Miracle-dz/UniFAR-FTUs huggingface dataset:
Cutting a Wire with Non-Maximally Entangled States

Distributed quantum computing supports combining the computational power of multiple quantum devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations via classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer qubits. The original circuit's outcome can be replicated by executing these subcircuits on separate devices and combining their results. However, the number of circuit executions required to achieve a fixed result accuracy with circuit cutting grows exponentially with the number of cuts, posing significant costs. In contrast, quantum teleportation allows the distribution of quantum computations without an exponential increase in circuit executions. Nevertheless, each teleportation requires a pre-shared pair of maximally entangled qubits for transmitting a quantum state, and non-maximally entangled qubits cannot be used for this purpose. Addressing this, our work explores utilizing non-maximally entangled qubit pairs in wire cutting, a specific form of circuit cutting, to mitigate the associated costs. The cost of this cutting procedure reduces with the increasing degree of entanglement in the pre-shared qubit pairs. We derive the optimal sampling overhead in this context and present a wire cutting technique employing pure non-maximally entangled states that achieves this optimal sampling overhead. Hence, this offers a continuum between existing wire cutting and quantum teleportation.
Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential

Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure-property relations in the system. In this work, we fit an atomic cluster expansion potential to a set of actively learned DFT training data spanning a wide configurational space. We demonstrate the ability of the potential to produce realistic amorphous structures and rationalize the formation of different morphologies of the turbostratic free carbon phase. Finally, we relate the materials stiffness to its composition and microstructure, finding a delicate dependence on Si-C bonds that contradicts commonly assumed relations to the free carbon phase.
Concept-aware Data Construction Improves In-context Learning of Language Models

Many recent language models (LMs) are capable of in-context learning (ICL), manifested in the LMs' ability to perform a new task solely from natural-language instruction. Previous work curating in-context learners assumes that ICL emerges from a vast over-parametrization or the scale of multi-task training. However, recent theoretical work attributes the ICL ability to concept-dependent training data and creates functional in-context learners even in small-scale, synthetic settings. In this work, we practically explore this newly identified axis of ICL quality. We propose Concept-aware Training (CoAT), a framework for constructing training scenarios that make it beneficial for the LM to learn to utilize the analogical reasoning concepts from demonstrations. We find that by using CoAT, pre-trained transformers can learn to better utilise new latent concepts from demonstrations and that such ability makes ICL more robust to the functional deficiencies of the previous models. Finally, we show that concept-aware in-context learning is more effective for a majority of new tasks when compared to traditional instruction tuning, resulting in a performance comparable to the previous in-context learners using magnitudes of more training data.
High Precision 16-bit Readout Gas Sensor Interface in 0.13μm CMOS

A high precision 16-bit readout circuit for gas sensor interface is realized in 0. 13μm CMOS technology. The front end signal conditioning circuit comprises a low power resistance-to-time period converter with subthreshold operation of transistors. The time-period proportional to the transducer output is digitized using a counter based frequency-to-digital conversion (FDC) technique. In order to handle the sensor signal with a dynamic range of 160dB, subranging technique is employed to provide feedback to front end circuit interfacing the sensor. While the 3 MSBs of 16-bit word restrict the input frequency to FDC between 1MHz and 10MHz, the 13-bit counter operates on this signal to provide a worst case resolution of 0.4%. The power consumption of the readout circuit is 366μW from a 1.2V supply.
Entropy-Maximizing Dynamics of Continuous Markets

By assuming the existence of the growth optimal portfolio (GOP), the stationarity of GOP-volatilities, and the maximization of relative entropy, the paper applies the benchmark approach to the modeling of the long-term dynamics of continuous markets. It reveals conservation laws, where the GOP is shown to follow a time-transformed squared Bessel process of dimension four. Moreover, it predicts the convergence of the averages of the GOP-volatilities with respect to the driving independent Brownian motions toward a common level.
Hepatic and Adipose Tissue Lipogenesis as Related to Age and Thyroid Status in the Rat

We have previously reported that, in the rat, chronic thyroxine (T4) treatment induced a transient adipose tissue hyperplasia and that, in preadipocytes cultures, lipogenesis as well as adipose conversion were enhanced by triiodothyronine. Therefore we looked for the possibility of a relationship between in vivo stimulation of adipose tissue lipogenesis and the stimulation of fat cell recruitment by thyroid hormones. Hepatic and adipose tissue de novo lipogenesis were estimated by the incorporation of 3H2O into lipids in rats of various ages made slightly hyperthyroid by daily injections of T4 (0.2 microgram/g/day) from birth. Hepatic and adipose tissue lipogenesis were increased at 3 and 6 weeks of age, no stimulation being observed when animals get older. 21 week-old animals were therefore acutely treated with 0.2 or 2 micrograms T4/g/day. In this case, only the high T4 dose was able to induce a consistent lipogenesis stimulation in liver and in retroperitoneal adipose tissue and failed to induce it in epididymal adipose tissue. These results pointed out that thyroid hormones can stimulate lipogenesis both in liver and adipose tissue. However, there is an age related fall in the sensitivity to thyroid hormones for lipogenesis stimulation, not only in the liver, but also and more pronounced in adipose tissue, in parallel to that observed in vivo for adipose differentiation; moreover, this decreased sensitivity seems to be accelerated by a long lasting hyperthyroidal state.
Lifelong Person Re-Identification with Backward-Compatibility

