Plausible Ones Research Articles

Adaptive rewiring: a general principle for neural network development

The nervous system, especially the human brain, is characterized by its highly complex network topology. The neurodevelopment of some of its features has been described in terms of dynamic optimization rules. We discuss the principle of adaptive rewiring, i.e., the dynamic reorganization of a network according to the intensity of internal signal communication as measured by synchronization or diffusion, and its recent generalization for applications in directed networks. These have extended the principle of adaptive rewiring from highly oversimplified networks to more neurally plausible ones. Adaptive rewiring captures all the key features of the complex brain topology: it transforms initially random or regular networks into networks with a modular small-world structure and a rich-club core. This effect is specific in the sense that it can be tailored to computational needs, robust in the sense that it does not depend on a critical regime, and flexible in the sense that parametric variation generates a range of variant network configurations. Extreme variant networks can be associated at macroscopic level with disorders such as schizophrenia, autism, and dyslexia, and suggest a relationship between dyslexia and creativity. Adaptive rewiring cooperates with network growth and interacts constructively with spatial organization principles in the formation of topographically distinct modules and structures such as ganglia and chains. At the mesoscopic level, adaptive rewiring enables the development of functional architectures, such as convergent-divergent units, and sheds light on the early development of divergence and convergence in, for example, the visual system. Finally, we discuss future prospects for the principle of adaptive rewiring.

Interplay of Semantic Plausibility and Word Order Canonicity in Sentence Processing of People With Aphasia Using a Verb-Final Language.

The Western Aphasia Battery is widely used to assess people with aphasia (PWA). Sequential Commands (SC) is one of the most challenging subtests for PWA. However, test items confound linguistic factors that make sentences difficult for PWA. The current study systematically manipulated semantic plausibility and word order in sentences like those in SC to examine how these factors affect comprehension deficits in aphasia. Fifty Korean speakers (25 PWA and 25 controls) completed a sentence-picture matching task that manipulated word order (canonical vs. noncanonical) and semantic plausibility (plausible vs. less plausible). Analyses focused on accuracy and aimed to identify sentence types that best discriminate the groups. Additionally, we explored which sentence type serves as the best predictor of aphasia severity. PWA demonstrated greater difficulties in processing less plausible sentences than plausible ones compared to the controls. Across the groups, noncanonical and less plausible sentences elicited lower accuracy than canonical and plausible sentences. Notably, the accuracy of PWA and control groups differed in noncanonical and less plausible sentences. Additionally, aphasia severity significantly correlated with less plausible sentences. Even in languages with flexible word order, PWA find it challenging to process sentences with noncanonical syntactic structures and less plausible semantic roles.

Multilevel Algorithm for Large-Scale Gravity Inversion

Surface gravity inversion attempts to recover the density contrast distribution in the 3D Earth model for geological interpretation. Since airborne gravity is characterized by large data volumes, large-scale 3D inversion exceeds the capacity of desktop computing resources, making it difficult to achieve the appropriate depth/lateral resolution for geological interpretation. In addition, gravity data are finite and noisy, and their inversion is ill posed. Especially in the absence of a priori geological information, regularization must be introduced to overcome the difficulty of the non-uniqueness of the solutions to recover the most geologically plausible ones. Because the use of Haar wavelet operators has an edge-preserving property and can preserve the sensitivity matrix structure at each level of the multilevel method to obtain faster solvers, we present a multilevel algorithm for large-scale gravity inversion solved by the re-weighted regularized conjugate gradient (RRCG) algorithm to reduce the inversion computational resources and improve the depth/lateral resolution of the inversion results. The RRCG-based multilevel inversion was then applied to synthetic cases and airborne gravity data from the Quest-South project in British Columbia, Canada. Results from synthetic models and field data show that the RRCG-based multilevel inversion is suitable for obtaining density contrast distributions with appropriate horizontal and vertical resolution, especially for large-scale gravity inversions compared to Occam’s inversion.

Iron depletion in mineral dust grains from Saturn’s main rings

ABSTRACT During the Grand Finale orbits, Cassini’s Cosmic Dust Analyzer (CDA) recorded in situ mass spectra of ice and mineral nanodust grains ejected from Saturn’s main rings falling into the planet’s atmosphere. We present a compositional analysis of the mineral dust fraction employing a spectral deconvolution method to determine the elemental composition of these grains. The results indicate a relatively homogenous composition of exclusively Mg-rich silicates, with Mg, Si, and Ca close to CI chondritic abundances but a significant depletion in Fe and only traces of organic material at best. The Fe depletion becomes even more pronounced when compared to Fe-rich interplanetary dust particles encountered by CDA in the Saturnian system, which are assumed to contaminate and darken the main rings over time. We discuss potential explanations for the depletion, from which we favour compositional alteration of the infalling dust grains by impact-triggered chemistry in combination with dynamical selection effects and instrumental bias as the most plausible ones. This might cause an accumulation of Fe in the main rings over time, most likely in the form of oxides.

