Miracle-dz/UniFAR-FTUs huggingface dataset:
Is Parents’ ADHD Symptomatology Associated With the Clinical Feasibility or Effectiveness of a Psychoeducational Program Targeting Their Children’s ADHD?

Objective: To examine if the clinical feasibility and effectiveness of a psychoeducational program targeting children’s ADHD differ between parents with varying ADHD symptom severities. Method: An open trial analyzed data from 549 parents of children with ADHD aged 3 to 17 years, who participated in psychoeducation at an outpatient habilitation/disability clinic. In all analyses, parents were stratified into three symptom severity groups (low/middle/high) based on scores on an ADHD screening scale. Results: Parental ADHD symptom severity was not associated with results on any outcome, although the odds of having incomplete data were higher in parents reporting higher symptom severity. Across the entire sample, we observed high program completion rates, positive acceptability ratings and large increases in parental knowledge. Conclusions: Psychoeducation may be beneficial regardless of the participating parent’s ADHD symptomatology. Further research is needed to examine whether these results are generalizable to parents diagnosed with ADHD, an underrepresented group in our study.
The Port Delivery System with ranibizumab: a new paradigm for long-acting retinal drug delivery

Abstract The Port Delivery System with ranibizumab (PDS) is an innovative intraocular drug delivery system designed for the continuous delivery of ranibizumab into the vitreous for 6 months and beyond. The PDS includes an ocular implant, a customized formulation of ranibizumab, and four dedicated ancillary devices for initial fill, surgical implantation, refill-exchange, and explantation, if clinically indicated. Ranibizumab is an ideal candidate for the PDS on account of its unique physicochemical stability and high solubility. Controlled release is achieved via passive diffusion through the porous release control element, which is tuned to specific drug characteristics to accomplish a therapeutic level of ranibizumab in the vitreous. To characterize drug release from the implant, release rate was measured in vitro with starting concentrations of ranibizumab 10, 40, and 100 mg/mL, with release of ranibizumab 40 and 100 mg/mL found to remain quantifiable after 6 months. Using a starting concentration of 100 mg/mL, active release rate at approximately 6 months was consistent after the initial fill and first, second, and third refills, demonstrating reproducibility between implants and between multiple refill-exchanges of the same implant. A refill-exchange performed with a single 100-µL stroke using the refill needle was shown to replace over 95% of the implant contents with fresh drug. In vitro data support the use of the PDS with fixed refill-exchange intervals of at least 6 months in clinical trials.
Targeting BCAT1 combined with α-ketoglutarate triggers metabolic synthetic lethality in glioblastoma.

Branched-chain amino acid transaminase 1 (BCAT1) is upregulated selectively in human isocitrate dehydrogenase (IDH) wildtype (WT) but not mutant glioblastoma multiforme (GBM) and promotes IDHWT GBM growth. Through a metabolic synthetic lethal screen, we report here that α-ketoglutarate (AKG) kills IDHWT GBM cells when BCAT1 protein is lost, which is reversed by re-expression of BCAT1 or supplementation with branched-chain α-ketoacids (BCKAs), downstream metabolic products of BCAT1. In patient-derived IDHWT GBM tumors in vitro and in vivo, co-treatment of BCAT1 inhibitor gabapentin and AKG resulted in synthetic lethality. However, AKG failed to evoke a synthetic lethal effect with loss of BCAT2, BCKDHA, or GPT2 in IDHWT GBM cells. Mechanistically, loss of BCAT1 increased the NAD+/NADH ratio but impaired oxidative phosphorylation, mTORC1 activity, and nucleotide biosynthesis. These metabolic alterations were synergistically augmented by AKG treatment, thereby causing mitochondrial dysfunction and depletion of cellular building blocks, including ATP, nucleotides, and proteins. Partial restoration of ATP, nucleotides, proteins, and mTORC1 activity by BCKA supplementation prevented IDHWT GBM cell death conferred by the combination of BCAT1 loss and AKG. These findings define a targetable metabolic vulnerability in the most common subset of GBM that is currently incurable.
New beamforming strategy for improved ultrasound imaging : application to biological tissues nonlinear imaging

