Miracle-dz/UniFAR-FTUs huggingface dataset:
A Note on Output Length of One-Way State Generators

We study output length of one-way state generators (OWSGs) and their weaker variants. - Standard OWSGs. Recently, Cavalar et al. (arXiv:2312.08363) give OWSGs with $m$-qubit outputs for any $m=\omega(\log \lambda)$, where $\lambda$ is the security parameter, and conjecture that there do not exist OWSGs with $O(\log \log \lambda)$-qubit outputs. We prove their conjecture in a stronger manner by showing that there do not exist OWSGs with $O(\log \lambda)$-qubit outputs. This means that their construction is optimal in terms of output length. - Constant-advantage OWSGs. Let $\epsilon$-OWSGs be a parameterized variant of OWSGs where a quantum polynomial-time adversary's advantage is at most $\epsilon$. For any constant $\epsilon>0$, we construct $\epsilon$-OWSGs with $O(\log \log \lambda)$-qubit outputs assuming the existence of subexponentially secure OWFs. We show that this is almost tight by proving that there do not exist $O(1)$-OWSGs with $(\log \log \lambda)/2+O(1)$-qubit outputs. - Weak OWSGs. We refer to $(1-1/\mathsf{poly}(\lambda))$-OWSGs as weak OWSGs. We construct weak OWSGs with $m$-qubit outputs for any $m=\omega(1)$ assuming the existence of exponentially secure injective OWFs with linear expansion. We show that this is tight by proving that there do not exist weak OWSGs with $O(1)$-qubit outputs.
Two Steps Forward and One Step Back: The Right to Opt-out of Sale under CPRA

The California Privacy Rights Act (CPRA) was a ballot initiative that revised the California Consumer Privacy Act (CCPA). Although often framed as expanding and enhancing privacy rights, a close analysis of textual revisions -- both changes from the earlier law and changes from earlier drafts of the CPRA guidelines -- suggest that the reality might be more nuanced. In this work, we identify three textual revisions that have potential to negatively impact the right to opt-out of sale under CPRA and evaluate the effect of these textual revisions using (1) a large-scale longitudinal measurement study of 25,000 websites over twelve months and (2) an experimental user study with 775 participants recruited through Prolific. We find that all revisions negatively impacted the usability, scope, and visibility of the right to opt-out of sale. Our results provide the first comprehensive evaluation of the impact of CPRA on Internet privacy. They also emphasize the importance of continued evaluation of legal requirements as guidelines and case law evolve after a law goes into effect.
Knowledge Distillation of LLM for Automatic Scoring of Science Education Assessments

This study proposes a method for knowledge distillation (KD) of fine-tuned Large Language Models (LLMs) into smaller, more efficient, and accurate neural networks. We specifically target the challenge of deploying these models on resource-constrained devices. Our methodology involves training the smaller student model (Neural Network) using the prediction probabilities (as soft labels) of the LLM, which serves as a teacher model. This is achieved through a specialized loss function tailored to learn from the LLM's output probabilities, ensuring that the student model closely mimics the teacher's performance. To validate the performance of the KD approach, we utilized a large dataset, 7T, containing 6,684 student-written responses to science questions and three mathematical reasoning datasets with student-written responses graded by human experts. We compared accuracy with state-of-the-art (SOTA) distilled models, TinyBERT, and artificial neural network (ANN) models. Results have shown that the KD approach has 1% and 4% higher scoring accuracy than ANN and TinyBERT and comparable accuracy to the teacher model. Furthermore, the student model size is 0.02M, 10,000 times smaller in parameters and x10 faster in inferencing than the teacher model and TinyBERT, respectively. The significance of this research lies in its potential to make advanced AI technologies accessible in typical educational settings, particularly for automatic scoring.
Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning

Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.
Crafting crystalline topological insulators via accidental mode degeneracies

Crystalline topological insulators have recently become a powerful platform for realizing photonic topological states from microwaves to the visible. Appropriate geometric symmetries of the lattice are at the core of their functionality. Here we put forward an alternative approach to craft those systems by designing the internal symmetries of the Hamiltonian via accidental mode degeneracies. We illustrate our approach constructing ananalog of breathing honeycomb lattice using simpler lattice geometry and six times less meta-atoms, reveal edge and corner states and calculate the relevant topological invariants.
Semantic-aware SAM for Point-Prompted Instance Segmentation

