Science & Mathematics

Maqolada ichki ishlar organlarida ijro intizomini mustahkamlashning konstitutsiyaviy kafolatlari tahlilqilingan bo‘lib, unda ijro intizomining huquqiy asoslari, davlat organlari mas’uliyati, qonun ustuvorligi, fuqarolar huquqva erkinliklarini ta’minlash kabi Konstitutsiyadagi asosiy normalar bilan bog‘liq holda yoritilgan. Shuningdek, amaldagimexanizmlar sifatida ichki xizmat tekshiruvlar, raqamli monitoring va nazorat, kadrlarning xizmat va jangovar tayyorgarlikdarajasi va javobgarlik tizimi kabi vositalarning samaradorligi o‘rganilgan. Tadqiqot natijasida tizimda mavjud muammolarko‘rsatilib, ularni bartaraf etish bo‘yicha amaliy takliflar ishlab chiqilgan

Scipopus (Phaeopterina) quetzal sp. nov.urn:lsid:zoobank.org:act: 9AD7F67E-87E6-4721-A306-F23CD79B310EFig. 65Differential diagnosisScipopus (Phaeopterina) quetzal sp. nov. resembles the sympatric species S. (Ph.) guatemalensis and S. (Ph.) uniformis sp. nov. in having a brown median stripe on the clypeus, but it differs by the white microtrichose clypeus, the bright orange subapical band on the mid femur and the strongly convex female cervical sclerite.EtymologyThe species name is a noun in apposition inspired by the type locality.Type material examinedHolotypeGUATEMALA • 1 ♀; Baja Vera, Biotopo Quetzal; 15°12′49″ N, 15°12′49″ W; 1690 m a.s.l.; 7–10 May 2009; cloud forest; Malaise trap; debu00334712/MYCRO607-19 sequenced for CO1–5′, CO1–3′, 12S, 28S; UVG (dissected and photographed, Fig. 65A–D).DescriptionLENGTH. 12 mm.HEAD. Palpus orange, pale microtrichose and setulose, narrow (length 5.0 × height). Clypeus orange, brown medially, width ~1.6 × height, silvery microtrichose on entire surface. Frontal vitta dull, orange, microtrichose. Epicephalon light brown and/or orange, shiny, wide (width ⅔ or more of upper frontal vittal width at inner verticals), clearly delineated from upper frontal vitta. Paracephalon light brown and/ or orange. One lower fronto-orbital seta absent; all other head chaetotaxy well-developed.THORAX. Scutum black-brown, dark brown microtrichose with a wide, shiny, silvery-blue median sheen. Female cervical sclerite strongly convex. Postpronotal lobe black-brown, sparsely setulose on outer lateral margin. Notopleuron black-brown. Thorax dark black-brown with blue sheen, entirely silvery microtrichose. Legs dark brown, mid femur with subapical bright orange band; fore tarsomere 1 entirely white, mid tarsomere 1 brown, hind leg and tarsomeres unknown. Wing entirely brown infuscate.FEMALE ABDOMEN. T1 with fine, long, dark brown or black setae. Pleuron pale grey, faded on available specimen but apparently darker grey or brown on P1, P2 and dorsal half of P3–6. Posterior margin of T2 ~1.5× as wide as T1 dorsally. T1+2 ~2.0 × length of T3. Oviscape black-brown, microtrichosity not observed (dissected), ~3.0 × length of T6. Common duct very short, smooth, not delineated form paired duct. Paired duct short (4.0 × length of paired spermathecae), narrow, relatively parallel-sided. Paired spermathecal stems not differentiated from spermathecae. Paired spermathecae elongate, ringed by well-defined striae and ridges. Single spermathecal duct arising from base of paired duct, narrow, ½ the diameter and ¾ the length of paired duct, with small swollen bulb apically. Single spermatheca similar to paired but much smaller.MALE ABDOMEN. Not observed.DistributionGuatemala.