Nowadays, ultrasound imaging is a common diagnostic tool thanks to its non-invasive behavior and relatively cheap equipment. Classic medical echographic imaging is based on the linear response of the biological tissue. However harmonic imaging, based on the harmonic frequencies generated by the nonlinear properties of the tissue, is more and more used for clinical application. The quantification of nonlinearity is based on the evaluation of the nonlinearity parameter which strongly influences the harmonics generation. The nonlinearity parameter estimation using an echographic approach would bring new modalities for imaging and diagnosis. However the echographic method for nonlinearity estimation is limited by two factors: the presence of speckles in the image and the focalization used during transmission, which concentrates the energy at one particular depth. The objectives of this thesis work are developing novel approaches to reduce the speckle noise using original smoothing techniques and improving the nonlinearity parameter estimation in echo mode using new transmission-reception strategies. Firstly, new speckle noise reduction approaches were investigated. The Thomson’s multitaper approach was proposed, consisting in using several different orthogonal apodizations during beamforming. This method was combined to a coherent plane-wave compounding transmission-reception strategy improving the spatial resolution and the contrast while improving the frame rate. In a second time, the nonlinearity parameter was estimated using a comparative method. The second-harmonic pressure field of a reference area was compared to the pressure field of an area where the nonlinearity parameter is unknown. However in echo-mode, the pressure field of the medium is unknown. It is assumed in this thesis work that the local pressure can be derived from envelope image local amplitude if the speckle noise is smoothed. The nonlinearity parameter estimation has been improved using plane-wave transmission and orthogonal apodizations compared to the use of a single focalization transmission
Feature Extraction Techniques for Palmprint Identification: A Survey

Palmprint recognition has been investigated over the past decade. Palmprint recognition has five stages palmprint acquisition, pre-processing, feature extraction, enrolment (database) and matching. Due to rich information in palmprint it became a powerful means in person identification. The major approach for palmprint recognition is to extract feature vectors corresponding to individual palm image and to perform matching based on some distance metrics. Palmprint recognition is a challenging problem mainly due to low quality of pattern, large nonlinear distortion between different impression of same palm and large image size, which makes feature extraction and matching computationally demanding. In this paper we talk about the various approaches of palmprint recognition using matching pattern method.
Customer relationship management (CRM) system implementations: an assessment of organisational culture

Many commentators have identified organisational culture as an important factor that enables or disables the achievement of desirable CRM outcomes, along with other key people-related issues, such as senior management commitment and people’s willingness to support the initiative. Our review of the CRM literature shows that an organisational environment that puts more importance on customer-focused behaviours, information sharing, cross-functional teams, performancebased rewards, supportive relationships, adaptive and responsive attitudes to change, and a higher degree of risk-taking and innovation, is more likely to be associated with successful CRM system implementations. We have used the Competing Values Framework to study the culture of organisations implementing CRM in Australia. The ultimate objective of this study is to provide insights into the relationships between organisational culture and CRM implementation outcomes. We have collected organisational culture data from 101 organisations that are implementing CRM. The results show that only half of the organisations implementing CRM in Australia have organisational culture characteristics that match those that the literature suggests are associated with successful CRM outcomes.
Analitička metoda za proračun optimalnog odnosa unutrašnjeg i vanjskog prečnika statora BLDC motora

CRC Press, Taylor & Francis Group, 2010. ABSTRACT This paper presents a simple analytical method for the calculation of optimal split ratio of the BLDC (Brushless Direct Current) motor with permanent magnets on the rotor, for achieving maximum volume torque density. The method is based on the assumption that the product of surface and linear current density varies in a relatively narrow range for a certain type of electrical machines, and that its value primarily de-pends on the applied type of cooling. It is shown that by adopt-ing a constant value of this product in the optimization process, the maximum allowed temperature of the conductor is limited, while, at the same time, designs that correspond to engineering experience in the construction of rotating electric machines are obtained. The results of the calculations are verified by com-puter
Opponent Modeling in Negotiation Dialogues by Related Data Adaptation

Opponent modeling is the task of inferring another party's mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent's preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.
Group action Markov chain Monte Carlo for accelerated sampling of energy landscapes with discrete symmetries and energy barriers

Monte Carlo sampling of the canonical distribution presents a formidable challenge when the potential energy landscape is characterized by a large number of local minima separated by high barriers. The principal observation of this work is that the multiple local minima and energy barriers in a landscape can often occur as a result of discrete symmetries in the potential energy function. A new Monte Carlo method is proposed, group action Markov chain Monte Carlo (GA-MCMC), which augments more conventional trial moves (e.g. random jumps, hybrid Monte Carlo, etc.) with the application of a group action from a well-chosen generating set of the discrete symmetry group; the result is a framework for symmetry-adapted MCMC. It is shown that conventional trial moves are generally optimal for “local mixing” rates, i.e. sampling a single energy well; whereas the group action portion of the GA-MCMC trial move allows the Markov chain to propagate between energy wells and can vastly improve the rate of “global mixing”. The proposed method is compared with standard jumps and umbrella sampling (a popular alternative for energy landscapes with barriers) for potential energies with translational, reﬂection, and rotational symmetries. GA-MCMC is shown to consistently outperform the considered alternatives, even when the symmetry of the potential energy function is broken. The work culminates by extending GA-MCMC to a clustering-type algorithm for interacting dielectric polymer chains. Not only does GA-MCMC again outperform the considered alternatives, but it is the only method which consistently converges for all of the cases considered. Some new and interesting phenomena regarding the electro-elasticity of dielectric polymer chains, unveiled via GA-MCMC, is brieﬂy discussed.
Dashboard Design Patterns