Single-point annotation in visual tasks, with the goal of minimizing labelling costs, is becoming increasingly prominent in research. Recently, visual foundation models, such as Segment Anything (SAM), have gained widespread usage due to their robust zero-shot capabilities and exceptional annotation performance. However, SAM's class-agnostic output and high confidence in local segmentation introduce 'semantic ambiguity', posing a challenge for precise category-specific segmentation. In this paper, we introduce a cost-effective category-specific segmenter using SAM. To tackle this challenge, we have devised a Semantic-Aware Instance Segmentation Network (SAPNet) that integrates Multiple Instance Learning (MIL) with matching capability and SAM with point prompts. SAPNet strategically selects the most representative mask proposals generated by SAM to supervise segmentation, with a specific focus on object category information. Moreover, we introduce the Point Distance Guidance and Box Mining Strategy to mitigate inherent challenges: 'group' and 'local' issues in weakly supervised segmentation. These strategies serve to further enhance the overall segmentation performance. The experimental results on Pascal VOC and COCO demonstrate the promising performance of our proposed SAPNet, emphasizing its semantic matching capabilities and its potential to advance point-prompted instance segmentation. The code will be made publicly available.
On the large deviation rate function for marked sparse random graphs

We consider (annealed) large deviation principles for component empirical measures of several families of marked sparse random graphs, including (i) uniform graphs on $n$ vertices with a fixed degree distribution; (ii) uniform graphs on $n$ vertices with a fixed number of edges; (iii) Erd\H{o}s-R\'enyi $G(n, c/n)$ random graphs. Assuming that edge and vertex marks are independent, identically distributed, and take values in a finite state space, we show that the large deviation rate function admits a concise representation as a sum of relative entropies that quantify the cost of deviation of a probability measure on marked rooted graphs from certain auxiliary independent and conditionally independent versions. The proof exploits unimodularity, the consequent mass transport principle, and random tree labelings to express certain combinatorial quantities as expectations with respect to size-biased distributions, and to identify unimodular extensions with suitable conditional laws. We also illustrate how this representation can be used to establish Gibbs conditioning principles that provide insight into the structure of marked random graphs conditioned on a rare event. Additional motivation for this work arises from the fact that such a representation is also useful for characterizing the annealed pressure of statistical physics models with general spins, and large deviations of evolving interacting particle systems on sparse random graphs.
Leveraging Public Representations for Private Transfer Learning

Motivated by the recent empirical success of incorporating public data into differentially private learning, we theoretically investigate how a shared representation learned from public data can improve private learning. We explore two common scenarios of transfer learning for linear regression, both of which assume the public and private tasks (regression vectors) share a low-rank subspace in a high-dimensional space. In the first single-task transfer scenario, the goal is to learn a single model shared across all users, each corresponding to a row in a dataset. We provide matching upper and lower bounds showing that our algorithm achieves the optimal excess risk within a natural class of algorithms that search for the linear model within the given subspace estimate. In the second scenario of multitask model personalization, we show that with sufficient public data, users can avoid private coordination, as purely local learning within the given subspace achieves the same utility. Taken together, our results help to characterize the benefits of public data across common regimes of private transfer learning.
On orbital stability of solitons for 2D Maxwell-Lorentz equations

We prove the orbital stability of soliton solutions for 2D Maxwell--Lorentz system with extended charged particle. The solitons corresponds to the uniform motion and rotation of the particle. We reduce the corresponding Hamilton system by the canonical transformation via transition to a comoving frame. The solitons are the critical points of the reduced Hamiltonian. The key point of the proof is a lower bound for the Hamiltonian.
Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks

In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.
Linear quadratic optimal control turnpike in finite and infinite dimension: two-term expansion of the value function

In this paper, we consider a linear quadratic (LQ) optimal control problem in both finite and infinite dimensions. We derive an asymptotic expansion of the value function as the fixed time horizon T tends to infinity. The leading term in this expansion, proportional to T, corresponds to the optimal value attained through the classical turnpike theory in the associated static problem. The remaining terms are associated with optimal stabilization problems towards the turnpike.
On the equivalence between the effective adjunction conjectures of Prokhorov-Shokurov and of Li

