Explicit Sequence Research Articles

Implicit and Explicit Sequence Learning in Adults With Developmental Language Disorder.

Developmental language disorder (DLD) is a neurodevelopmental disorder that impacts approximately 7% of the population and is characterized by unexplained deficits in expressive and/or receptive components of language. A common procedural learning task, serial reaction time (SRT), has been used to develop models of the basis of DLD. However, paradigms involve differing levels of implicit and explicit learning during this task, muddying interpretations of the data. Here, we tested adults with DLD on implicit and explicit SRT tasks to better understand implicit and explicit procedural learning in this population. We hypothesized that adults with DLD would demonstrate reduced learning on only the implicit SRT task, as alternate explicit neural mechanisms could lead to equivalent performance on the explicit task. Fifty participants (25 with DLD and 25 with typical language) completed implicit and explicit SRT tasks, measuring their ability to learn visually presented 10-element sequences. Group differences were evaluated on sequence learning, error rates, and explicit recall of the sequence after learning. Sequence learning was the same between the groups on both tasks. However, individuals with DLD showed increased errors and significantly worse recall of the explicitly learned sequence. Results suggest that sequence learning may be intact in this population, while aspects of explicit learning and motoric responses are impaired. Results are interpreted in light of a neurobiological model of DLD. https://doi.org/10.23641/asha.26210651.

Retrosynthesis prediction with an iterative string editing model

Retrosynthesis is a crucial task in drug discovery and organic synthesis, where artificial intelligence (AI) is increasingly employed to expedite the process. However, existing approaches employ token-by-token decoding methods to translate target molecule strings into corresponding precursors, exhibiting unsatisfactory performance and limited diversity. As chemical reactions typically induce local molecular changes, reactants and products often overlap significantly. Inspired by this fact, we propose reframing single-step retrosynthesis prediction as a molecular string editing task, iteratively refining target molecule strings to generate precursor compounds. Our proposed approach involves a fragment-based generative editing model that uses explicit sequence editing operations. Additionally, we design an inference module with reposition sampling and sequence augmentation to enhance both prediction accuracy and diversity. Extensive experiments demonstrate that our model generates high-quality and diverse results, achieving superior performance with a promising top-1 accuracy of 60.8% on the standard benchmark dataset USPTO-50 K.

A topology, material and beam section optimization method for complex structures

This article presents an engineering method for optimizing the topology, materials and beam cross-sectional types of complex structures comprising bars, beams or shells, or their combinations. The optimization problem is established based on an extended ground structure where each location contains multiple components or cross-sectional beams with different materials. This problem is solved through explicit sequence approximate problems involving discrete 0/1 variables to determine the optimal topology, materials and beam section types of components, and continuous size variables. The 0/1 variables are determined with a genetic algorithm, in which size variables are optimized with a dual method after determining 0/1 variables in each generation. The final design is restricted to a condition where at most one component can exist per location, realized through modified operators in the genetic algorithm. Structural analyses are conducted before establishing approximate problems in iteration cycles. Numerical examples, including an engineering application, demonstrate the efficiency of this method.

Dual perspective denoising model for session-based recommendation

Session-based recommendation predicts the next interaction option for an anonymous user based on his/her short-term behavior records. A key challenge faced by session-based recommendation is the processing of noise information. That is, the precise identification of noise items and noise connections. Existing research typically follows two assumptions: (1) the latest interaction item is directly treated as the user’s real-time preference; (2) the temporal order relationship between items is directly treated as the user’s true interaction intention conversion pattern. These strict assumptions ignore two facts: (1) the items that the user accidentally clicked on also exist in the interaction session. (2) the connection edges caused by noise items or user interaction intention drift are also fused into the explicit sequential structure. To address the above two issues, we propose a new Dual Perspective Denoising Model for session-based recommendation called DPDM. Specifically, DPDM mainly consists of two parts: Noise Item Recognition Network and Noise Structure Reconstruction Network. The Noise Item Recognition Network removes noise items from the session through a sparse attention mechanism and virtual target representation. The Noise Structure Reconstruction Network composed of Sequence Module and Co-occurrence Module is used to mine users’ real interaction intention conversion patterns. The Sequence Module captures sequence relationships after removing noise items. The Co-occurrence Module eliminates noise connections by optimizing explicit interaction sequences with L0 activation regularization. Extensive experiments were conducted on three representative real-world datasets, and DPDM achieved positive results, demonstrating the effectiveness of the components in the model.

