Sayed Pedram Haeri Boroujeni, Niloufar Mehrabi, Patrick Woods
et al.
Large Language Models (LLMs) have achieved remarkable progress across reasoning, generation, and decision-making tasks, yet deploying them on mobile, embedded, and edge devices remains particularly challenging. On-device LLM inference is heavily constrained by the memory and bandwidth overhead of the key-value (KV) cache, which grows linearly with context length and often dominates decoding cost. Existing KV-cache quantization schemes typically rely on fixed precision or hand-crafted heuristics, thereby wasting bits on low-impact tokens while over-compressing informative ones, leading to avoidable accuracy degradation. Inspired by Huffman coding's principle of variable-length allocation, we propose adaptive KV-cache quantization, a learned policy that assigns bit-width proportional to token importance, minimizing expected memory and latency without sacrificing competitive accuracy. Our framework extracts lightweight token-level features, including token frequency, quality score, attention variance, and entropy-based uncertainty, and feeds them into a compact data-driven controller that dynamically selects KV precision from {2-bit, 4-bit, 8-bit, FP16} during decoding. This adaptive precision policy reduces KV memory footprint and latency while improving accuracy compared to static KV quantization and rule-based baselines, and maintaining competitive accuracy close to FP16 inference across standard LLM benchmarks. Extensive experiments across multiple commonsense reasoning benchmarks using SmolLM-135M, SmolLM-360M, and SmolLM-1.7B demonstrate that our controller consistently improves the accuracy-latency trade-off. For instance, with SmolLM-360M on HellaSwag, our method reduces decoding latency (ms/token) by 17.75% relative to static KV quantization, improves accuracy by 7.60 points, and remains within only 0.30 points of FP16 inference.
The rapid development of Industry 4.0 technologies requires robust and comprehensive standardization to ensure interoperability, safety and efficiency in the Industry of the Future. This paper examines the fundamental role and functionality of standardization, with a particular focus on its importance in Europe's regulatory framework. Based on this, selected topics in context of standardization activities in context intelligent manufacturing and digital twins are highlighted and, by that, an overview of the Industry 4.0 standards framework is provided. This paper serves both as an informative guide to the existing standards in Industry 4.0 with respect to Artificial Intelligence and Digital Twins, and as a call to action for increased cooperation between standardization bodies and the research community. By fostering such collaboration, we aim to facilitate the continued development and implementation of standards that will drive innovation and progress in the manufacturing sector.
Intellectual property rights (IPR) and standards are important institutions that by shaping appropriability conditions of companies impact international trade flows and the rate and direction of technological progress and innovation activity. We shed light on microfoundations of IPR and standardization capabilities and explore how companies have developed their IPR and standardization strategies and adapted to related institutional changes in the European Single Market. The analysis of the IPR and standardization strategies of companies active in Päijät-Häme region of Finland, a northern part of the European Union, reveals that only a few companies have explicit IPR and standardization strategies, but several have systematic approaches to following the development of standards and IPR environments in their industries. Companies build dynamic IPR and standardization capabilities and adapt their IPR and standardization strategies to the changing institutional environment via experiential learning.
Padam Prasad Paudel, Sunyong Park, Kwang Cheol Oh
et al.
Ginkgo biloba trees are widely planted in urban areas of developed countries for their resilience, longevity and aesthetic appeal. Annual pruning to control tree size, shape and interference with traffic and pedestrians generates large volumes of unutilized Ginkgo biomass. This study aimed to valorize these pruning residues into charcoal by optimizing pyrolysis conditions and evaluating its fuel properties. The pyrolysis experiment was conducted at 400 to 600 degrees Celsius, after oven drying pretreatment. The mass yield of charcoal was found to vary from 27.33 to 32.05 percent and the approximate volume shrinkage was found to be 41.19 to 49.97 percent. The fuel properties of the charcoals were evaluated using the moisture absorption test, proximate and ultimate analysis, thermogravimetry, calorimetry and inductively coupled plasma optical emission spectrometry. The calorific value improved from 20.76 to 34.26 MJ per kg with energy yield up to 46.75 percent. Charcoal exhibited superior thermal stability and better combustion performance. The results revealed satisfactory properties compared with other biomass, coal and biochar standards. The product complied with first grade standards at 550 and 600 degrees Celsius and second grade wood charcoal standards at other temperatures. However, higher concentrations of some heavy metals like Zn indicate the need for pretreatment and further research on copyrolysis for resource optimization. This study highlights the dual benefits of waste management and renewable energy, providing insights for urban planning and policymaking.
