Adaptive and personalized systems have become pervasive technologies, gradually playing an increasingly important role in our daily lives. Indeed, we are now accustomed to interacting with algorithms that leverage the power of Language Models (LLMs) to assist us in various scenarios, from services suggesting music or movies to personal assistants proactively supporting us in complex decision-making tasks. As these technologies continue to shape our everyday experiences, ensuring that the internal mechanisms guiding these algorithms are transparent and comprehensible becomes imperative. The EU General Data Protection Regulation (GDPR) recognizes the users’ right to explanation when confronted with intelligent systems, highlighting the significance of this aspect. Regrettably, current research often prioritizes the maximization of personalization strategy effectiveness, such as recommendation accuracy, at the expense of model explainability. To address this concern, the workshop aims to provide a platform for in-depth discussions on challenges, problems, and innovative research approaches in the field. The workshop specifically focuses on investigating the role of transparency and explainability in recent methodologies for constructing user models and developing personalized and adaptive systems.

RNA interference (RNAi) is a crucial mechanism that can contribute to immunity against infectious microbes through the action of DICER-LIKE (DCL) and ARGONAUTE (AGO) proteins. In the case of the fungal pathogen Botrytis cinerea and the oomycete Hyaloperonospora arabidopsidis, plant DCL and AGO proteins have proven roles as negative regulators of immunity, suggesting functional specialization of these proteins. To address this aspect in a broader taxonomic context, we characterized the colonization pattern of an informative set of DCL and AGO loss-of-function mutants in Arabidopsis thaliana upon infection with a panel of pathogenic microbes with different lifestyles, and a fungal mutualist. Our results revealed that AGO1 and AGO4 function as positive regulators of immunity to a bacterial and a fungal pathogen, respectively. Additionally, AGO2 and AGO10 positively modulated the colonization by a fungal mutualist. Therefore, analysing the role of RNAi across a broader range of plant-microbe interactions has identified previously unknown functions for AGO proteins. For some pathogen interactions, however, all tested mutants exhibited wild type-like infection phenotypes, suggesting that the roles of AGO and DCL proteins in these interactions may be more complex to elucidate.

[This corrects the article DOI: 10.1021/acsomega.3c08932.].

Fokus rada je na motivaciji kao jednom od glavnih čimbenika u poučavanju i učenju stranih jezika, kao i na načinu na koji se učenici motiviraju za njihovo učenje. Kao potpora empirijskom istraživanju testirani su aspekti motivacije (intrinzične i ekstrinzične) kako bi se ispitao njihov doprinos tijekom online nastave izazvane Covidom-19 i nakon povratka u škole. U prilog dobivenim rezultatima dane su preporuke koje bi mogle poslužiti budućoj organizaciji online nastave, ali i poslužiti pedagoško-psihološkim i didaktičko-metodičkim kompetencijama nastavnika. Istraživanje je potvrdilo povećanje i intrinzične i ekstrinzične motivacije nakon povratka u školu, za razliku od razdoblja provedenoga u online nastavi s izuzetkom aspekta učenja njemačkoga jezika. Prisutna intrinzična motivacija za učenje njemačkoga jezika u predikciji učenja njemačkoga jezika daje bolje rezultate iz razdoblja 2019. u odnosu na razdoblje 2021. Zanimljivo je da su učenici 2019. više uživali u samostalnom učenju tijekom online nastave nego danas u učionicama. Utjecaj analiziranih aspekata ekstrinzične motivacije proveden je uz pomoć korištenih metoda i nastavnih oblika rada. Utvrđene vrijednosti pokazuju veću snagu u 2021. godini u odnosu na 2019. godinu. Implikacije nedostatka digitalnih kompetencija ozbiljno zahtijevaju restrukturiranje obrazovnoga rada učitelja.

This paper presents a novel event-triggered control (ETC) design framework based on measured $\mathcal{L}_{p}$ norms. We consider a class of systems with finite $\mathcal{L}_{p}$ gain from the network-induced error to a chosen output. The $\mathcal{L}_{p}$ norms of the network-induced error and the chosen output since the last sampling time are used to formulate a class of triggering rules. Based on a small-gain condition, we derive an explicit expression for the $\mathcal{L}_{p}$ gain of the resulting closed-loop systems and present a time-regularization, which can be used to guarantee a lower bound on the inter-sampling times. The proposed framework is based on a different stability- and triggering concept compared to ETC approaches from the literature, and thus may yield new types of dynamical properties for the closed-loop system. However, for specific output choices it can lead to similar triggering rules as “standard” static and dynamic ETC approaches based on input-to-state stability and yields therefore a novel interpretation for some of the existing triggering rules. We illustrate the proposed framework with a numerical example from the literature.

Mammography is the leading methodology used to diagnose breast cancer. Effective, cheap and reliable, the mammography can be used to screen large populations, if the imagery produced can be analysed efficiently. State-of-the-art generative artificial intelligence approaches can be used to create tools able to aid in this task. Here we present a study focused on the emerging research topic of the application of generative diffusion models to the task of anomaly detection and we apply if for detecting anomalies on mammograms. Diffusion models exhibit promising results in making pixel-level predictions with image level annotations, but no such application has been published so far regarding mammography. We have, therefore, developed a novel approach utilizing U-net backbone that is able to generate mammograms with Fréchet Inception Distance (FID) of 14.62. We showed its ability to perform anomaly detection with Intersection over Union (IoU) of 0.195 which demonstrates the viability of our approach for early-stage research.