Training-related changes in neural beta oscillations associated with implicit and explicit motor sequence learning

Many motor actions we perform have a sequential nature while learning a motor sequence involves both implicit and explicit processes. In this work, we developed a task design where participants concurrently learn an implicit and an explicit motor sequence across five training sessions, with EEG recordings at sessions 1 and 5. This intra-subject approach allowed us to study training-induced behavioral and neural changes specific to the explicit and implicit components. Based on previous reports of beta power modulations in sensorimotor networks related to sequence learning, we focused our analysis on beta oscillations at motor-cortical sites. On a behavioral level, substantial performance gains were evident early in learning in the explicit condition, plus slower performance gains across training sessions in both explicit and implicit sequence learning. Consistent with the behavioral trends, we observed a training-related increase in beta power in both sequence learning conditions, while the explicit condition displayed stronger beta power suppression during early learning. The initially stronger beta suppression and subsequent increase in beta power specific to the explicit component, correlated with enhanced behavioral performance, possibly reflecting higher cortical excitability. Our study suggests an involvement of motor-cortical beta oscillations in the explicit component of motor sequence learning.

Zaremba’s Conjecture for Geometric Sequences: An Algorithm

Even though Zaremba’s conjecture remains open, Bourgain and Kontorovich solved the problem for a full density subset. Nevertheless, there are only a handful of explicit sequences known to satisfy the strong version of the conjecture, all of which were obtained using essentially the same algorithm. In this note, we provide a refined algorithm using the folding lemma for continued fractions, which both generalizes and improves on the old one. As a result, we uncover new examples that fulfill the strong version of Zaremba’s conjecture.

Micro-consolidation occurs when learning an implicit motor sequence, but is not influenced by HIIT exercise

We investigated if micro-consolidation, a phenomenon recently discovered during the brief rest periods between practice when learning an explicit motor sequence, generalises to learning an implicit motor sequence task. We demonstrate micro-consolidation occurs in the absence of explicit sequence awareness. We also investigated the effect of a preceding bout of high-intensity exercise, as exercise is known to augment the consolidation of new motor skills. Micro-consolidation was not modified by exercise.

Motor learning and performance in schizophrenia and aging: two different patterns of decline

Psychomotor slowing has consistently been observed in schizophrenia, however research on motor learning in schizophrenia is limited. Additionally, motor learning in schizophrenia has never been compared with the waning of motor learning abilities in the elderly. Therefore, in an extensive study, 30 individuals with schizophrenia, 30 healthy age-matched controls and 30 elderly participants were compared on sensorimotor learning tasks including sequence learning and adaptation (both explicit and implicit), as well as tracking and aiming. This paper presents new findings on an explicit motor sequence learning task, an explicit verbal learning task and a simple aiming task and summarizes all previously published findings of this large investigation. Individuals with schizophrenia and elderly had slower Movement Time (MT)s compared with controls in all tasks, however both groups improved over time. Elderly participants learned slower on tracking and explicit sequence learning while individuals with schizophrenia adapted slower and to a lesser extent to movement perturbations in adaptation tasks and performed less well on cognitive tests including the verbal learning task. Results suggest that motor slowing is present in schizophrenia and the elderly, however both groups show significant but different motor skill learning. Cognitive deficits seem to interfere with motor learning and performance in schizophrenia while task complexity and decreased movement precision interferes with motor learning in the elderly, reflecting different underlying patterns of decline in these conditions. In addition, evidence for motor slowing together with impaired implicit adaptation supports the influence of cerebellum and the cerebello-thalamo-cortical-cerebellar (CTCC) circuits in schizophrenia, important for further understanding the pathophysiology of the disorder.

구체적-반구체적-추상적 단계를 활용한 명시적 교수가 지적장애 초등학생의 덧셈 연산에 미치는 영향: 사례연구

The purpose of this intervention study was to investigate the effects of an explicit conerete-to-semiconcrete-to-abstract(CSA) sequence of instruction on the addition ability of an elementary student with intellectual disability(ID). In this study, one male student with ID attending in a public special school was participated. An A-B design, one of single-subject design, was employed to examine the intervention effectiveness. According to the results, the participant’s addition ability was increased upon the implementation of the intervention, and the participant’s acquired ability was maintained after the intervention was discontinued. These findings provided evidence that the explicit CSA sequence of instruction was effective in teaching the addition ability to one elementary student with ID. The limitations of the study were that the participant was one elementary student with ID and the dependent variable was limited on addition skills. Therefore, the explicit CSA sequence of instruction should be applied to students with various disabilities.