Silver nanoparticles induces apoptosis of cancer stem cells in head and neck cancer

BackgroundSeveral nano formulations of silver nanoparticles with bioconjugates, herbal extracts and anti-cancerous drug coating have been vividly studied to target cancer. Despite of such extensive studies, AgNPs (silver nanoparticles) have not reached the stage of clinical use. Out of all possible reasons for this failure, the unexplored effect on Cancer Stem Cell (CSC) population and mechanism of action of AgNPs, are the most plausible ones and are worked upon in this study. MethodsAgNPs were synthesized by chemical reduction method using sodium citrate and characterized by UV, FTIR, XRD and electron microscopy. CSC population was isolated from Cal33 cell line by MACS technique. MTT assay, trypan blue exclusion assay, Annexin V and PI based apoptosis assay and cell cycle assay were performed. ResultsThe results showed that synthesized AgNPs have cytotoxic activity on all cancer cell lines tested with the IC50 value of a wide range (1.5–49.21 µg/ml for cell lines and 0.0643–0.1211 µg/ml for splenocytes and thymocytes). CSCs Cal33 showed higher resistance to AgNP treatment and arrest in G1/G0 phase upon cell cycle analysis. ConclusionAgNPs as an anti-cancer agent although have great potential but is limited by its off-target effects on normal cells and less effective on cancer stem cells at lower concentrations.

Data augmentation via context similarity: An application to biomedical Named Entity Recognition

In this paper, we present COntext SImilarity-based data augmentation for NER (COSINER), a new method for improving Named Entity Recognition (NER) tasks using data augmentation. Unlike current techniques, which may generate noisy and mislabeled samples through text manipulation, COSINER uses context similarity to replace entity mentions with more plausible ones on the basis of available training data and considering the context in which entities typically appear. Through experiments on popular benchmark datasets, we show that COSINER outperforms existing baselines in various few-shot scenarios where training data is limited. Additionally, our method’s computing times are comparable to the simplest augmentation methods and are better than approaches that rely on pre-trained models in their architecture.

Choice of model and re‐nesting probability function influences behaviour of avian seasonal productivity models and their demographic predictions

Measuring seasonal productivity is difficult in multi‐brooded species without labour‐intensive ringing studies. Individual‐based (IB) models have been used to estimate seasonal productivity with no direct knowledge of number of nesting attempts, but they are often based on simplified re‐nesting probability (φR) step‐functions instead of observed or more biologically plausible ones. We present a new, open‐source IB seasonal productivity model parameterized from studies of Black Redstart Phoenicurus ochruros and Yellowhammer Emberiza citrinella. We examined how the φR function shape (empirical versus simplified) influenced (1) model performance, (2) re‐nesting compensation and (3) population‐level predictions of a simulated management intervention. Population‐level predictions were made only for Yellowhammer as we had more detailed demographic data, such as survival rates, available. Pattern‐oriented modelling revealed that IB models produced realistic within‐population distributions of breeding parameters, and those specified with an observed or empirically derived φR function generally outperformed those specified with simpler step functions. Strength of re‐nesting compensation differed depending on the φR function used. For Yellowhammers, type of φR function in IB models marginally influenced population‐level predictions of a simulated management intervention (potential population growth rate increased between 23% and 29% relative to no management intervention). In contrast, a simple deterministic productivity model, which did not simulate re‐nesting compensation, predicted a 41% increase in potential population growth. At a population level, choice of φR function may have less influence on IB model predictions, but choice of model itself (IB versus deterministic) may have substantial impact. We discuss how more biologically plausible φR functions might either be observed directly, derived from nest data, or estimated from proxy information such as moult or brood patch changes.