Prokhorov and Shokurov introduced the famous effective adjunction conjecture, also known as the effective base-point-freeness conjecture. This conjecture asserts that the moduli component of an lc-trivial fibration is effectively base-point-free. Li proposed a variation of this conjecture, which is known as the $\Gamma$-effective adjunction conjecture, and proved that a weaker version of his conjecture is implied by the original Prokhorov-Shokurov conjecture. In this paper, we establish the equivalence of Prokhorov-Shokurov's and Li's effective adjunction conjectures. The key to our proof is the formulation of a uniform rational polytope for canonical bundle formulas, which relies on recent developments in the minimal model program theory of algebraically integrable foliations by Ambro-Cascini-Shokurov-Spicer and Chen-Han-Liu-Xie.
Strichartz estimates for the Schrödinger equation on products of odd-dimensional spheres

We generalize the framework developed in [25] to prove certain Strichartz estimates which are scale-invariant up to an ε-loss on any product of odd-dimensional spheres. Some partial results toward such Strichartz estimates on a general compact globally symmetric space are also given, including a kernel estimate sharp up to an ε-loss near rational times and near corners of a maximal torus.
On a conjecture of R. M. Murty and V. K. Murty II

Let $\omega^*(n)$ be the number of primes $p$ such that $p-1$ divides $n$. Assuming the Elliott--Halberstam Conjecture, we prove a conjecture posted by M. R. Murty and V. K. Murty in 2021 which states that $$\sum_{n\leqslant x}\omega^*(n)^2\sim 2\frac{\zeta(2)\zeta(3)}{\zeta(6)}x\log x, \quad \text{as} \quad x\rightarrow \infty. $$ The above sum was first investigated by Prachar in 1955. One of the key ingredients in our argument is the application of a sieve result on estimating various certain summations involving primes in arithmetic progressions, rather than a direct use of the Brun--Titchmarsh inequality which would not be applicable for our task.
CAMEL: Co-Designing AI Models and Embedded DRAMs for Efficient On-Device Learning

On-device learning allows AI models to adapt to user data, thereby enhancing service quality on edge platforms. However, training AI on resource-limited devices poses significant challenges due to the demanding computing workload and the substantial memory consumption and data access required by deep neural networks (DNNs). To address these issues, we propose utilizing embedded dynamic random-access memory (eDRAM) as the primary storage medium for transient training data. In comparison to static random-access memory (SRAM), eDRAM provides higher storage density and lower leakage power, resulting in reduced access cost and power leakage. Nevertheless, to maintain the integrity of the stored data, periodic power-hungry refresh operations could potentially degrade system performance. To minimize the occurrence of expensive eDRAM refresh operations, it is beneficial to shorten the lifetime of stored data during the training process. To achieve this, we adopt the principles of algorithm and hardware co-design, introducing a family of reversible DNN architectures that effectively decrease data lifetime and storage costs throughout training. Additionally, we present a highly efficient on-device training engine named \textit{CAMEL}, which leverages eDRAM as the primary on-chip memory. This engine enables efficient on-device training with significantly reduced memory usage and off-chip DRAM traffic while maintaining superior training accuracy. We evaluate our CAMEL system on multiple DNNs with different datasets, demonstrating a $2.5\times$ speedup of the training process and $2.8\times$ training energy savings than the other baseline hardware platforms.
Gila River Flow Needs Assessment

T Gila River is widely recognized for the habitat it provides for people and wildlife in Southwest New Mexico. Flow variability is the defining feature of the Gila River in New Mexico—creating a multi-aged riparian forest and floodplain wetlands that support rich bird diversity and provide habitat for numerous mammals. An array of aquatic habitats supports native fishes. Numerous federally protected species are found in the Cliff-Gila Valley: Southwestern willow flycatcher, spikedace, loach minnow, northern Mexican gartersnake and narrow-headed gartersnake. Western yellow-billed cuckoo is proposed for federal listing as threatened. The Gila is a rare example of a Southwest river with a natural flow pattern that sustains its high biodiversity. The Arizona Water Settlement Act of 2004 (AWSA) provides an opportunity to augment water supply needs in the Southwest New Mexico, authorizing diversion of an additional 14,000 acre-feet annually from the upper Gila River in exchange for Central Arizona Project water. The Act was accompanied by an appropriation to New Mexico which may be used either for “other water utilization alternatives to meet the water supply demands” of the region or a permanent river diversion and other associated facilities. The Gila River Flow Needs Assessment is intended to help water and natural resource managers effectively weigh the ecological impacts of a permanent diversion and adapt to climate change.
Operational and Performance Experience with uMI550 Digital PET-CT during Routine Quality Control Procedures