Demodicosis in a Kunekune pig and molecular characterisation of porcine demodectic mites involved: a case report

BackgroundIn January 2021, a female 1-year-old Kunekune was presented at the University Clinic for Swine with severe reduction of the field of vision resulting in prolonged reaction time when targeting barriers, due to moderate to severe thickening of the skin around both orbits also affecting the eyelids. MethodsClinical examination revealed skin hyperplasia, nodular enlargement of the skin pores of the axillar and inguinal region. Ophthalmologists decided to remove parts of the thickened periocular skin, followed by histopathological examination.ResultsOnce large amounts of demodectic mites were detected by histopathology, demodicosis could be diagnosed and treatment of the pig was started using sarolaner. Morphological and molecular analyses were performed. Histopathological and parasitological exams led to the aetiological diagnosis of demodicosis in the affected Kunekune pig. Severe skin lesions were revealed to be the consequence of an infestation with Demodex sp. Morphological analyses confirmed the involvement of D. phylloides. Molecular characterization indicated a Demodex species closely related to mites documented in wild boar - most probably D. phylloides for which no explicit sequences are available in GenBank yet. Treatment with sarolaner (2.6 mg/kg) resulted in a substantial regression of skin lesions, already detectable 1 month after first treatment.ConclusionsDemodicosis is a very rare disease in pigs that is most probably related to an impaired immune response to the mites. Demodectic mange should be included in the list of differential diagnoses in cases of periocular alterations of the skin of pigs.Graphical

Timing Patterns in the Extended Basal Ganglia System.

The human brain is a constructive organ. It generates predictions to modulate its functioning and continuously adapts to a dynamic environment. Increasingly, the temporal dimension of motor and non-motor behaviour is recognised as a key component of this predictive bias. Nevertheless, the intricate interplay of the neural mechanisms that encode, decode and evaluate temporal information to give rise to a sense of time and control over sensorimotor timing remains largely elusive. Among several brain systems, the basal ganglia have been consistently linked to interval- and beat-based timing operations. Considering the tight embedding of the basal ganglia into multiple complex neurofunctional networks, it is clear that they have to interact with other proximate and distal brain systems. While the primary target of basal ganglia output is the thalamus, many regions connect to the striatum of the basal ganglia, their main input relay. This establishes widespread connectivity, forming the basis for first- and second-order interactions with other systems implicated in timing such as the cerebellum and supplementary motor areas. However, next to this structural interconnectivity, additional functions need to be considered to better understand their contribution to temporally predictive adaptation. To this end, we develop the concept of interval-based patterning, conceived as a temporally explicit hierarchical sequencing operation that underlies motor and non-motor behaviour as a common interpretation of basal ganglia function.

Real-world validation of a framework for automated knowledge driven feature engineering inspired by medical domain experts

Knowledge discovery from real-world data in health care can be demanding due to unstructured data and low registration quality in electronic health records (EHRs). This requires close collaboration of domain experts and data scientists. To perform the knowledge discovery process more effectively and efficiently, a framework for automatic Knowledge Driven Feature Engineering (aKDFE) has been developed. Central to aKDFE is an automated feature engineering (FE), i.e., an automated construction of new, highly informative variables, referred to as features, from those directly observed and recorded, e.g., in EHRs. The framework learns and aggregates domain knowledge to generate features that are more informative compared to those recorded in EHRs or manually engineered (manual FE) as done in medical research projects today.Manual KDFE is a systematic manual FE process, which improves prediction performance without loss of explainability of the predictions. But the following research questions remained open: (i) is it possible to automate KDFE, (ii) are aKDFE features more informative than features from a manual FE process, and (iii) does aKDFE produce explainable and transparent results?To summarize the present study, aKDFE is (i) more efficient than manual FE since it automates the manual knowledge discovery and FE processes. It is (ii) more effective due to its higher predictive power compared to manual KDFE. This was evaluated on a real-world medical research project by comparing the classification ability when using manual and aKDFE features in machine learning (ML) models, measured as the area under the receiver operating characteristic curve (AUROC). The project included 26,992 patients regarding “Negative bone structure effects of antiepileptic drug consumption”. The baseline features (manual FE) used in studied project were compared with features generated by aKDFE; aKDFE-generated features resulted in higher AUROC than baseline features, with a p-value <0.05. Finally, aKDFE (iii) applies and describes data pivoting and feature generation as explicit and transparent operation sequences on EHR features. Inefficiency issues remain, mainly regarding non-automatic FE and baseline generation.Threat of validity to the aKDFE framework exists in the selection of the pivoting method, the generalization of used FE operations and rules, and usage of evaluation metrics.

