,390 participants) that generated 172 effect sizes from both recognition (k = 119) and discrimination (k = 53) paradigms. We used state-of-the-art approaches for assessing the validity and robustness of the analyses. We found comparable and large deficits in ASD for both face identity recognition (Hedge's g = -0.86) and discrimination (Hedge's g = -0.82). This means that the score of an average ASD individual is nearly 1 SD below the average TD individual on tasks assessing both aspects of face identity processing. These deficits generalize across age groups, sex, IQ scores, and task paradigms. These findings suggest that deficits in face identity processing may represent a core deficit in ASD. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Statistical learning as a reference point for memory distortions: Swap and shift errors.

Humans use regularities in the environment to facilitate learning, often without awareness or intent. How might such regularities distort long-term memory? Here, participants studied and reported the colors of objects in a long-term memory paradigm, uninformed that certain colors were sampled more frequently overall. When participants misreported an object's color, these errors were often centered around the average studied color (i.e., "Rich" color), demonstrating swap errors in long-term memory due to imposed statistical regularities. We observed such swap errors regardless of memory load, explicit knowledge, or the distance in color space between the correct color of the tested object and the Rich color. An explicit guessing strategy where participants intentionally made swap errors when uncertain could not fully account for our results. We discuss other potential sources of observed swap errors such as false memory and implicit biased guessing. Although less robust than swap errors, evidence was also observed for subtle shift errors towards or away from the Rich color dependent on the color distance between the correct color and the Rich color. Together, these findings of swap and shift errors provide converging evidence for memory distortion mechanisms induced by a reference point, bridging a gap in the literature between how attention to regularities similarly influences visual working memory and visual long-term memory.

Optimal prediction of synchronization-preserving races

Concurrent programs are notoriously hard to write correctly, as scheduling nondeterminism introduces subtle errors that are both hard to detect and to reproduce. The most common concurrency errors are (data) races, which occur when memory-conflicting actions are executed concurrently. Consequently, considerable effort has been made towards developing efficient techniques for race detection. The most common approach is dynamic race prediction: given an observed, race-free trace σ of a concurrent program, the task is to decide whether events of σ can be correctly reordered to a trace σ * that witnesses a race hidden in σ. In this work we introduce the notion of sync(hronization)-preserving races. A sync-preserving race occurs in σ when there is a witness σ * in which synchronization operations (e.g., acquisition and release of locks) appear in the same order as in σ. This is a broad definition that strictly subsumes the famous notion of happens-before races. Our main results are as follows. First, we develop a sound and complete algorithm for predicting sync-preserving races. For moderate values of parameters like the number of threads, the algorithm runs in Õ( N ) time and space, where N is the length of the trace σ. Second, we show that the problem has a Ω( N /log 2 N ) space lower bound, and thus our algorithm is essentially time and space optimal. Third, we show that predicting races with even just a single reversal of two sync operations is NP-complete and even W1-hard when parameterized by the number of threads. Thus, sync-preservation characterizes exactly the tractability boundary of race prediction, and our algorithm is nearly optimal for the tractable side. Our experiments show that our algorithm is fast in practice, while sync-preservation characterizes races often missed by state-of-the-art methods.