Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting the electroporated tissue area trajectory in Electroporation-based protocol optimization

Electroporation (EP), the temporary or permanent permeabilization of the cell membrane induced by an electric field, is the basis of various applications in medicine and food processing. In EP-based protocol optimization in terms of pulse number, such as in electrochemotherapy (ECT), irreversible electroporation (IRE), and gene electrotransfer (GET), it is essential to reach an optimal dose-response, that is, the pulse dose that maximizes the electroporated tissue area while minimizing damage. Predicting the electroporated tissue area variation in time, i.e., its trajectory, requires measuring the EP threshold trajectory and understanding the interaction between both trajectories and the damaged tissue area trajectory. Here we introduce a new methodology, based on the analysis of the nonlinear dynamic interaction of the EP threshold and damaged tissue area trajectories, that shows that the EP threshold trajectory is the time gradient of the electric field. This allows predicting the electroporated area trajectory with a single electroporated area measurement at the last pulse, thus avoiding the need to measure the EP threshold trajectory, a rather cumbersome task. Also, it makes it possible to explain at the macroscopic level why the EP threshold trajectory has an approximate exponential time decrease while the EP threshold isoline trajectory, aka the electroporated tissue area trajectory, has an approximate logarithmic time increase. Further, it permits predicting an optimal dose response in terms of pulse number in an EP-based protocol, with a single electroporated tissue area measurement. Examples of its application to an in vitro vegetal model, and to an in vivo skinfold chamber shed new light on the nonlinear dynamic behavior of electroporated tissue area, EP threshold, and damaged tissue area trajectories, paving the way for optimal treatment planning in EP-based protocols.
JML and Aspects: The Benefits of Instrumenting JML Features with AspectJ

The Java Modeling Language (JML) is used to specify designs of Java classes and interfaces. To this end, JML has a rich set of features for specifying methods, including specification inheritance. Thus, the most fundamental motivation for employing JML is to improve functional software correctness of Java applications, and helps to reduce corrective maintenance effort of those applications. Previously, we presented a new JML compiler (ajmlc) that generates aspects (AspectJ) for contract enforcement. This paper describes the main reasons to instrument JML features with AspectJ, with particular emphasis on issues related to instrumentation code size — we also defined guidelines to use ajmlc that always generate compact instrumented code than the classical JML compiler (jmlc). In addition, we discuss the analogy between JML and AspectJ, and how the ajmlc also deals with Java ME applications, which is not possible with jmlc. Moreover, we implemented other JML features such as the new the new assertion semantics based on “strong validity” presented elsewhere. The paper includes studies to compare the final code generated by ajmlc with the one produced by jmlc. Results indicate that the overhead in code size produced by our compiler is very small when using the proposed guidelines, which is essential for Java ME applications.
Versatile, fast and accurate frequency excursions with a semiconductor laser

Achieving accurate arbitrary frequency excursions with a laser can be quite a technical challenge, especially when steep slopes (GHz/$\mu$s) are required, due to both deterministic and stochastic frequency fluctuations. In this work we present a multi-stage correction combining four techniques: pre-distorsion of the laser modulation, iterative correction, opto-electronic feedback loop and feed-forward correction. This combination allows not only to compensate for the non-instantaneous response of the laser to an input modulation, but also to correct in real time the stochastic frequency fluctuations. We implement this multi-stage architecture on a commercial DBR laser and verify its efficiency, first with monochromatic operation and second with highly demanding frequency excursions. We demonstrate that our multi-stage correction not only enables a strong reduction of the laser linewidth, but also allows steep frequency excursions with non-linearities well below 1%, and a laser spectral purity consistently better than $100$ kHz even in the midst of GHz-scale frequency excursions.
Resonances in nonlinear systems with a decaying chirped-frequency excitation and noise

The influence of multiplicative white noise on the resonance capture of strongly nonlinear oscillatory systems under chirped-frequency excitations is investigated. It is assumed that the intensity of the perturbation decays polynomially with time, and its frequency grows according to a power low. Resonant solutions with a growing amplitude and phase, synchronized with the excitation, are considered. The persistence of such a regime in the presence of stochastic perturbations is discussed. In particular, conditions are described that guarantee the stochastic stability of the resonant modes on infinite or asymptotically large time intervals. The technique used is based on a combination of the averaging method, stability analysis and construction of stochastic Lyapunov functions. The proposed theory is applied to the Duffing oscillator with a chirped-frequency excitation and noise.
Maximizing Phylogenetic Diversity under Time Pressure: Planning with Extinctions Ahead