Cost evaluation of finite dimensional and approximate null-controllability for the one dimensional half-heat equation

In this article we evaluate the cost of the finite dimensional and the approximate controllability of a one dimensional anomalous diffusion equation which is not null-controllable. The finite dimensional controllability property consists in driving to zero a finite number of frequencies of the corresponding solutions. To evaluate its cost, we construct an explicit biorthogonal sequence to a finite part of a family of real exponential functions. Since the exponents of these functions satisfy the so-called Müntz density condition, there is no biorthogonal sequence to the entire family which shows that the equation is not even spectrally controllable. For the approximate controllability problem, we combine the idea of finite dimensional controllability studied before and the intrinsic dissipative nature of the system. These arguments allow to show that the controlled solution can be made arbitrarily small after sufficiently large time and provide estimates for the corresponding approximate controls.

The compatibility of Christ’s ascension in the Epistle to the Hebrews and the theology of pre-advent judgment (Part 1)

The Seventh-day Adventist doctrine of pre-advent investigative judgment has facedskepticism since the late 19th century, largely due to perceived conflicts with the Epistleto the Hebrews. This study endeavors to establish the doctrine’s biblical foundation,predominantly found in the book of Daniel, while scrutinizing its compatibility withthe New Testament. By analyzing texts in Daniel 7, 8, 9, and the explicit sequence inRevelation 14, this research provides compelling evidence for the concept of pre-adventjudgment in the Bible. However, questions arise concerning the location of Jesus’post-ascension ministry in heaven, particularly in the context of “seated at the right handof God”. Despite potential conflicts suggested by this phrase, aspects such as intercessoryministry and High priest Christology appear to harmonize with the doctrine. Whilethis article offers substantial support, a more detailed examination of their compatibilityawaits part two of this study.

Feature-Level Deeper Self-Attention Network With Contrastive Learning for Sequential Recommendation

Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. Meanwhile, the item-level sequential recommendation also suffers from limited supervised signal issues. In this paper, we propose a novel model Feature-level Deeper Self-Attention Network with Contrastive Learning (FDSA-CL) for sequential recommendation. Specifically, FDSA-CL first integrates various heterogeneous features of items into feature-level sequences with different weights through a vanilla attention mechanism. After that, FDSA-CL applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Moreover, we propose contrastive learning and item feature recommendation tasks to capture the embedding commonality and further utilize the beneficial interaction among the two levels, so as to alleviate the sparsity of the supervised signal and extract the most critical information. Finally, we jointly optimize the above tasks. We evaluate the proposed model using two real-world datasets and experimental results show that our model significantly outperforms the state-of-the-art approaches.

Do they know or just do it? Investigating implicit and explicit sequence learning by capuchin monkeys, human adults and children

In humans, it is now established that sequential regularities can be learned implicitly (i.e. without acquiring conscious knowledge) or explicitly (with acquisition of conscious knowledge). Is this dual-processing capability also the case for non-human primates? In this study, we designed a non-verbal task to probe implicit and explicit sequence learning in capuchin monkeys (Sapajus sp., n = 12), human adults (n = 12), and children from 5 to 10 years old (n = 64). After learning spatial sequences on a touchscreen, participants’ conscious access to the sequences was probed with a forced choice sequence completion test. All performed above chance level in this test, without being instructed or trained to do so. However, only human adults who reported the presence of regularities performed at ceiling level. We suggest future directions that could build on our findings to disentangle implicit and explicit learning in monkeys and children.

Novel molecular requirements for CRISPR RNA-guided transposition.