Ushbu maqolada rivojlangan mamlakatlarda maktabgacha ta’limning tarixiy taraqqiyot bosqichlari hamdamaktabgacha ta’lim mazmunining shakllanish jarayoni tahlil qilinadi. Shuningdek, maktabgacha ta’lim tizimini rivojlantirishningzamonaviy yo‘llari va metodlari, ilg‘or mamlakatlar tajribasining o‘ziga xos xususiyatlari, yangi islohotlar natijasidayuzaga kelgan imkoniyatlar hamda sohaning yutuqlari va ko‘rsatkichlari yoritilgan.

Ushbu maqolada sun’iy intellekt (SI) texnologiyalaridan foydalanish orqali raqamli biznes jarayonlarini optimallashtirishning nazariy va amaliy jihatlari tahlil qilinadi. Raqamli iqtisodiyot sharoitida korxonalar faoliyatining samaradorligini oshirish, xarajatlarni kamaytirish va qaror qabul qilish jarayonlarini tezlashtirishda sun’iy intellekt muhim omilga aylanmoqda. Tadqiqotda sun’iy intellektning biznes jarayonlariga integratsiyasi, avtomatlashtirish, ma’lumotlarni tahlil qilish, prognozlash va mijozlar bilan ishlashdagi roli yoritilgan. Shuningdek, mashinali o‘rganish, katta ma’lumotlar (Big Data), chat-botlar, intellektual tahlil tizimlari va raqamli platformalarning biznes samaradorligiga ta’siri o‘rganilgan. Maqola yakunida O‘zbekiston sharoitida sun’iy intellekt asosida raqamli biznes jarayonlarini rivojlantirish bo‘yicha amaliy taklif va tavsiyalar ilgari suriladi.

Dense 3D terrain information is critical for accurate landslide detection; however, acquiring such data at large scale is often limited by sensing infrastructure, coverage, and environmental conditions. Emerging sensing modalities (e.g., mmWave, low-cost radar, sparse LiDAR) can operate under adverse conditions, but inherently produce sparse and noisy 3D observations, raising an important question: \emph{how much sensing is sufficient for reliable landslide detection?} Given the absence of large-scale sparse-terrain datasets, we have constructed a physics-informed dataset by combining digital elevation models from OpenTopography with soil moisture data from NASA's SMAP. Subsequently, we have randomly generated landslide events and introduced controlled sparsification and stochastic noise to emulate sensing constraints. We have then developed a learning-based framework to reconstruct the underlying terrain and identify landslide-prone regions using a CNN + XGBoost method. Our results show that with only 20\% of the original observations, the proposed method achieves a detection accuracy of 80\% and a ROC-AUC of 0.868, with a reconstruction error (L2 Chamfer Distance) of 99.67~$m^2$ on the test set. We have further studied the impact of sparsity, highlighting the trade-off between sensing density and detection performance.

Objective: The rapid advancement of large language models (LLMs) in healthcare has opened new possibilities for AI-assisted clinical decision-making; however, safe and effective deployment remains challenging because of hallucination and domain misalignment. This study introduces DocTalk, a doctor-assistive question-answering (QA) system designed to generate concise, chart-grounded responses from de-identified discharge summaries. Methods: We developed a high-fidelity data generation pipeline that produces 500 clinically relevant QA pairs through sentence-level decomposition, chain-of-thought-guided question formulation, and expert review. Six open-source LLMs, including three general-purpose and three biomedical models, were evaluated before and after parameter-efficient instruction tuning using Low-Rank Adaptation (LoRA). The framework integrates prompt engineering, in-context learning, and medically aligned evaluation to support factual and interpretable responses. Results: On DocTalk-QA, instruction tuning produced consistent improvements across lexical, semantic, and clinical fidelity metrics. The best-performing tuned model, Llama-3-Med42-8B, achieved ROUGE-L = 0.40, BERTScore-F1 = 0.76, and UMLS-F1 = 0.49. To examine external generalization beyond the DocTalk-QA dataset, the two best-performing models, Llama-3.1-8B-Instruct and Llama-3-Med42-8B, were evaluated on PubMedQA before and after instruction tuning. Accuracy improved from 0.931 to 0.977 for Llama-3.1-8B-Instruct and from 0.935 to 0.943 for Llama-3-Med42-8B. Conclusion: DocTalk was deployed as a secure web-based platform for interactive EHR querying. These findings suggest that clinically grounded instruction tuning can improve medical QA performance and support practical clinical decision support.