Théo Delemazure, Rupert Freeman, Jérôme Lang
et al.
In many proportional parliamentary elections, electoral thresholds (typically 3-5%) are used to promote stability and governability by preventing the election of parties with very small representation. However, these thresholds often result in a significant number of "wasted votes" cast for parties that fail to meet the threshold, which reduces representativeness. One proposal is to allow voters to specify replacement votes, by either indicating a second choice party or by ranking a subset of the parties, but there are several ways of deciding on the scores of the parties (and thus the composition of the parliament) given those votes. We introduce a formal model of party voting with thresholds, and compare a variety of party selection rules axiomatically, and experimentally using a dataset we collected during the 2024 European election in France. We identify three particularly attractive rules, called Direct Winners Only (DO), Single Transferable Vote (STV) and Greedy Plurality (GP).
Abhay Kumara Sri Krishna Nandiraju, Gondy Leroy, David Kauchak
et al.
Understanding health information is essential in achieving and maintaining a healthy life. We focus on simplifying health information for better understanding. With the availability of generative AI, the simplification process has become efficient and of reasonable quality, however, the algorithms remove information that may be crucial for comprehension. In this study, we compare generative AI to detect missing information in simplified text, evaluate its importance, and fix the text with the missing information. We collected 50 health information texts and simplified them using gpt-4-0613. We compare five approaches to identify missing elements and regenerate the text by inserting the missing elements. These five approaches involve adding missing entities and missing words in various ways: 1) adding all the missing entities, 2) adding all missing words, 3) adding the top-3 entities ranked by gpt-4-0613, and 4, 5) serving as controls for comparison, adding randomly chosen entities. We use cosine similarity and ROUGE scores to evaluate the semantic similarity and content overlap between the original, simplified, and reconstructed simplified text. We do this for both summaries and full text. Overall, we find that adding missing entities improves the text. Adding all the missing entities resulted in better text regeneration, which was better than adding the top-ranked entities or words, or random words. Current tools can identify these entities, but are not valuable in ranking them.
Standardization of clinical reports is crucial for improving the quality of healthcare and facilitating data integration. The lack of unified standards, including format, terminology, and style, is a great challenge in clinical fundus diagnostic reports, which increases the difficulty for large language models (LLMs) to understand the data. To address this, we construct a bilingual standard terminology, containing fundus clinical terms and commonly used descriptions in clinical diagnosis. Then, we establish two models, RetSTA-7B-Zero and RetSTA-7B. RetSTA-7B-Zero, fine-tuned on an augmented dataset simulating clinical scenarios, demonstrates powerful standardization behaviors. However, it encounters a challenge of limitation to cover a wider range of diseases. To further enhance standardization performance, we build RetSTA-7B, which integrates a substantial amount of standardized data generated by RetSTA-7B-Zero along with corresponding English data, covering diverse complex clinical scenarios and achieving report-level standardization for the first time. Experimental results demonstrate that RetSTA-7B outperforms other compared LLMs in bilingual standardization task, which validates its superior performance and generalizability. The checkpoints are available at https://github.com/AB-Story/RetSTA-7B.
Anastasia Baluta, Maria Pruzhinskaya, Philippe Rosnet
et al.
Type Ia Supernovae (SNe Ia) are used as reliable cosmic distance indicators and their standardization is necessary for a more accurate measurement of the cosmological parameters of the Universe. However, the Hubble diagram still shows some intrinsic dispersion, potentially influenced by the supernova's environment. In this study, we reproduce the Hubble diagram fit for the Pantheon supernova sample, and also investigate the possibility of introducing various standardization equations for supernovae exploded in early- and late-type galaxies. We analyze 330 Pantheon SNe Ia to study how host galaxy morphology affects their standardization. We find that SNe Ia hosted by early-type galaxies have different standardization parameters compared to those hosted by late-type galaxies. We conclude that correcting supernova luminosity for host galaxy morphology is essential to perform the precise cosmological analysis.
Miroslaw Staron, Jonathan Strom, Albin Karlsson
et al.
