Miracle-dz/UniFAR-FTUs huggingface dataset:
No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML

On-device ML introduces new security challenges: DNN models become white-box accessible to device users. Based on white-box information, adversaries can conduct effective model stealing (MS) and membership inference attack (MIA). Using Trusted Execution Environments (TEEs) to shield on-device DNN models aims to downgrade (easy) white-box attacks to (harder) black-box attacks. However, one major shortcoming is the sharply increased latency (up to 50X). To accelerate TEE-shield DNN computation with GPUs, researchers proposed several model partition techniques. These solutions, referred to as TEE-Shielded DNN Partition (TSDP), partition a DNN model into two parts, offloading the privacy-insensitive part to the GPU while shielding the privacy-sensitive part within the TEE. This paper benchmarks existing TSDP solutions using both MS and MIA across a variety of DNN models, datasets, and metrics. We show important findings that existing TSDP solutions are vulnerable to privacy-stealing attacks and are not as safe as commonly believed. We also unveil the inherent difficulty in deciding optimal DNN partition configurations (i.e., the highest security with minimal utility cost) for present TSDP solutions. The experiments show that such ``sweet spot'' configurations vary across datasets and models. Based on lessons harvested from the experiments, we present TEESlice, a novel TSDP method that defends against MS and MIA during DNN inference. TEESlice follows a partition-before-training strategy, which allows for accurate separation between privacy-related weights from public weights. TEESlice delivers the same security protection as shielding the entire DNN model inside TEE (the ``upper-bound'' security guarantees) with over 10X less overhead (in both experimental and real-world environments) than prior TSDP solutions and no accuracy loss.
Cosmology from LOFAR Two-metre Sky Survey Data Release 2: Angular clustering of radio sources

Covering $\sim 5600\, \deg ^2$ to rms sensitivities of ∼70 −100 μJy beam−1, the LOFAR Two-metre Sky Survey Data Release 2 (LoTSS-DR2) provides the largest low-frequency (∼150 MHz) radio catalogue to date, making it an excellent tool for large-area radio cosmology studies. In this work, we use LoTSS-DR2 sources to investigate the angular two-point correlation function of galaxies within the survey. We discuss systematics in the data and an improved methodology for generating random catalogues, compared to that used for LoTSS-DR1, before presenting the angular clustering for ∼900 000 sources ≥1.5 mJy and a peak signal-to-noise ≥7.5 across ∼80 % of the observed area. Using the clustering we infer the bias assuming two evolutionary models. When fitting angular scales of $0.5 \le \theta <5\, \deg$, using a linear bias model, we find LoTSS-DR2 sources are biased tracers of the underlying matter, with a bias of $b_{C}= 2.14^{+0.22}_{-0.20}$ (assuming constant bias) and $b_{E}(z=0)= 1.79^{+0.15}_{-0.14}$ (for an evolving model, inversely proportional to the growth factor), corresponding to $b_E= 2.81^{+0.24}_{-0.22}$ at the median redshift of our sample, assuming the LoTSS Deep Fields redshift distribution is representative of our data. This reduces to $b_{C}= 2.02^{+0.17}_{-0.16}$ and $b_{E}(z=0)= 1.67^{+0.12}_{-0.12}$ when allowing preferential redshift distributions from the Deep Fields to model our data. Whilst the clustering amplitude is slightly lower than LoTSS-DR1 (≥2 mJy), our study benefits from larger samples and improved redshift estimates.
Robust Adaptive Compensation of External Disturbances for Multi-Channel Linear Systems