Retrieval-augmented generation (RAG) can improve factual grounding without guaranteeing that every generated claim is supported. Previous work has studied claim-level conformal factuality in single-stage RAG and, more recently, multi-hop RAG. We examine its behavior in agentic RAG, where a language-model policy adaptively issues follow-up retrieval queries or terminates retrieval before response generation. We retain the retrieval-based relevance-scoring formulation and post-generation conformal filtering and evaluate six model-dataset configurations: GPT-6 Luna and Llama 3.1 8B on HotpotQA, Natural Questions, and TriviaQA. Each configuration uses 60 fixed questions, 100 seeded 30/30 calibration-test partitions, five conformal target levels, and a no-conformal-prediction baseline. Across the six configurations, higher target levels are accompanied by higher observed full-support rates and reduced answer availability. At the 95% target, non-empty response coverage ranges from 17.23% to 60.17%, claim retention from 10.72% to 43.16%, and full support among non-empty outputs from 89.14% to 95.56%. We formulate the adaptive retrieval trajectory explicitly and characterize the reliability-informativeness trade-off when conformal factuality is applied after agent termination.

This paper studies UAV swarm search in GNSS-denied maritime environments, where inter-UAV ranging drives cooperative localization and uncertainty propagates along the multi-hop reference chain, affecting connectivity, collision avoidance, and search effectiveness. We propose an uncertainty-aware control barrier function (CBF) framework approximating cascading localization covariance through Fisher information matrices. First, the cascading covariance model captures how ranging noise and neighbor uncertainty accumulate along the localization chain. Second, uncertainty-tightened CBFs incorporate the covariance model and its rate into margins for connectivity maintenance and collision avoidance, with conditional invariance and graceful-degradation bounds. Third, a hierarchical Voronoi-target strategy with formation keeping supports efficient forward-looking coverage. The distributed controller is validated with a standard unbiased extended Kalman filter in the loop. The framework is demonstrated on several polygon shapes and swarm sizes from 6 to 30 sUAVs. An estimator-in-the-loop run completes the search in 302.5 s, 20 Monte Carlo deployments confirm repeatable completion with all local QPs feasible and containment in at least 98.97% of UAV-frames, and six MBZIRC runs provide estimator-in-the-loop evidence. The source code is publicly available.

Rubric-based evaluation asks which level of a rubric a response meets. It is usually done by prompting a large language model (LLM) as a judge, which needs a generative call per judgment and reads every level description, so its cost grows with the rubric. We present RubriX, a rubric judge whose architecture is inspired by contrastive language models (CLM): a LoRA-adapted encoder embeds the input and each level description, and a softmax over scaled cosine similarities gives a probability for every level without text generation. Trained on public rubric data and proprietary synthetic data, RubriX reaches 75.4% exact accuracy on held-out rubrics, against 57.1% for TypeSafe's Jev and 49.8% for a Qwen3-8B judge. On eight human-labelled benchmark sets it is within 6 percentage points of Jev's accuracy on most of them, while Jev keeps a larger lead on two and ranks responses best on average; against the results reported in the G-Eval paper, RubriX outperforms G-Eval with GPT-3.5 on every SummEval dimension and both QAGS subsets. With level embeddings cached, RubriX's latency stays at about 28 ms per judgment as level descriptions grow from 10 to 3,000 tokens, while Jev's (approximate network time subtracted) rises from 44 ms to 211 ms and the Qwen3-8B judge's from 32 ms to 516 ms, making RubriX 7.7x faster than Jev and 18.8x faster than the Qwen3-8B judge at 3,000 tokens per level.