Language-based Photo Color Adjustment for Graphic Designs

Adjusting the photo color to associate with some design elements is an essential way for a graphic design to effectively deliver its message and make it aesthetically pleasing. However, existing tools and previous works face a dilemma between the ease of use and level of expressiveness. To this end, we introduce an interactive language-based approach for photo recoloring, which provides an intuitive system that can assist both experts and novices on graphic design. Given a graphic design containing a photo that needs to be recolored, our model can predict the source colors and the target regions, and then recolor the target regions with the source colors based on the given language-based instruction. The multi-granularity of the instruction allows diverse user intentions. The proposed novel task faces several unique challenges, including: 1) color accuracy for recoloring with exactly the same color from the target design element as specified by the user; 2) multi-granularity instructions for parsing instructions correctly to generate a specific result or multiple plausible ones; and 3) locality for recoloring in semantically meaningful local regions to preserve original image semantics. To address these challenges, we propose a model called LangRecol with two main components: the language-based source color prediction module and the semantic-palette-based photo recoloring module. We also introduce an approach for generating a synthetic graphic design dataset with instructions to enable model training. We evaluate our model via extensive experiments and user studies. We also discuss several practical applications, showing the effectiveness and practicality of our approach. Please find the code and data at https://zhenwwang.github.io/langrecol.

Do we rely on good-enough processing in reading under auditory and visual noise?

Noise, as part of real-life communication flow, degrades the quality of linguistic input and affects language processing. According to predictions of the noisy-channel and good-enough processing models, noise should make comprehenders rely more on word-level semantics instead of actual syntactic relations. However, empirical evidence supporting this prediction is still lacking. For the first time, we investigated whether auditory (three-talker babble) and visual (short idioms appearing next to a target sentence on the screen) noise would trigger greater reliance on semantics and make readers of Russian sentences process the sentences superficially. Our findings suggest that, although Russian speakers generally relied on semantics in sentence comprehension, neither auditory nor visual noise increased this reliance. The only effect of noise on semantic processing was found in reading speed under auditory noise measured by first fixation duration: only without noise, the semantically implausible sentences were read slower than semantically plausible ones. These results do not support the predictions of the study based on the noisy-channel and good-enough processing models, which is discussed in light of the methodological differences among the studies of noise and their possible limitations.

Spelling deficits in children with intellectual disabilities: Evidence from a regular orthography.

In individuals with intellectual disabilities (ID), efficient reading and writing skills promote social integration, self-autonomy, and independence. However, research has mainly focused on reading skills, while evidence on spelling skills is scarce and mostly on English-speaking subjects. In the present research project, we compared the spelling skills of children with intellectual disabilities (ID) learning in Italian, a regular orthography, to those of typically developing children matched for school level. In the first study, the performance on a Passage Dictation Test of forty-four children with ID attending regular classrooms from 4th to 8th grades (mean age = 12.16 years; SD = 1.57) were compared with controls matched for sex and grade. In the second study, a Words and Nonwords Dictation Test was administered (with stimuli varying for lexicality, orthographic complexity, regularity of transcription, and the presence of different types of phonetic-phonological difficulties) to twenty-two children with ID attending regular classrooms from 4th to 8th grades (mean age = 12.2 years; SD = 1.37) and 22 controls matched for sex and grade. In both studies, an error analysis was performed to characterize types of misspellings. Separate ANOVAs were performed on z scores. Children with ID generally had a lower performance than controls. In the Passage Dictation Test, they showed a higher number of phonological (and phonetic-phonological) errors than phonologically plausible ones, indicating, as a group, predominant phonological difficulties as compared to lexical-orthographic ones. In the Words and Nonwords Dictation Test, they performed poorly on regular stimuli presenting specific types of phonetic-to-phonological difficulties (geminates, non-continuant consonants) and committed more minimal distance, context-sensitive and simple conversion misspellings. However, deficits in the orthographic-lexical procedure, as indicated by a low performance in words with unpredictable spelling, were present in a high percentage of children. It is concluded that children with ID have significant spelling difficulties not confined to the orthographic process but also in phoneme-to-grapheme mapping that, in a regular orthography like Italian, should be acquired early and easily.

Bayesian Quantification and Reduction of Uncertainties in 3D Geomechanical‐Numerical Models

AbstractThe distance to failure of the upper crustal rock in the prevalent stress field is of importance to better understand fault reactivation by natural and induced processes as well as to plan and manage georeservoirs. In particular, the contemporary stress state is one of the key ingredients for this assessment. To provide a continuous description of the 3D absolute stress state geomechanical‐numerical models are used. However, stress magnitude data for model calibration are sparse and incomplete and thus, the resulting model uncertainties are large. In order to reduce the uncertainties, we incorporate additional constraints on stress magnitudes to check the plausibility of different data‐based stress states. We use formation integrity tests, borehole breakouts, drilling induced fractures, and observations of seismicity and distinct seismological quiescence. This information is weighted according to its confidence and the agreement with the different modeled stress states is assessed. The information is introduced to a Bayesian approach to estimate weights of the modeled stress states and thereby identify their plausibility. A case study in southern Germany shows the ability of the approach to identify from a wide range of stress states a small number of plausible ones and reject implausible stress states. This significantly reduces the number of stress states and thus lowers the model uncertainties.