This paper introduces design patterns for dashboards to inform dashboard design processes. Despite a growing number of public examples, case studies, and general guidelines there is surprisingly little design guidance for dashboards. Such guidance is necessary to inspire designs and discuss tradeoffs in, e.g., screenspace, interaction, or information shown. Based on a systematic review of 144 dashboards, we report on eight groups of design patterns that provide common solutions in dashboard design. We discuss combinations of these patterns in “dashboard genres” such as narrative, analytical, or embedded dashboard. We ran a 2-week dashboard design workshop with 23 participants of varying expertise working on their own data and dashboards. We discuss the application of patterns for the dashboard design processes, as well as general design tradeoffs and common challenges. Our work complements previous surveys and aims to support dashboard designers and researchers in co-creation, structured design decisions, as well as future user evaluations about dashboard design guidelines. Detailed pattern descriptions and workshop material can be found online: https://dashboarddesignpatterns.github. io
Unified Abstract Syntax Tree Representation Learning for Cross-Language Program Classification

Program classification can be regarded as a high-level abstraction of code, laying a foundation for various tasks related to source code comprehension, and has a very wide range of applications in the field of software engineering, such as code clone detection, code smell classification, defects classification, etc. The cross-language program classification can realize code transfer in different programming languages, and can also promote cross-language code reuse, thereby helping developers to write code quickly and reduce the development time of code transfer. Most of the existing studies focus on the semantic learning of the code, whilst few studies are devoted to cross-language tasks. The main challenge of cross-language program classification is how to extract semantic features of different programming languages. In order to cope with this difficulty, we propose a Unified Abstract Syntax Tree (namely UAST in this paper) neural network. In detail, the core idea of UAST consists of two unified mechanisms. First, UAST learns an AST representation by unifying the AST traversal sequence and graph-like AST structure for capturing semantic code features. Second, we construct a mechanism called unified vocabulary, which can reduce the feature gap between different programming languages, so it can achieve the role of cross-language program classification. Besides, we collect a dataset containing 20,000 files of five programming languages, which can be used as a benchmark dataset for the cross-language program classification task. We have done experiments on two datasets, and the results show that our proposed approach out-performs the state-of-the-art baselines in terms of four evaluation metrics (Precision, Recall, F1-score, and Accuracy).
TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources

Recent advances in deep reinforcement learning (DRL) have largely promoted the performance of adaptive traffic signal control (ATSC). Nevertheless, regarding the implementation, most works are cumbersome in terms of storage and computation. This hinders their deployment on scenarios where resources are limited. In this work, we propose TinyLight, the first DRL-based ATSC model that is designed for devices with extremely limited resources. TinyLight first constructs a super-graph to associate a rich set of candidate features with a group of light-weighted network blocks. Then, to diminish the model's resource consumption, we ablate edges in the super-graph automatically with a novel entropy-minimized objective function. This enables TinyLight to work on a standalone microcontroller with merely 2KB RAM and 32KB ROM. We evaluate TinyLight on multiple road networks with real-world traffic demands. Experiments show that even with extremely limited resources, TinyLight still achieves competitive performance. The source code and appendix of this work can be found at https://bit.ly/38hH8t8.
On the Role of Postconditions in Dynamic First-Order Epistemic Logic

Dynamic Epistemic Logic (DEL) is a logic that models information change in a multi-agent setting through the use of action models with pre- and post-conditions. In a recent work, DEL has been extended to first-order epistemic logic (DFOEL), with a proof that the resulting Epistemic Planning Problem is decidable, as long as action models pre- and post-conditions are non-modal and the first-order domain is finite. Our contribution highlights the role post-conditions have in DFOEL. We show that the Epistemic Planning Problem with possibly infinite first-order domains is undecidable if the non-modal event post-conditions may contain first-order quantifiers, while, on the contrary, the problem becomes decidable when event post-conditions are quantifier-free. The latter result is non-trivial and makes an extensive use of automatic structures.
Deep Ensemble as a Gaussian Process Approximate Posterior