CRISPR-associated transposases(CASTs) direct DNA integration downstream of target sites using the RNA-guided DNA binding activity of nuclease-deficient CRISPR-Cas systems. Transposition relies on several key protein-protein and protein-DNA interactions, but little is known about the explicit sequence requirements governing efficient transposon DNA integration activity. Here, we exploit pooled library screening and high-throughput sequencing to reveal novel sequence determinants during transposition by the Type I-F Vibrio cholerae CAST system(VchCAST). On the donor DNA, large transposon end libraries revealed binding site nucleotide preferences for the TnsB transposase, as well as an additional conserved region that encoded a consensus binding site for integration host factor (IHF). Remarkably, we found that VchCAST requires IHF for efficient transposition, thus revealing a novel cellular factor involved in CRISPR-associated transpososome assembly. On the target DNA, we uncovered preferred sequence motifs at the integration site that explained previously observed heterogeneity with single-base pair resolution. Finally, we exploited our library data to design modified transposon variants that enable in-frame protein tagging. Collectively, our results provide new clues about the assembly and architecture of the paired-end complex formed between TnsB and the transposon DNA, and inform the design of custom payload sequences for genome engineering applications with CAST systems.

A Single Session of Mindfulness Meditation Expedites Immediate Motor Memory Consolidation to Improve Wakeful Offline Learning

Posttraining meditation has been shown to promote wakeful memory stabilization of explicit motor sequence information in learners who are experienced meditators. We investigated the effect of single-session mindfulness meditation on wakeful and sleep-dependent forms of implicit motor memory consolidation in meditation naïve adults. Immediately after training with a target implicit motor sequence, participants (N = 20, eight females, 23.9 ± 3.3 years) completed either a 10-min mindfulness meditation (N = 10) or a control listening task before exposure to task interference induced by training with a novel implicit sequence. Target sequence performance was tested following 5-hr wakeful and 15-hr postsleep periods. Bayesian inference was applied to group comparisons of mean reaction time (RT) changes across training, interference, wakeful, and postsleep timepoints. Relative to control conditions, posttraining meditation reduced RT slowing between target sequence training and interference sequence introduction (BF10 [Bayes factors] = 6.61) and supported RT performance gains over the wakeful period (BF10 = 8.34). No group differences in postsleep RT performance were evident (BF10 = 0.38). These findings illustrate that posttraining mindfulness meditation expedites wakeful, but not sleep-dependent, offline learning with implicit motor sequences. Previous meditation experience is not required to obtain wakeful consolidation gains from posttraining mindfulness meditation.

Involvement of the prefrontal cortex in motor sequence learning: A functional near-infrared spectroscopy (fNIRS) study

Our previous functional near-infrared spectroscopy (fNIRS) study on motor sequence learning (Polskaia et al., 2020) did not detect the same decrease in activity in the left dorsolateral prefrontal cortex (DLPFC) associated with movement automaticity, as reported by Wu et al. (2004). This was partly attributed to insufficient practice time to reach neural efficiency. Therefore, we sought to expand on our previous work to better understand the contribution of the prefrontal cortex (PFC) to motor sequence learning by examining learning across a longer period of time. Participants were randomly assigned to one of two groups: control or trained. fNIRS was acquired at three time points: pre-test, post-test, and retention. Participants performed four sequences (S1, S2, S3, and S4) of right-hand finger tapping. The trained group also underwent four days of practice of S1 and S2. No group differences in the left DLPFC and ventrolateral (VLPFC) were found between sessions for S1 and S2. Our findings revealed increased contribution from the right VLPFC in post-test for the trained group, which may reflect the active retrieval of explicit information from long-term memory. Our results suggest that despite additional practice time, explicit motor sequence learning requires the continued involvement of the PFC.

Exploring general Apéry limits via the Zudilin–Straub t-transform

Inspired by a recent beautiful construction of Armin Straub and Wadim Zudilin, that ‘tweaked’ the sum of the sth powers of the n-th row of Pascal's triangle, getting instead of sequences of numbers, sequences of rational functions, we do the same for general binomial coefficients sums, getting a practically unlimited supply of Apéry limits. While getting what we call ‘major Apéry miracles’, proving irrationality of the associated constants (i.e. the so-called Apéry limits) is very rare, we do get, every time, at least a ‘minor Apéry miracle’ where an explicit constant, defined as an (extremely slowly converging) limit of some explicit sequence, is expressed as an Apéry limit of some recurrence, with some initial conditions, thus enabling a very fast computation of that constant, with exponentially decaying error.