Phylogenetic Diversity (PD) is a measure of the overall biodiversity of a set of present-day species (taxa) within a phylogenetic tree. In Maximize Phylogenetic Diversity (MPD) one is asked to find a set of taxa (of bounded size/cost) for which this measure is maximized. MPD is a relevant problem in conservation planning, where there are not enough resources to preserve all taxa and minimizing the overall loss of biodiversity is critical. We consider an extension of this problem, motivated by real-world concerns, in which each taxon not only requires a certain amount of time to save, but also has an extinction time after which it can no longer be saved. In addition there may be multiple teams available to work on preservation efforts in parallel; we consider two variants of the problem based on whether teams are allowed to collaborate on the same taxa. These problems have much in common with machine scheduling problems, (with taxa corresponding to tasks and teams corresponding to machines), but with the objective function (the phylogenetic diversity) inspired by biological considerations. Our extensions are, in contrast to the original MPD, NP-hard, even in very restricted cases. We provide several algorithms and hardness-results and thereby show that the problems are fixed-parameter tractable (FPT) when parameterized the target phylogenetic diversity, and that the problem where teams are allowed to collaborate is FPT when parameterized the acceptable loss of diversity.
Automatic Annotation of Grammaticality in Child-Caregiver Conversations

The acquisition of grammar has been a central question to adjudicate between theories of language acquisition. In order to conduct faster, more reproducible, and larger-scale corpus studies on grammaticality in child-caregiver conversations, tools for automatic annotation can offer an effective alternative to tedious manual annotation. We propose a coding scheme for context-dependent grammaticality in child-caregiver conversations and annotate more than 4,000 utterances from a large corpus of transcribed conversations. Based on these annotations, we train and evaluate a range of NLP models. Our results show that fine-tuned Transformer-based models perform best, achieving human inter-annotation agreement levels.As a first application and sanity check of this tool, we use the trained models to annotate a corpus almost two orders of magnitude larger than the manually annotated data and verify that children's grammaticality shows a steady increase with age.This work contributes to the growing literature on applying state-of-the-art NLP methods to help study child language acquisition at scale.
The Era of Semantic Decoding

Recent work demonstrated great promise in the idea of orchestrating collaborations between LLMs, human input, and various tools to address the inherent limitations of LLMs. We propose a novel perspective called semantic decoding, which frames these collaborative processes as optimization procedures in semantic space. Specifically, we conceptualize LLMs as semantic processors that manipulate meaningful pieces of information that we call semantic tokens (known thoughts). LLMs are among a large pool of other semantic processors, including humans and tools, such as search engines or code executors. Collectively, semantic processors engage in dynamic exchanges of semantic tokens to progressively construct high-utility outputs. We refer to these orchestrated interactions among semantic processors, optimizing and searching in semantic space, as semantic decoding algorithms. This concept draws a direct parallel to the well-studied problem of syntactic decoding, which involves crafting algorithms to best exploit auto-regressive language models for extracting high-utility sequences of syntactic tokens. By focusing on the semantic level and disregarding syntactic details, we gain a fresh perspective on the engineering of AI systems, enabling us to imagine systems with much greater complexity and capabilities. In this position paper, we formalize the transition from syntactic to semantic tokens as well as the analogy between syntactic and semantic decoding. Subsequently, we explore the possibilities of optimizing within the space of semantic tokens via semantic decoding algorithms. We conclude with a list of research opportunities and questions arising from this fresh perspective. The semantic decoding perspective offers a powerful abstraction for search and optimization directly in the space of meaningful concepts, with semantic tokens as the fundamental units of a new type of computation.
Whose Side Are You On? Investigating the Political Stance of Large Language Models

Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval. As the widespread adoption of LLMs continues to surge, it becomes increasingly crucial to ensure that these models yield responses that are politically impartial, with the aim of preventing information bubbles, upholding fairness in representation, and mitigating confirmation bias. In this paper, we propose a quantitative framework and pipeline designed to systematically investigate the political orientation of LLMs. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. Across topics, the results indicate that LLMs exhibit a tendency to provide responses that closely align with liberal or left-leaning perspectives rather than conservative or right-leaning ones when user queries include details pertaining to occupation, race, or political affiliation. The findings presented in this study not only reaffirm earlier observations regarding the left-leaning characteristics of LLMs but also surface particular attributes, such as occupation, that are particularly susceptible to such inclinations even when directly steered towards conservatism. As a recommendation to avoid these models providing politicised responses, users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
Science based AI model certification for untrained operational environments with application in traffic state estimation