Abstract Introduction The quality control (QC) procedures for positron emission tomography (PET) scanners are covered by National Electrical Manufacturers Association and International Electrotechnical Commission. QC must be carried out at regular intervals according to the specifications of the scanner manufacturer. Daily and weekly QC plays a valuable role in monitoring positron emission tomography (PET) scanner performance changes. This study shares operational and performance experience of QC procedures that do not require a radioactive Ge-68 source to perform daily QC and experience with fluorodeoxyglucose F18 ( 18 F-FDG) as a substitute for germanium-68/sodium-22 (Ge-68/Na-22) source for weekly QC. Method This study was performed on an uMI550 digital positron emission tomography-computed tomography (PET-CT) scanner. In this scanner daily QC checks system temperature and humidity, system count rate, data link status, and voltage. QC was performed at the console control, the position of the scanner table was in the home position pulled out from the gantry, and the room was closed during the quick QC. Weekly full QC check items include look-up table drift, energy drift, time-of-flight status, C-map status, temperature and humidity, and voltage. Weekly full QC was performed with a 18 F-FDG source in a rod phantom source. Results Over 200 daily QC tests without a radioactive source Ge-68 phantom and 50 full weekly QC tests using a 18 F-FDG rod phantom were performed with this scanner according to the manufacturer's instructions and a test report was generated. No daily QC errors or warnings were observed during this period. Conclusion The new approach for the daily PET QC does not expose operators to radiation. This translates into commercial and operational merits with consistent performance and results. Implications for Practice Reduction in radiation exposure to operating staff during QC procedure in PET-CT scanner.
Exploring the Latest Highlights in Medical Natural Language Processing across Multiple Languages: A Survey

Summary Objectives : This survey aims to provide an overview of the current state of biomedical and clinical Natural Language Processing (NLP) research and practice in Languages other than English (LoE). We pay special attention to data resources, language models, and popular NLP downstream tasks. Methods : We explore the literature on clinical and biomedical NLP from the years 2020-2022, focusing on the challenges of multilinguality and LoE. We query online databases and manually select relevant publications. We also use recent NLP review papers to identify the possible information lacunae. Results : Our work confirms the recent trend towards the use of transformer-based language models for a variety of NLP tasks in medical domains. In addition, there has been an increase in the availability of annotated datasets for clinical NLP in LoE, particularly in European languages such as Spanish, German and French. Common NLP tasks addressed in medical NLP research in LoE include information extraction, named entity recognition, normalization, linking, and negation detection. However, there is still a need for the development of annotated datasets and models specifically tailored to the unique characteristics and challenges of medical text in some of these languages, especially low-resources ones. Lastly, this survey highlights the progress of medical NLP in LoE, and helps at identifying opportunities for future research and development in this field.
Oleuropein in olive leaf, branch, and stem extracts: stability and biological activity in human cervical carcinoma and melanoma cells

Abstract Olive leaves as a main byproduct of olive oil and fruit industry are a valuable source of phytochemicals such as polyphenols, with multiple biomedical effects. Apart from leaves, olive branches and stems make up a significant amount of olive waste. It is well known that the drying process and long-term storage affect the stability and concentration of polyphenols present in raw materials. For that matter, two different means of storing olive waste, at room temperature and +4 °C, were compared by determining the content of the polyphenol oleuropein (OLE) in olive leaf, branch, and stem extracts (LE, BE, and SE) by HPLC-DAD method. Total phenols (TPC), o-diphenols (o-DPC), and total flavonoids (TFC) content in extracts were assessed by UV-Vis measurements. LE prepared from leaves stored at +4 °C had the highest OLE content, 30.7 mg g−1 of dry extract (DE). SE from stems stored at +4 °C was the richest in TPC and TFC (193 mg GAE/g DE and 82.9 mg CE/g DE, respectively), due to the higher purity of the extract. The biological activity of extracts was determined on cervical cancer (HeLa), melanoma (A375), metastatic melanoma (A375M) tumor cell lines, and on spontaneously immortalized cell line of keratinocytes (HaCaT), using the MTT assay. The data show that all extracts had a similar dose-dependent effect on cell viability in HeLa cells, while the effect of LE on melanoma A375 and A375M, and HaCaT cells was cell-line dependent.