Standardization processes build upon consensus between partners, which depends on their ability to identify points of disagreement and resolving them. Large standardization organizations, like the 3GPP or ISO, rely on leaders of work packages who can correctly, and efficiently, identify disagreements, discuss them and reach a consensus. This task, however, is effort-, labor-intensive and costly. In this paper, we address the problem of identifying similarities, dissimilarities and discussion points using large language models. In a design science research study, we work with one of the organizations which leads several workgroups in the 3GPP standard. Our goal is to understand how well the language models can support the standardization process in becoming more cost-efficient, faster and more reliable. Our results show that generic models for text summarization correlate well with domain expert's and delegate's assessments (Pearson correlation between 0.66 and 0.98), but that there is a need for domain-specific models to provide better discussion materials for the standardization groups.
Cedric Westphal, Jungha Hong, Shin-Gak Kang
et al.
New applications are being supported by current and future networks. In particular, it is expected that Metaverse applications will be deployed in the near future, as 5G and 6G network provide sufficient bandwidth and sufficiently low latency to provide a satisfying end-user experience. However, networks still need to evolve to better support this type of application. We present here a basic taxonomy of the metaverse, which allows to identify some of the networking requirements for such an application; we also provide an overview of the current state of balthe standardization efforts in different standardization organizations, including ITU-T, 3GPP, IETF and MPAI.
When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates. The treatment coefficient of the outcome model targets a conditional treatment effect. Model-based standardization is typically applied to average the model predictions over the target covariate distribution, and generate a covariate-adjusted estimate of the marginal treatment effect. The standard approach to model-based standardization involves maximum-likelihood estimation and use of the non-parametric bootstrap. We introduce a novel, general-purpose, model-based standardization method based on multiple imputation that is easily applicable when the outcome model is a generalized linear model. We term our proposed approach multiple imputation marginalization (MIM). MIM consists of two main stages: the generation of synthetic datasets and their analysis. MIM accommodates a Bayesian statistical framework, which naturally allows for the principled propagation of uncertainty, integrates the analysis into a probabilistic framework, and allows for the incorporation of prior evidence. We conduct a simulation study to benchmark the finite-sample performance of MIM in conjunction with a parametric outcome model. The simulations provide proof-of-principle in scenarios with binary outcomes, continuous-valued covariates, a logistic outcome model and the marginal log odds ratio as the target effect measure. When parametric modeling assumptions hold, MIM yields unbiased estimation in the target covariate distribution, valid coverage rates, and similar precision and efficiency than the standard approach to model-based standardization.
Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3%~4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach. Codes and models are released here: https://github.com/LuJunru/NapSS.
Stuti Pathak, Thomas M. McDonald, Seppe Sels
et al.
The processing, storage and transmission of large-scale point clouds is an ongoing challenge in the computer vision community which hinders progress in the application of 3D models to real-world settings, such as autonomous driving, virtual reality and remote sensing. We propose a novel, one-shot point cloud simplification method which preserves both the salient structural features and the overall shape of a point cloud without any prior surface reconstruction step. Our method employs Gaussian processes suitable for functions defined on Riemannian manifolds, allowing us to model the surface variation function across any given point cloud. A simplified version of the original cloud is obtained by sequentially selecting points using a greedy sparsification scheme. The selection criterion used for this scheme ensures that the simplified cloud best represents the surface variation of the original point cloud. We evaluate our method on several benchmark and self-acquired point clouds, compare it to a range of existing methods, demonstrate its application in downstream tasks of registration and surface reconstruction, and show that our method is competitive both in terms of empirical performance and computational efficiency. The code is available at \href{https://github.com/stutipathak5/gps-for-point-clouds}{https://github.com/stutipathak5/gps-for-point-clouds}.
Devanand Gojiya, Paresh Davara, Vanraj Gohil
et al.
AbstractThe investigation was intended to produce the protein‐enriched extruded snack by incorporation, comprising corn flour and defatted sesame flour. Experiment was executed using central composite rotatable design through four variables, feed moisture content (10–18%, w.b), defatted sesame flour (10%–40%), die head temperature (100–160°C), and screw speed (200–300 rpm). The quality of extrudates was evaluated by examining protein content, extrudate hardness, water solubility index, and water absorption index of the product as well as bulk density, expansion ratio, and sensory attribute. The results analysis revealed that defatted sesame flour significantly did utmost influence among tested variables followed by feed moisture content, die head temperature with screw speed. The admixture symmetry with standardized extrusion process amended nutritive class of corn flour‐based extrudate in terms of protein content by 19.21 g/100 g.Novelty impact statement The defatted sesame flour (DSF) is residual; afterward, oil extraction was used for formulating novel protein‐rich extrudate. The impact of various operational and feed parameters has been investigated, standardized, and validated for formulating protein‐augmented extrudate using response surface methodology. The DSF can be utilized effectively in protein‐rich corn‐based extrudate development which has comparatively higher protein (19.21 g/100 g) content compare to the control.