What We Do for Food

Food is essential for survival, but how humans obtain and manage it is regulated socially. The life of Neolithic and other non-industrial communities depended on environmental variations – temperature patterns and precipitation. For farming communities, even minor changes in those patterns could have led to periods of food scarcity. In order to overcome and prepare for periods of scarcity, non-industrial communities applied different social buffering strategies. In this paper, the social buffering strategies Early/Middle Neolithic Starčevo and Late Neolithic Vinča culture communities applied in overcoming the environmental variability are tested and the most plausible ones are considered.

Writing composition ability and spelling competence in deaf subjects: a psycholinguistic analysis of source of difficulties

We studied the compositional written skills and spelling competence of individuals with a severe hearing impairment, examining qualitative and quantitative characteristics of their texts, the psycholinguistic variables modulating their productions, and writing errors following a fine-grained analysis. Sixteen deaf young adults, educated in bilingual settings, were examined and compared to a group of control hearing subjects matched for gender, age, and education. Writing skills were examined through both written composition and written picture-naming tasks. Concerning compositional skills, deaf participants produced shorter and less informative texts, with fewer adjectives and subordinates, and were qualitatively worse with respect to texts produced by hearing controls. Words produced by deaf participants were those acquired earlier and facilitated by a higher lexical neighbourhood. Errors were mainly semantic, morphological, and syntactic errors, reflecting general linguistic weakness. Spelling errors were few, with phonologically nonplausible misspellings relative to controls, and with phonologically plausible ones being quite rare. In the picture-naming task, deaf people had a greater number of all types of errors with respect to their text, including semantic and morphological errors. Their spelling performance featured mainly phonologically nonplausible misspellings, while phonologically plausible ones were relatively few and comparable to controls. Overall, the writing of deaf adults reveal limitations in grammar and lexical-sematic linguistic competence. This was associated with spelling deficits characterized mainly by the poorer use of phonological sublexical spelling procedures. However, in an ecologic context, their spelling deficits appear not so important as has been claimed in the literature.

Unified and Fast Human Trajectory Prediction Via Conditionally Parameterized Normalizing Flow

Human trajectory prediction is crucial for service robots, autonomous driving and advanced driver assistant systems. Current top-performing methods mainly rely on intractable generative models to learn a distribution of future trajectories, and sample multiple plausible ones as prediction results. In this letter, we propose a novel perspective for trajectory prediction, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Predicting Flow</i> , which models the probability distribution of future trajectories explicitly and tractably by a conditionally parameterized normalizing flow. Compared with previous methods, our prediction framework is unified and faster by enjoying three advantages: i) Both future trajectories and probability maps of future positions can be predicted with our network, bringing more comprehensive prediction results. ii) For future trajectories, exact probabilities for predicted samples can be directly calculated without the aid of extra modules. iii) Our model is adaptive and compact with the conditional parameterization mechanism, leading to significantly stronger expression capability and higher inference speed. Exhaustive experiments on widely-used benchmarks demonstrate that our approach outperforms state-of-the-art performance in both accuracy and inference speed.

An Analysis Method for Metric-Level Switching in Beat Tracking

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not reflect such behaviors, as they typically assume a fixed relationship between the reference beats and estimated beats. In this paper, we propose a new performance analysis method, called annotation coverage ratio (ACR), that accounts for a variety of possible metric-level switching behaviors of beat trackers. The idea is to derive sequences of modified reference beats of all metrical levels for every two consecutive reference beats, and compare every sequence of modified reference beats to the subsequences of estimated beats. We show via experiments on three datasets of different genres the usefulness of ACR when utilized alongside existing metrics, and discuss the new insights to be gained.

Of tinfoil hats and thinking caps: Reasoning is more strongly related to implausible than plausible conspiracy beliefs

People who strongly endorse conspiracy theories typically exhibit biases in domain-general reasoning. We describe an overfitting hypothesis, according to which (a) such theories overfit conspiracy-related data at the expense of wider generalisability, and (b) reasoning biases reflect, at least in part, the need to reduce the resulting dissonance between the conspiracy theory and wider data. This hypothesis implies that reasoning biases should be more closely associated with belief in implausible conspiracy theories (e.g., the moon landing was faked) than with more plausible ones (e.g., the Russian Federation orchestrated the attack on Sergei Skripal). In two pre-registered studies, we found that endorsement of implausible conspiracy theories, but not plausible ones, was associated with reduced information sampling in an information-foraging task and with less reflective reasoning. Thus, the relationship between belief in conspiracy theories and reasoning is not homogeneous, and reasoning is not linked specifically to the “conspiracy” aspect of conspiracy theories. Instead, it may reflect an adaptive response to the tension between implausible theories and other beliefs and data.