Change Detection & Captioning (CDC) aims to jointly localize changed regions and describe their semantic content from bi-temporal remote sensing images. While recent approaches have demonstrated the effectiveness of unified modeling, they primarily rely on shared representations or jointly optimized architectures, leaving task-specific communication under heterogeneous supervision less explored. However, detection and captioning differ fundamentally in their supervision granularity, leading to heterogeneous representations with uneven reliability across tasks and spatial locations. In this paper, we reformulate CDC as a selective information routing problem between heterogeneous task branches, where the key challenge is to determine when and where information should be transferred. To this end, we propose CoDeRNet, a unified framework that preserves task-specific representations while enabling bidirectional exchange of complementary information. CoDeRNet introduces a Confidence-Decomposed Routing (CoDeR) mechanism, which decomposes the routing condition into receiver-side need for support and sender-side reliability cues to guide selective transfer. Experiments on LEVIR-MCI and WHU-CDC show that CoDeRNet achieves strong overall CDC performance, particularly improving caption generation while preserving competitive change localization. Extensive analyses further show that the benefits arise not merely from joint learning or generic cross-task communication, but from explicitly modeling selective cross-task information routing. These results highlight the importance of conditional information transfer for unified CDC under heterogeneous supervision.

Disease diagnosis is a key application of large language model (LLM) agents in healthcare. However, existing medical agents are primarily trained and evaluated with static question-answering data, providing limited opportunities to learn active information acquisition, examination decision-making, and dynamic clinical interaction. To address this limitation, we present ClinVerse, an interactive, real-data-grounded multi-agent environment designed for training and evaluating medical diagnostic agents. Built on ClinPatient, a scalable dataset comprising 150k diagnostic cases derived from real-world electronic health records, ClinVerse supports coordinated interactions among six specialized clinical roles. Specifically, a doctor agent starts from an initial patient complaint, progressively gathers clinical evidence through patient interviews and examination requests, and ultimately produces a diagnosis. For each interaction trajectory, a rule-based scoring framework quantifies information acquisition, examination ordering, and diagnostic accuracy. These trajectory-level scores serve as reward signals for agent training while also enabling scalable evaluation. In experiments, we evaluate various general-purpose and medical-domain models and show that nine self-evolution methods across three categories consistently improve agents' diagnostic capabilities within ClinVerse. We further analyze the interplay among model capability, environment feedback, and self-evolution methods.

Document layout analysis (DLA) faces a long-standing dilemma: existing methods either capture rich local details at prohibitive computational cost (e.g., Transformer-based approaches with quadratic complexity) or achieve efficient global modeling but sacrifice fine-grained structural awareness (e.g., state-space models). This trade-off hinders deployment in resource-constrained scenarios. We present LayoutMamba, a hybrid framework that synergistically combines sparse-attention Transformers for localized feature extraction (O(n) complexity) and the recurrent state-space mechanism of Mamba for dilution-free long-range dependency modeling across the 2D document canvas. Our two-stage paradigm first uses a lightweight Transformer encoder to hierarchically distill salient local semantics; second, Mamba propagates global contextual dependencies without attention dilution, enabling precise, efficient, and scalable layout understanding. We further identify a critical yet overlooked issue in existing DLA benchmarks: the inherent low-overlap distribution bias of layout objects, which we mitigate through a novel Geometric Overlap Penalty Loss (GOPLoss), a dual-optimization objective that minimizes redundant bounding box overlaps and encourages geometrically consistent predictions. Extensive experiments on PubLayNet and D4LA show that LayoutMamba surpasses state-of-the-art AP on PubLayNet with 8x fewer parameters and maintains leading performance on D4LA, ranking top-3 among published methods, achieving an unprecedented balance between accuracy, efficiency, and scalability. Our work pioneers a cognition-inspired paradigm for document analysis, advancing practical deployment in edge computing and resource-limited environments.