Meryem Banu Cavlak, Gagandeep Singh, Mohammed Alser
et al.
Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art basecallers employ complex deep learning models to achieve high basecalling accuracy. This makes basecalling computationally inefficient and memory-hungry, bottlenecking the entire genome analysis pipeline. However, for many applications, the majority of reads do no match the reference genome of interest (i.e., target reference) and thus are discarded in later steps in the genomics pipeline, wasting the basecalling computation. To overcome this issue, we propose TargetCall, the first pre-basecalling filter to eliminate the wasted computation in basecalling. TargetCall's key idea is to discard reads that will not match the target reference (i.e., off-target reads) prior to basecalling. TargetCall consists of two main components: (1) LightCall, a lightweight neural network basecaller that produces noisy reads; and (2) Similarity Check, which labels each of these noisy reads as on-target or off-target by matching them to the target reference. Our thorough experimental evaluations show that TargetCall 1) improves the end-to-end basecalling runtime performance of the state-of-the-art basecaller by 3.31x while maintaining high (98.88%) recall in keeping on-target reads, 2) maintains high accuracy in downstream analysis, and 3) achieves better runtime performance, throughput, recall, precision, and generality compared to prior works. TargetCall is available at https://github.com/CMU-SAFARI/TargetCall.
Motion planning problems can be simplified by admissible projections of the configuration space to sequences of lower-dimensional quotient-spaces, called sequential simplifications. To exploit sequential simplifications, we present the Quotient-space Rapidly-exploring Random Trees (QRRT) algorithm. QRRT takes as input a start and a goal configuration, and a sequence of quotient-spaces. The algorithm grows trees on the quotient-spaces both sequentially and simultaneously to guarantee a dense coverage. QRRT is shown to be (1) probabilistically complete, and (2) can reduce the runtime by at least one order of magnitude. However, we show in experiments that the runtime varies substantially between different quotient-space sequences. To find out why, we perform an additional experiment, showing that the more narrow an environment, the more a quotient-space sequence can reduce runtime.
Mahdi Soltan Mohammadi, Kazem Cheshmi, Ganesh Gopalakrishnan
et al.
Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication overlap, verification, and shape analysis. For sparse matrix codes, array data dependence analysis is made more difficult by the use of index arrays that make it possible to store only the nonzero entries of the matrix (e.g., in A[B[i]], B is an index array). Here, dependence analysis is often stymied by such indirect array accesses due to the values of the index array not being available at compile time. Consequently, many dependences cannot be proven unsatisfiable or determined until runtime. Nonetheless, index arrays in sparse matrix codes often have properties such as monotonicity of index array elements that can be exploited to reduce the amount of runtime analysis needed. In this paper, we contribute a formulation of array data dependence analysis that includes encoding index array properties as universally quantified constraints. This makes it possible to leverage existing SMT solvers to determine whether such dependences are unsatisfiable and significantly reduces the number of dependences that require runtime analysis in a set of eight sparse matrix kernels. Another contribution is an algorithm for simplifying the remaining satisfiable data dependences by discovering equalities and/or subset relationships. These simplifications are essential to make a runtime-inspection-based approach feasible.
The Internet of Things (IoT) propagates the paradigm of interconnecting billions of heterogeneous devices by various manufacturers. To enable IoT applications, the communication between IoT devices follows specifications defined by standard developing organizations. In this paper, we present a case study that investigates disclosed insecurities of the popular IoT standard ZigBee, and derive general lessons about security economics in IoT standardization efforts. We discuss the motivation of IoT standardization efforts that are primarily driven from an economic perspective, in which large investments in security are not considered necessary since the consumers do not reward them. Success at the market is achieved by being quick-to-market, providing functional features and offering easy integration for complementors. Nevertheless, manufacturers should not only consider economic reasons but also see their responsibility to protect humans and technological infrastructures from being threatened by insecure IoT products. In this context, we propose a number of recommendations to strengthen the security design in future IoT standardization efforts, ranging from the definition of a precise security model to the enforcement of an update policy.