The expanding role of Artificial Intelligence (AI) in diverse engineering domains highlights the challenges associated with deploying AI models in new operational environments, involving substantial investments in data collection and model training. Rapid application of AI necessitates evaluating the feasibility of utilizing pre-trained models in unobserved operational settings with minimal or no additional data. However, interpreting the opaque nature of AI's black-box models remains a persistent challenge. Addressing this issue, this paper proposes a science-based certification methodology to assess the viability of employing pre-trained data-driven models in untrained operational environments. The methodology advocates a profound integration of domain knowledge, leveraging theoretical and analytical models from physics and related disciplines, with data-driven AI models. This novel approach introduces tools to facilitate the development of secure engineering systems, providing decision-makers with confidence in the trustworthiness and safety of AI-based models across diverse environments characterized by limited training data and dynamic, uncertain conditions. The paper demonstrates the efficacy of this methodology in real-world safety-critical scenarios, particularly in the context of traffic state estimation. Through simulation results, the study illustrates how the proposed methodology efficiently quantifies physical inconsistencies exhibited by pre-trained AI models. By utilizing analytical models, the methodology offers a means to gauge the applicability of pre-trained AI models in new operational environments. This research contributes to advancing the understanding and deployment of AI models, offering a robust certification framework that enhances confidence in their reliability and safety across a spectrum of operational conditions.
Modem Optimization of High-Mobility Scenarios: A Deep-Learning-Inspired Approach

The next generation wireless communication networks are required to support high-mobility scenarios, such as reliable data transmission for high-speed railways. Nevertheless, widely utilized multi-carrier modulation, the orthogonal frequency division multiplex (OFDM), cannot deal with the severe Doppler spread brought by high mobility. To address this problem, some new modulation schemes, e.g. orthogonal time frequency space and affine frequency division multiplexing, have been proposed with different design criteria from OFDM, which promote reliability with the cost of extremely high implementation complexity. On the other hand, end-to-end systems achieve excellent gains by exploiting neural networks to replace traditional transmitters and receivers, but have to retrain and update continually with channel varying. In this paper, we propose the Modem Network (ModNet) to design a novel modem scheme. Compared with end-to-end systems, channels are directly fed into the network and we can directly get a modem scheme through ModNet. Then, the Tri-Phase training strategy is proposed, which mainly utilizes the siamese structure to unify the learned modem scheme without retraining frequently faced up with time-varying channels. Simulation results show the proposed modem scheme outperforms OFDM systems under different highmobility channel statistics.
Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning

Self-supervised representation learning has been highly promising for histopathology image analysis with numerous approaches leveraging their patient-slide-patch hierarchy to learn better representations. In this paper, we explore how the combination of domain specific natural language information with such hierarchical visual representations can benefit rich representation learning for medical image tasks. Building on automated language description generation for features visible in histopathology images, we present a novel language-tied self-supervised learning framework, Hierarchical Language-tied Self-Supervision (HLSS) for histopathology images. We explore contrastive objectives and granular language description based text alignment at multiple hierarchies to inject language modality information into the visual representations. Our resulting model achieves state-of-the-art performance on two medical imaging benchmarks, OpenSRH and TCGA datasets. Our framework also provides better interpretability with our language aligned representation space. Code is available at https://github.com/Hasindri/HLSS.
More than Just Statistical Recurrence: Human and Machine Unsupervised Learning of M\=aori Word Segmentation across Morphological Processes

Non-M\=aori-speaking New Zealanders (NMS)are able to segment M\=aori words in a highlysimilar way to fluent speakers (Panther et al.,2024). This ability is assumed to derive through the identification and extraction of statistically recurrent forms. We examine this assumption by asking how NMS segmentations compare to those produced by Morfessor, an unsupervised machine learning model that operates based on statistical recurrence, across words formed by a variety of morphological processes. Both NMS and Morfessor succeed in segmenting words formed by concatenative processes (compounding and affixation without allomorphy), but NMS also succeed for words that invoke templates (reduplication and allomorphy) and other cues to morphological structure, implying that their learning process is sensitive to more than just statistical recurrence.
The genre of applications requiring long-term and continuous monitoring of ECG signals