This paper proposes a new algorithm for compensating external disturbances for class of multi-channel linear systems. The solution to this problem is based on the use of the internal model principle and the extended error adaptation algorithm. It is assumed that the disturbance is the output of an autonomous linear generator with unknown parameters. At the first stage, a full-order observer with unknown input signals (Unknown Input Observer - UIO) is synthesized to solve the problem of estimating the state vector of this plant. Then a new observer of external disturbance is formed on the basis of state vector estimations. At the last stage, based on the new observer's estimations, a system with an extended state vector is formed for which a regulator providing compensation of disturbance is constructed. The performance of the obtained results is confirmed using computer simulation in MATLAB Simulink.
TOWARDS AN INTEGRATED HISTORICAL BIOGEOGRAPHY OF THE NEOTROPICAL LOWLAND AVIFAUNA: COMBINING DIVER-SIFICATION ANALYSIS AND LANDSCAPE EVOLUTION

– The importance of landscape evolution as driver of biotic diversification in the Neotropical low-lands has long been appreciated, but no clear common mechanisms have been revealed. We suggest that three main issues have collaborated to this: (i) poor understanding of earth history; (ii) problems with testing diversification hypotheses; and (iii) poor representation of diversity by current taxonomy. Thus, in order to study diversification patterns, some basic steps need to be observed: (i) the units of biodiversity need to be properly recognized through taxonomic reviews, with good geographic sampling; (ii) diversification patterns need to be carefully confronted to new information on landscape history and (iii) hypotheses need to be evaluated against direct and unambiguous predictions from alternative historical scenarios. Here we show how studies
Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance

Through Ecological Momentary Assessment (EMA) studies, a number of time-series data is collected across multiple individuals, continuously monitoring various items of emotional behavior. Such complex data is commonly analyzed in an individual level, using personalized models. However, it is believed that additional information of similar individuals is likely to enhance these models leading to better individuals' description. Thus, clustering is investigated with an aim to group together the most similar individuals, and subsequently use this information in group-based models in order to improve individuals' predictive performance. More specifically, two model-based clustering approaches are examined, where the first is using model-extracted parameters of personalized models, whereas the second is optimized on the model-based forecasting performance. Both methods are then analyzed using intrinsic clustering evaluation measures (e.g. Silhouette coefficients) as well as the performance of a downstream forecasting scheme, where each forecasting group-model is devoted to describe all individuals belonging to one cluster. Among these, clustering based on performance shows the best results, in terms of all examined evaluation measures. As another level of evaluation, those group-models' performance is compared to three baseline scenarios, the personalized, the all-in-one group and the random group-based concept. According to this comparison, the superiority of clustering-based methods is again confirmed, indicating that the utilization of group-based information could be effectively enhance the overall performance of all individuals' data.
Variational quantum eigensolver for closed-shell molecules with non-bosonic corrections.

The realization of quantum advantage with noisy-intermediate-scale quantum (NISQ) machines has become one of the major challenges in computational sciences. Maintaining coherence of a physical system with more than ten qubits is a critical challenge that motivates research on compact system representations to reduce algorithm complexity. Toward this end, the variational quantum eigensolver (VQE) used to perform quantum simulations is considered to be one of the most promising algorithms for quantum chemistry in the NISQ era. We investigate reduced mapping of one spatial orbital to a single qubit to analyze the ground state energy in a way that the Pauli operators of qubits are mapped to the creation/annihilation of singlet pairs of electrons. To include the effect of non-bosonic (or non-paired) excitations, we introduce a simple correction scheme in the electron correlation model approximated by the geometrical mean of the bosonic (or paired) terms. Employing it in a VQE algorithm, we assess ground state energies of H2O, N2, and Li2O in good agreement with full configuration interaction (FCI) models respectively, using only 6, 8, and 12 qubits with quantum gate depths proportional to the squares of the qubit counts. With the adopted seniority-zero approximation that uses only one half of the qubit counts of a conventional VQE algorithm, we find that our non-bosonic correction method reaches reliable quantum chemistry simulations at least for the tested systems.
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality

Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar generalization performance to that of the full dataset. However, current dataset distillation techniques fall short, showing a notable performance gap when compared to training on the original data. In this work, we are the first to argue that using just one synthetic subset for distillation will not yield optimal generalization performance. This is because the training dynamics of deep networks drastically change during the training. Hence, multiple synthetic subsets are required to capture the training dynamics at different phases of training. To address this issue, we propose Progressive Dataset Distillation (PDD). PDD synthesizes multiple small sets of synthetic images, each conditioned on the previous sets, and trains the model on the cumulative union of these subsets without requiring additional training time. Our extensive experiments show that PDD can effectively improve the performance of existing dataset distillation methods by up to 4.3%. In addition, our method for the first time enable generating considerably larger synthetic datasets.
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices

With the increasing extent of malware attacks in the present day along with the difficulty in detecting modern malware, it is necessary to evaluate the effectiveness and performance of Deep Neural Networks (DNNs) for malware classification. Multiple DNN architectures can be designed and trained to detect and classify malware binaries. Results demonstrate the potential of DNNs in accurately classifying malware with high accuracy rates observed across different malware types. Additionally, the feasibility of deploying these DNN models on edge devices to enable real-time classification, particularly in resource-constrained scenarios proves to be integral to large IoT systems. By optimizing model architectures and leveraging edge computing capabilities, the proposed methodologies achieve efficient performance even with limited resources. This study contributes to advancing malware detection techniques and emphasizes the significance of integrating cybersecurity measures for the early detection of malware and further preventing the adverse effects caused by such attacks. Optimal considerations regarding the distribution of security tasks to edge devices are addressed to ensure that the integrity and availability of large scale IoT systems are not compromised due to malware attacks, advocating for a more resilient and secure digital ecosystem.
Patterns in taxonomic and functional diversity of lake phytoplankton

Summary 1. Patterns in phytoplankton diversity in lakes and their relationships with environmental gradients have been traditionally based on taxonomic analyses and indices, even though measures of functional diversity (FD) might be expected to be more responsive to such gradients. 2. We assessed the influence of water column physical structure, and other components of the overall environment, on lake phytoplankton diversity using two taxonomically based indices [species richness (S) and the Shannon index (H’)] and a FD index, to determine whether these different measures respond in similar ways to habitat structure. The study encompassed 45 lakes in Eastern Canada, within two lake districts [the Eastern Townships Region (ETR) and Laurentians Region (LR)] that vary in geology and landscape and in lake morphometry and chemistry. 3. Across all lakes, S and H’ were higher in lakes having greater vertical temperature heterogeneity and higher susceptibility to wind mixing. In addition, H’ declined with total phosphorus concentration. FD was only related to maximum lake depth, a variable that integrates many other habitat features. 4. Further insight into the factors affecting phytoplankton diversity was obtained by contrasting the two regions. The taxonomically based diversity measures differed little between the regions, while FD was higher in the ETR where more trait variants were present and more evenly distributed amongst species. Whereas factors driving S did not differ between the regions, we found region-dependent patterns in the relationships of H’ and FD with maximum lake depth: both indices decreased with maximum depth in the region with lakes more exposed to wind (ETR) but increased in the more hilly landscape where lakes are more sheltered from wind mixing (LR). 5. Our study demonstrates that, for phytoplankton communities, a FD index can show simpler and stronger responses to environmental drivers than a taxonomically based index, while shedding further light onto the functional traits that are important in particular lake categories.
Housing, Financial and Capital Taxation Policies to Ensure Robust Growth in Sweden