Reusing pretrained medical-imaging models offers an alternative to training from scratch. A model trained for the same task may perform poorly at a new hospital because imaging protocols and preprocessing differ. Effective adaptation depends on the model architecture, domain shift, and available labels and compute. Adaptation can improve scores on a small labeled set yet reduce performance on unseen data. We present MedAdaptAgent, an automatic harness that turns a supplied model, target data, and a free-text request into a new-site adaptation workflow. Its LLM-driven Adaptation Planner uses model-site evidence to choose compatible experiments and revise subsequent adjustments. The harness requires evidence of improvement over the pretrained model, then delivers the selected model as a complete, runnable package. Evaluation spans 26 segmentation and classification transfers across architectures and compute settings, with segmentation annotation budgets ranging from zero-shot transfer to few-shot adaptation. With 20 local annotations, mean segmentation Dice rises from 0.483 to 0.774 across 10 transfers, a 60.1% relative gain, with no harmful replacements. Mean classification AUROC rises from 0.810 to 0.837 across 13 transfers, a 3.4% relative gain. Comparing adapted models with the pretrained model reduces harmful classification replacements by 30.8% versus selecting the highest-scoring model on the available labels, using identical candidates. Seven public-model deployments demonstrate adaptation and reproducible package delivery across contrast and modality shifts. A CT-to-MRI study extends the evaluation to four LLM planners, including two open-weight models. We release the code on GitHub.

Reasoning language models generate thousands of tokens per answer, and generation speed is limited by memory bandwidth, so storing weights in two bits instead of bfloat16 can potentially make inference 8$\times$ cheaper. Two-bit methods are usually judged by perplexity and multiple-choice question-answering accuracy, both of which say little about whether a model can still reason through more difficult problems. We present ThinkBit, a strictly scalar two-bit framework built to preserve reasoning, in which every weight is an integer codeword times a bfloat16 scale shared by a group of weights. We first use a sensitivity metric to assign each layer a ternary, 2-bit, or 3-bit tier to allocate bitwidths optimally among layers. This is followed by a layerwise Gumbel-softmax-based scalar quantization algorithm that searches for optimal codewords block by block. Since a layerwise scheme does not capture how quantized layers interact with each other, we recalibrate scales, with the codewords frozen, using a knowledge-distillation-based loss against the teacher model. We then perform a short quantization-aware training (QAT) stage that lets the model mimic the base model's distribution by reassigning codewords. Finally, a paired reasoning test on training problems selects the final checkpoint between the recalibration stage and the QAT stage. On Qwen3-1.7B at matched size, our recalibrated checkpoint scores 46.7 on MATH-500 against 30.0 for EfficientQAT trained on the same data, and 73.0 after QAT on reasoning traces; at 0.52 GB it is smaller than every published 2-bit GGUF of the model yet more accurate on MATH-500. We show that this framework scales and up to 14B, a 3.74 GB model scores 94.8 on MATH-500, against 97.5 for the 29.5 GB bfloat16 model.

A numerical claim generated by a financial analyst agent can match a number in a company filing but refer to the wrong entity, concept, period, unit or scope. A check based on reference answers can mark such an answer as wrong but does not explain why it fails. In this paper, we present NumTrace, a new benchmark that audits the provenance of each numerical claim made by financial analyst agents: its source and financial semantics. NumTrace currently has 540 questions over 2,163 SEC EDGAR documents. To the best of our knowledge, it is the first numerical benchmark about who holds a company's shares (Form 13F) and what its executives are owed (Item 402(i)). On a frozen snapshot of these documents, we build the reference answers according to accounting rules. NumTrace then gives each claim three separate verdicts: value, binding and support. By retaining separate value and binding verdicts, NumTrace detects right number, wrong account (RNWA) errors: a claim has an RNWA error when it passes the value check but fails the binding check. The RNWA rate quantifies the gap between value-only and joint value-and-binding pass rates. Finally, we audit three agents, Claude Opus 5.5, GPT-6 Astra and DeepSeek-V4.1-Flash. RNWA errors make up 67.5-81.8% of each agent's failing claims. In particular, on 13F co-filing group questions, about 91% of claims pass the value check, but fewer than half pass the binding check.