Presently, cardiovascular issues have become an emerging challenge for physiologists and researchers. Due to the fact, that the population is growing at a rapid pace and of course there is a shortage of doctors and hospitals, the demand and cost of healthcare is rising and is a topic of major concern. Also, it is not always possible for the affected person to visit the hospital or the doctor. In addition to this, specifically talking about people suffering from cardiac diseases, they require a system which continuously monitors their heart activities so that instantaneous detection of cardiac abnormalities at an early stage can be performed, consequently followed by an immediate remedy/treatment by the doctor. As a result, a huge amount of attention has been diverted to make the health-care systems intelligent i.e., real-time, continuous, long-term sensing and monitoring of bio-signals which can be carried out even at remote locations whether the person is in motion or at rest. There are several methods to measure the bio-signals but Electrocardiogram (ECG) is the premium method for measuring the heart signals. In this paper, we review the domains where continuous and long-term supervision of heart-rate is essential; the areas include elderly and differently-abled people, fetal ECG monitoring, infant monitoring, monitoring for automobile drivers, etc.
An Effective Learning Management System for Revealing Student Performance Attributes

A learning management system streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an e-learning system plays a crucial role in rule regulation, policy establishment, and system development. However, existing LMSs do not have embedded mining modules to directly extract knowledge. As educational modes become more complex, educational data mining efficiency from those heterogeneous student learning behaviours is gradually degraded. Therefore, an LMS incorporated with an advanced educational mining module is proposed in this study, as a means to mine efficiently from student performance records to provide valuable insights for educators in helping plan effective learning pedagogies, improve curriculum design, and guarantee quality of teaching. Through two illustrative case studies, experimental results demonstrate increased mining efficiency of the proposed mining module without information loss compared to classic educational mining algorithms. The mined knowledge reveals a set of attributes that significantly impact student academic performance, and further classification evaluation validates the identified attributes. The design and application of such an effective LMS can enable educators to learn from past student performance experiences, empowering them to guide and intervene with students in time, and eventually improve their academic success.
A Taxonomy of Ambiguity Types for NLP

Ambiguity is an critical component of language that allows for more effective communication between speakers, but is often ignored in NLP. Recent work suggests that NLP systems may struggle to grasp certain elements of human language understanding because they may not handle ambiguities at the level that humans naturally do in communication. Additionally, different types of ambiguity may serve different purposes and require different approaches for resolution, and we aim to investigate how language models' abilities vary across types. We propose a taxonomy of ambiguity types as seen in English to facilitate NLP analysis. Our taxonomy can help make meaningful splits in language ambiguity data, allowing for more fine-grained assessments of both datasets and model performance.
Epitaxially defined Luttinger liquids on MoS$_2$ bicrystals

A mirror twin boundary (MTB) in a transition metal dichalcogenide (TMD) monolayer can host one-dimensional electron liquid of a topological nature with tunable interactions. Unfortunately, the electrical characterization of such boundaries has been challenging due to the paucity of samples with large enough size and high quality. Here, we report an epitaxial growth of monolayer molybdenum disulfide (MoS$_2$) bicrystals with well-isolated MTBs that are tens of micrometers long. Conductance measurements of these MTBs exhibit power-law behaviors as a function of temperature and bias voltage up to room temperature, consistent with electrons tunneling into a Luttinger liquid. Transport measurements of two distinct types of MTBs reveal the critical role of the atomic-scale defects. This study demonstrates that MTBs in TMD monolayers provide an exciting new platform for studying the interplay between electronic interactions and topology.
Visually Grounded Speech Models have a Mutual Exclusivity Bias

When children learn new words, they employ constraints such as the mutual exclusivity (ME) bias: a novel word is mapped to a novel object rather than a familiar one. This bias has been studied computationally, but only in models that use discrete word representations as input, ignoring the high variability of spoken words. We investigate the ME bias in the context of visually grounded speech models that learn from natural images and continuous speech audio. Concretely, we train a model on familiar words and test its ME bias by asking it to select between a novel and a familiar object when queried with a novel word. To simulate prior acoustic and visual knowledge, we experiment with several initialisation strategies using pretrained speech and vision networks. Our findings reveal the ME bias across the different initialisation approaches, with a stronger bias in models with more prior (in particular, visual) knowledge. Additional tests confirm the robustness of our results, even when different loss functions are considered.