Extensive structural reforms since the early 1990s have strengthened the resilience of the Swedish economy to shocks. However, more needs to be done to better manage near-term risks and ensure that growth remains sustainable in the longer run. Reforming the housing market would reduce the risks associated with high house prices, ensure adequate residential investment and improve labour mobility and well-being. Clarifying the division of responsibilities in financial regulation and improving the macroprudential toolkit would reduce the risks to stability and the contingent fiscal liabilities arising from a large, concentrated banking system. Better aligning the taxation of different types of assets would make taxation more neutral. Marche du logement, secteur financier et fiscalite du capital: conduire des politiques propres a garantir une croissance vigoureuse en Suede Les reformes structurelles de grande envergure conduites depuis le debut des annees 90 ont renforce la resistance de l’economie suedoise aux crises. Cependant, il faut aller plus loin encore pour mieux gerer les risques a court terme et faire en sorte que la croissance persiste dans la duree. Une reforme du marche de l’immobilier faciliterait un atterrissage en douceur des prix des logements, une hausse de l’investissement residentiel et une amelioration de la mobilite de la main-d’oeuvre et du bien-etre. Preciser la repartition des responsabilites dans la reglementation financiere et ameliorer la panoplie d’instruments macroprudentiels permettraient d’apaiser les menaces qui pesent sur la stabilite et de reduire les engagements budgetaires eventuels lies a l’existence d’un systeme bancaire vaste et concentre. Une harmonisation plus poussee de l’imposition des differentes categories d’actifs rendrait la fiscalite plus neutre.
Design of JiuTian Intelligent Network Simulation Platform

This paper introduced the JiuTian Intelligent Network Simulation Platform, which can provide wireless communication simulation data services for the Open Innovation Platform. The platform contains a series of scalable simulator functionalities, offering open services that enable users to use reinforcement learning algorithms for model training and inference based on simulation environments and data. Additionally, it allows users to address optimization tasks in different scenarios by uploading and updating parameter configurations. The platform and its open services were primarily introduced from the perspectives of background, overall architecture, simulator, business scenarios, and future directions.
An Improved Composition Theorem of a Universal Relation and Most Functions via Effective Restriction

Recently, Ivan Mihajlin and Alexander Smal proved a composition theorem of a universal relation and some function via so called xor composition, that is there exists some function $f:\{0,1\}^n \rightarrow \{0,1\}$ such that $\textsf{CC}(\text{U}_n \diamond \text{KW}_f) \geq 1.5n-o(n)$ where $\textsf{CC}$ denotes the communication complexity of the problem. In this paper, we significantly improve their result and present an asymptotically tight and much more general composition theorem of a universal relation and most functions, that is for most functions $f:\{0,1\}^n \rightarrow \{0,1\}$ we have $\textsf{CC}(\text{U}_m \diamond \text{KW}_f) \geq m+ n -O(\sqrt{m})$ when $m=\omega(\log^2 n),n =\omega(\sqrt{m})$. This is done by a direct proof of composition theorem of a universal relation and a multiplexor in the partially half-duplex model avoiding the xor composition. And the proof works even when the multiplexor only contains a few functions. One crucial ingredient in our proof involves a combinatorial problem of constructing a tree of many leaves and every leaf contains a non-overlapping set of functions. For each leaf, there is a set of inputs such that every function in the leaf takes the same value, that is all functions are restricted. We show how to choose a set of good inputs to effectively restrict these functions to force that the number of functions in each leaf is as small as possible while maintaining the total number of functions in all leaves. This results in a large number of leaves.
Effective Hamiltonian of the k=+1 FRW model from the K-Quantization method in LQC

In the effective theory of loop quantum cosmology LQC, the influence of the holonomy correction (with $\overline{\mu}$-scheme) on the homogeneous and the inhomogeneous cosmological models have been extensively studied in the case of flat space. In this paper,using the K-Quantization method and the $\overline{\mu}$-scheme,in the same framework of LQC,we construct the Hamiltonian constraint operator of the closed FRW model (k=+1), we show that the effective semi-classical limit gives the same holonomy correction expression that was already used in the case of a flat space. We also derive the modified Friedman equation. The numerical investigation is performed to plot the effective evolution of the massless scalar field in term of the volume,which indicate a minimum volume at the bounce and also a suitable infrared behavior.The condition that we apply to constrain the value of $\overline{\mu}$ in the semi-classical limit is consistent with the numerical results.
Syntax Error-Free and Generalizable Tool Use for LLMs via Finite-State Decoding