Cys-labeling kinetics of membrane protein GlpG: a role for specific SDS binding and micelle changes?

Empirically, α-helical membrane protein folding stability in surfactant micelles can be tuned by varying the mole fraction MFSDS of anionic (sodium dodecyl sulfate (SDS)) relative to nonionic (e.g., dodecyl maltoside (DDM)) surfactant, but we lack a satisfying physical explanation of this phenomenon. Cysteine labeling (CL) has thus far only been used to study the topology of membrane proteins, not their stability or folding behavior. Here, we use CL to investigate membrane protein folding in mixed DDM-SDS micelles. Labeling kinetics of the intramembrane protease GlpG are consistent with simple two-state unfolding-and-exchange rates for seven single-Cys GlpG variants over most of the explored MFSDS range, along with exchange from the native state at low MFSDS (which inconveniently precludes measurement of unfolding kinetics under native conditions). However, for two mutants, labeling rates decline with MFSDS at 0–0.2 MFSDS (i.e., native conditions). Thus, an increase in MFSDS seems to be a protective factor for these two positions, but not for the five others. We propose different scenarios to explain this and find the most plausible ones to involve preferential binding of SDS monomers to the site of CL (based on computational simulations) along with changes in size and shape of the mixed micelle with changing MFSDS (based on SAXS studies). These nonlinear impacts on protein stability highlights a multifaceted role for SDS in membrane protein denaturation, involving both direct interactions of monomeric SDS and changes in micelle size and shape along with the general effects on protein stability of changes in micelle composition.

Biological Plausibility of Arm Postures Influences the Controllability of Robotic Arm Teleoperation.

ObjectiveWe investigated how participants controlling a humanoid robotic arm’s 3D endpoint position by moving their own hand are influenced by the robot’s postures. We hypothesized that control would be facilitated (impeded) by biologically plausible (implausible) postures of the robot.BackgroundKinematic redundancy, whereby different arm postures achieve the same goal, is such that a robotic arm or prosthesis could theoretically be controlled with less signals than constitutive joints. However, congruency between a robot’s motion and our own is known to interfere with movement production. Hence, we expect the human-likeness of a robotic arm’s postures during endpoint teleoperation to influence controllability.MethodTwenty-two able-bodied participants performed a target-reaching task with a robotic arm whose endpoint’s 3D position was controlled by moving their own hand. They completed a two-condition experiment corresponding to the robot displaying either biologically plausible or implausible postures.ResultsUpon initial practice in the experiment’s first part, endpoint trajectories were faster and shorter when the robot displayed human-like postures. However, these effects did not persist in the second part, where performance with implausible postures appeared to have benefited from initial practice with plausible ones.ConclusionHumanoid robotic arm endpoint control is impaired by biologically implausible joint coordinations during initial familiarization but not afterwards, suggesting that the human-likeness of a robot’s postures is more critical for control in this initial period.ApplicationThese findings provide insight for the design of robotic arm teleoperation and prosthesis control schemes, in order to favor better familiarization and control from their users.

Updating the Frame Problem for AI Research

The Frame Problem is the problem of how to design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use. I apply the generative methodology I have developed elsewhere for cognitive science and AI research to show how the Frame Problem can be appropriately updated.

A Runic Inscription at Sarykoby (Southeastern Altai)

This article introduces our reading and interpretation of a recently discovered runic inscription found at the petroglyphic site Sarykoby, in the northern spurs of the Saylyugem Range in the Chuya steppe, southeastern Altai. The inscription belongs to a large composition with unusual representations. It consists of two lines with 2I and 13 characters. After discussing several variants of translation, we have selected the most plausible ones. The translation of the inscription with two variants of the second line is as follows: “I have written on the rock, ah! Oh, please speak! Give me luck (or “Going to battle”) - oh - I have written (this)”. The word su/su in the inscription meaning ‘glory, imperial state, greatness, happiness’, is one of the few Mongolian loans in Old Uyghur and possibly in Old Turkic. The Sarykoby inscription is located in an inconspicuous place, the characters are small, and the carving is shallow. This confirms the common view that many runic inscriptions in the Altai are intimate and were not intended for the public eye. At the same time, the Sarykoby inscription invites the readers to a dialog, and possibly carries a call to prayer or blessing. Its content is religious and philosophic in a sense. Perhaps the author believed that the inscription could confer a blessing upon the readers. This makes it very meaningful and unusual in the corpus of runic inscriptions of the Altai.