Deep Ensemble (DE) is an effective alternative to Bayesian neural networks for uncertainty quantification in deep learning. The uncertainty of DE is usually conveyed by the functional inconsistency among the ensemble members, say, the disagreement among their predictions. Yet, the functional inconsistency stems from unmanageable randomness and may easily collapse in specific cases. To render the uncertainty of DE reliable, we propose a refinement of DE where the functional inconsistency is explicitly characterized, and further tuned w.r. t. the training data and certain priori beliefs. Specifically, we describe the functional inconsistency with the empirical covariance of the functions dictated by ensemble members, which, along with the mean, define a Gaussian process (GP). Then, with specific priori uncertainty imposed, we maximize functional evidence lower bound to make the GP specified by DE approximate the Bayesian posterior. In this way, we relate DE to Bayesian inference to enjoy reliable Bayesian uncertainty. Moreover, we provide strategies to make the training efficient. Our approach consumes only marginally added training cost than the standard DE, but achieves better uncertainty quantification than DE and its variants across diverse scenarios.
Sequence Learning and Consolidation on Loihi using On-chip Plasticity

In this work we develop a model of predictive learning on neuromorphic hardware. Our model uses the on-chip plasticity capabilities of the Loihi chip to remember observed sequences of events and use this memory to generate predictions of future events in real time. Given the locality constraints of on-chip plasticity rules, generating predictions without interfering with the ongoing learning process is nontrivial. We address this challenge with a memory consolidation approach inspired by hippocampal replay. Sequence memory is stored in an initial memory module using spike-timing dependent plasticity. Later, during an offline period, memories are consolidated into a distinct prediction module. This second module is then able to represent predicted future events without interfering with the activity, and plasticity, in the first module, enabling online comparison between predictions and ground-truth observations. Our model serves as a proof-of-concept that online predictive learning models can be deployed on neuromorphic hardware with on-chip plasticity.
Artificial Intelligence and Medicine: A literature review

In practically every industry today, artificial intelligence is one of the most effective ways for machines to assist humans. Since its inception, a large number of researchers throughout the globe have been pioneering the application of artificial intelligence in medicine. Although artificial intelligence may seem to be a 21st-century concept, Alan Turing pioneered the first foundation concept in the 1940s. Artificial intelligence in medicine has a huge variety of applications that researchers are continually exploring. The tremendous increase in computer and human resources has hastened progress in the 21st century, and it will continue to do so for many years to come. This review of the literature will highlight the emerging field of artificial intelligence in medicine and its current level of development.
Counterexamples to Gerbner's conjecture on stability of maximal F‐free graphs

Let r ≥ 2 $r\ge 2$ and let F $F$ be an ( r + 1 ) $(r+1)$ ‐chromatic graph with a color‐critical edge, that is, there exists e ∈ E ( F ) $e\in E(F)$ such that F − e $F-e$ has chromatic number r $r$ . Gerbner recently conjectured that every n $n$ ‐vertex maximal F $F$ ‐free graph with at least ( 1 − 1 r ) n 2 2 − o ( n r + 1 r ) $(1-\frac{1}{r})\frac{{n}^{2}}{2}-o({n}^{\frac{r+1}{r}})$ edges contains an induced complete r $r$ ‐partite graph on n − o ( n ) $n-o(n)$ vertices. Let F s , k ${F}_{s,k}$ be a graph obtained from s $s$ copies of C 2 k + 1 ${C}_{2k+1}$ by sharing a common edge. In this paper, we show that for all k ≥ 2 $k\ge 2$ if G $G$ is an n $n$ ‐vertex maximal F s , k ${F}_{s,k}$ ‐free graph with at least n 2 ∕ 4 − o ( n s + 2 s + 1 ) ${n}^{2}\unicode{x02215}4-o({n}^{\frac{s+2}{s+1}})$ edges, then G $G$ contains an induced complete bipartite graph on n − o ( n ) $n-o(n)$ vertices. We also construct graphs to show that the magnitude in o ( n s + 2 s + 1 ) $o({n}^{\frac{s+2}{s+1}})$ is best possible, which disproves Gerbner's conjecture for r = 2 $r=2$ .
AI-Driven Contextual Advertising: A Technology Report and Implication Analysis

Programmatic advertising consists in automated auctioning of digital ad space. Every time a user requests a web page, placeholders on the page are populated with ads from the highest-bidding advertisers. The bids are typically based on information about the user, and to an increasing extent, on information about the surrounding media context. The growing interest in contextual advertising is in part a counterreaction to the current dependency on personal data, which is problematic from legal and ethical standpoints. The transition is further accelerated by developments in Artificial Intelligence (AI), which allow for a deeper semantic understanding of context and, by extension, more effective ad placement. In this article, we begin by identifying context factors that have been shown in previous research to positively influence how ads are received. We then continue to discuss applications of AI in contextual advertising, where it adds value by, e.g., extracting high-level information about media context and optimising bidding strategies. However, left unchecked, these new practices can lead to unfair ad delivery and manipulative use of context. We summarize these and other concerns for consumers, publishers and advertisers in an implication analysis.