Calocucullia celsiae (Herrich-Schäffer, [1850]) is an easily recognizable noctuid species, differing from all other similar species in its subfamily. Within this survey, it was recorded at two localities in Bosnia and Herzegovina. Two specimens were collected near Hutovo village in the southern Herzegovina region in April 2023, and a single specimen was collected near Zoranovići village in the central part of the country in May 2023. These are the first records of this species for Bosnia and Herzegovina and the westernmost known data on the presence of this species on the Balkan Peninsula.

Electromagnetic levitation represents a contemporary line of technology with a wide spectrum of applications in several areas of engineering. Regarding the increased demands for this technology in-depth research into its dynamics and the influence of different material characteristics used in these systems is needed. This paper presents simulation results of electrodynamic levitation system regarding different types of materials used for levitating disc. The main intention is to provide a comparison of electrodynamic levitation systems with different materials used for levitating disc in order to improve the system itself. Therefore, the impact of different materials used for the levitating disc of the electrodynamic levitation system would be investigated through some parameters of interest such as an analysis of the electromagnetic force, disk displacement, and the time required to achieve a stable disk position.

This research aimed to analyze the impact of two different non-Saccharomyces yeast species on the aromatic profile of red wines made from the cv. Babić (Vitis vinifera L.) red grape variety. The grapes were obtained from two positions in the Middle and South of Dalmatia. This study compared a control treatment with the Saccharomyces cerevisiae (Sc) strain as a type of sequential inoculation treatment with Lachancea thermotolerans (Lt x Sc) and Torulaspora delbrueckii (Td x Sc). The focus was on the basic wine parameters and volatile aromatic compound concentrations determined using the SPME-Arrow-GC/MS method. The results revealed significant differences in cis-linalool oxide, geraniol, neric acid, and nerol, which contribute to the sensory profile with floral and rose-like aromas; some ethyl esters, such as ethyl furoate, ethyl hexanoate, ethyl lactate, ethyl 2-hydroxy-3-methylbutanoate, ethyl 3-hydroxy butanoate, diethyl glutarate, and diethyl succinate, contribute to the aromatic profile with fruity, buttery, overripe, or aging aromas. A sensory evaluation of wines confirmed that Td x Sc treatments exhibited particularly positive aromatic properties together with a more intense fullness, harmony, aftertaste, and overall impression.

Data curation is an essential component of large-scale pretraining. In this work, we demonstrate that jointly selecting batches of data is more effective for learning than selecting examples independently. Multimodal contrastive objectives expose the dependencies between data and thus naturally yield criteria for measuring the joint learnability of a batch. We derive a simple and tractable algorithm for selecting such batches, which significantly accelerate training beyond individually-prioritized data points. As performance improves by selecting from larger super-batches, we also leverage recent advances in model approximation to reduce the associated computational overhead. As a result, our approach--multimodal contrastive learning with joint example selection (JEST)--surpasses state-of-the-art models with up to 13$\times$ fewer iterations and 10$\times$ less computation. Essential to the performance of JEST is the ability to steer the data selection process towards the distribution of smaller, well-curated datasets via pretrained reference models, exposing the level of data curation as a new dimension for neural scaling laws.

A two-year experiment was conducted with a local maize hybrid under full (F) and deficit (D) drip irrigation and rainfed conditions (R) to estimate maize evapotranspiration in Bosnia and Herzegovina (BiH). Three approaches, namely, A&P, SIMDualKc (SD), and vegetation index (VI), to estimate the actual crop coefficient (Kc act), the actual basal crop coefficient (Kcb act), and the actual crop evapotranspiration (ETc act), were applied with the dual crop coefficient method and remote sensing (RS) data for the first time. While Kcb act from all approaches matched FAO56 tabulated values, SD showed differences in comparison to A&P of up to 0.24 in D and R conditions, especially in the initial and mid-season stages. VI demonstrated very good performance in all treatments. In F, the obtained Kc act for all approaches during the initial and end stages were higher than the tabulated values, ranging from 0.71 to 0.87 for the Kc ini act and from 0.80 to 1.06 for the Kc end act, while the mid-season period showed very good agreement with the literature. The maize crop evapotranspiration range is 769–813 mm, 480–752 mm, and 332–618 mm for F, D, and R, respectively. The results confirmed the suitability of both approaches (SD and VI) to estimate maize crop evapotranspiration under F, with the VI approach demonstrating an advantage in calculating Kcb act, Kc act, and ETc act values under water stress conditions. The higher observed yields (67.6%) under irrigation conditions emphasize the need to transition from rainfed to irrigation-dependent agriculture in BiH, even for drought-resistant crops like maize.

To fully characterize the orientation dependence of magnetization transfer (MT) and inhomogeneous MT (ihMT) measures in the whole white matter (WM), for both single‐fiber and crossing‐fiber voxels.

The availability of powerful open-source large language models (LLMs) opens exciting use-cases, such as automated personal assistants that adapt to the user's unique data and demands. Two key desiderata for such assistants are personalization-in the sense that the assistant should reflect the user's own style-and privacy-in the sense that users may prefer to always store their personal data locally, on their own computing device. We present a new design for such an automated assistant, for the specific use case of personal assistant for email generation, which we call Panza. Specifically, Panza can be both trained and inferenced locally on commodity hardware, and is personalized to the user's writing style. Panza's personalization features are based on a new technique called data playback, which allows us to fine-tune an LLM to better reflect a user's writing style using limited data. We show that, by combining efficient fine-tuning and inference methods, Panza can be executed entirely locally using limited resources-specifically, it can be executed within the same resources as a free Google Colab instance. Finally, our key methodological contribution is a careful study of evaluation metrics, and of how different choices of system components (e.g. the use of Retrieval-Augmented Generation or different fine-tuning approaches) impact the system's performance.