Large language models (LLMs) have shown promising capabilities in using external tools to solve complex problems. However, existing approaches either involve fine-tuning on tool demonstrations, which do not generalize to new tools without additional training, or providing tool documentation in context, limiting the number of tools. Both approaches often generate syntactically invalid tool calls. In this paper, we propose ToolDec, a finite-state machine-guided decoding algorithm for tool-augmented LLMs. ToolDec eliminates tool-related errors for any tool-augmented LLMs by ensuring valid tool names and type-conforming arguments. Furthermore, ToolDec enables LLM to effectively select tools using only the information contained in their names, with no need for fine-tuning or in-context documentation. We evaluated multiple prior methods and their ToolDec-enhanced versions on a variety of tasks involving tools like math functions, knowledge graph relations, and complex real-world RESTful APIs. Our experiments show that ToolDec reduces syntactic errors to zero, consequently achieving significantly better performance and as much as a 2x speedup. We also show that ToolDec achieves superior generalization performance on unseen tools, performing up to 8x better than the baselines.
On the Role of Font Formats in Building Efficient Web Applications

The success of a web application is closely linked to its performance, which positively impacts user satisfaction and contributes to energy-saving efforts. Among the various optimization techniques, one specific subject focuses on improving the utilization of web fonts. This study investigates the impact of different font formats on client-side resource consumption, such as CPU, memory, load time, and energy. In a controlled experiment, we evaluate performance metrics using the four font formats: OTF, TTF, WOFF, and WOFF2. The results of the study show that there are significant differences between all pair-wise format comparisons regarding all performance metrics. Overall, WOFF2 performs best, except in terms of memory allocation. Through the study and examination of literature, this research contributes (1) an overview of methodologies to enhance web performance through font utilization, (2) a specific exploration of the four prevalent font formats in an experimental setup, and (3) practical recommendations for scientific professionals and practitioners.
Adapting the adapters for code-switching in multilingual ASR

Recently, large pre-trained multilingual speech models have shown potential in scaling Automatic Speech Recognition (ASR) to many low-resource languages. Some of these models employ language adapters in their formulation, which helps to improve monolingual performance and avoids some of the drawbacks of multi-lingual modeling on resource-rich languages. However, this formulation restricts the usability of these models on code-switched speech, where two languages are mixed together in the same utterance. In this work, we propose ways to effectively fine-tune such models on code-switched speech, by assimilating information from both language adapters at each language adaptation point in the network. We also model code-switching as a sequence of latent binary sequences that can be used to guide the flow of information from each language adapter at the frame level. The proposed approaches are evaluated on three code-switched datasets encompassing Arabic, Mandarin, and Hindi languages paired with English, showing consistent improvements in code-switching performance with at least 10\% absolute reduction in CER across all test sets.
"Because Some Sighted People, They Don't Know What the Heck You're Talking About: " A Study of Blind TikTokers' Infrastructuring Work to Build Independence

There has been extensive research on the experiences of individuals with visual impairments on text- and image-based social media platforms, such as Facebook and Twitter. However, little is known about the experiences of visually impaired users on short-video platforms like TikTok. To bridge this gap, we conducted an interview study with 30 BlindTokers (the nickname of blind TikTokers). Our study aimed to explore the various activities of BlindTokers on TikTok, including everyday entertainment, professional development, and community engagement. The widespread usage of TikTok among participants demonstrated that they considered TikTok and its associated experiences as the infrastructure for their activities. Additionally, participants reported experiencing breakdowns in this infrastructure due to accessibility issues. They had to carry out infrastructuring work to resolve the breakdowns. Blind users' various practices on TikTok also foregrounded their perceptions of independence. We then discussed blind users' nuanced understanding of the TikTok-mediated independence; we also critically examined BlindTokers' infrastructuring work for such independence.