Lifelong person re-identification (LReID) assumes a practical scenario where the model is sequentially trained on continuously incoming datasets while alleviating the catastrophic forgetting in the old datasets. However, not only the training datasets but also the gallery images are incrementally accumulated, that requires a huge amount of computational complexity and storage space to extract the features at the inference phase. In this paper, we address the above mentioned problem by incorporating the backward-compatibility to LReID for the first time. We train the model using the continuously incoming datasets while maintaining the model's compatibility toward the previously trained old models without re-computing the features of the old gallery images. To this end, we devise the cross-model compatibility loss based on the contrastive learning with respect to the replay features across all the old datasets. Moreover, we also develop the knowledge consolidation method based on the part classification to learn the shared representation across different datasets for the backward-compatibility. We suggest a more practical methodology for performance evaluation as well where all the gallery and query images are considered together. Experimental results demonstrate that the proposed method achieves a significantly higher performance of the backward-compatibility compared with the existing methods. It is a promising tool for more practical scenarios of LReID.
Selection metrics for multihop cooperative relaying

Cooperative relaying enables nodes to actively cooperate to deliver packets to their destination. The best-select protocol (BSP) implements a type of cooperative relaying that generalizes single path routing with sets of nodes (relay-sets) replacing the concept of a single node relay. Thus, while in traditional single path routing, packets hop from node to node, in BSP, packets hop from relay-set to relay-set. Through the exchange of channel gain information between relaysets, the best node within a relay-set is selected to transmit the data packet on behalf of the entire relayset. The node selected depends on the metric used. Any metric that can be posed in a dynamic programming framework can be used. In this paper, performance gains from a number of selection metrics are investigated. Specific selection metrics include maximizing the probability of packet delivery, maximizing the minimum channel gain along the path, maximizing the throughput, minimizing end-to-end delay, minimizing the total power, and minimizing the total energy. It will be shown that BSP can achieve significant gains in all of these metrics, with the possible exception of maximizing the probability of packet delivery. The performance is investigated with several different propagation models and node densities.
Ultra-high endurance silicon photonic memory using vanadium dioxide

Silicon photonics arises as a viable solution to address the stringent resource demands of emergent technologies, such as neural networks. Within this framework, photonic memories are fundamental building blocks of photonic integrated circuits that have not yet found a standardized solution due to several trade-off among different metrics such as energy consumption, speed, footprint, or fabrication complexity, to name a few. In particular, a photonic memory exhibiting ultra-high endurance performance (>10^6 cycles) has been elusive to date. Here, we report an ultra-high endurance silicon photonic memory using vanadium dioxide (VO_2) exhibiting a record cyclability of up to 10^7 cycles without degradation. Moreover, our memory features an ultra-compact footprint below 5 {\mu}m with potential for nanosecond and picojoule programming performance. Our silicon photonic memory could find application in emerging photonic applications demanding high number of memory updates such as photonic neural networks with in-situ training.
Geographical diversity in seasonality of major diarrhoeal pathogens in Bangladesh observed between 2010 and 2012

SUMMARY The study aimed to determine the geographical diversity in seasonality of major diarrhoeal pathogens among 21 138 patients enrolled between 2010 and 2012 in two urban and two rural sites in Bangladesh under the surveillance system of the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b). Distinct patterns in seasonality were found for rotavirus diarrhoea which peaked in winter across the sites (December and January) and dipped during the rainy season (May) in urban Dhaka, August in Mirpur and July in Matlab, equated by time-series analysis using quasi-Poisson regression model. Significant seasonality for shigellosis was observed in Dhaka and rural Mirzapur. Cholera had robust seasonality in Dhaka and Matlab in the hot and rainy seasons. For enterotoxogenic Escherichia coli (ETEC) diarrhoea, clearly defined seasonality was observed in Dhaka (summer). Understanding the seasonality of such pathogens can improve case management with appropriate therapy, allowing policy-makers to identify periods of high disease burden.
ViTCN: Vision Transformer Contrastive Network For Reasoning

Machine learning models have achieved significant milestones in various domains, for example, computer vision models have an exceptional result in object recognition, and in natural language processing, where Large Language Models (LLM) like GPT can start a conversation with human-like proficiency. However, abstract reasoning remains a challenge for these models, Can AI really thinking like a human? still be a question yet to be answered. Raven Progressive Matrices (RPM) is a metric designed to assess human reasoning capabilities. It presents a series of eight images as a problem set, where the participant should try to discover the underlying rules among these images and select the most appropriate image from eight possible options that best completes the sequence. This task always be used to test human reasoning abilities and IQ. Zhang et al proposed a dataset called RAVEN which can be used to test Machine Learning model abstract reasoning ability. In this paper, we purposed Vision Transformer Contrastive Network which build on previous work with the Contrastive Perceptual Inference network (CoPiNet), which set a new benchmark for permutationinvariant models Raven Progressive Matrices by incorporating contrast effects from psychology, cognition, and education, and extends this foundation by leveraging the cutting-edge Vision Transformer architecture. This integration aims to further refine the machine ability to process and reason about spatial-temporal information from pixel-level inputs and global wise features on RAVEN dataset.
Polymer Concentration Regimes from Fractional Microrheology

In this work, a framework for deriving theoretical equations for mean squared displacement (MSD) and fractional Fokker-Planck (FFP) is developed for any arbitrary rheological model. The obtained general results are then specified for two fractional rheological models. To test our framework and bridge the gap between microrheology and fractional rheological models, the microrheology of polystyrene (PS) in tetrahydrofuran (THF) solutions at several polymer concentrations is measured. By comparing the experimental and theoretical MSDs, we find the fractional rheological parameters and demonstrate that the polymer concentration regimes can be distinguished using the fractional exponent and relaxation time data because of the existence of a distinct behavior in each regime. We suggest simple approximations for the critical overlap concentration and the shear viscosity of weakly subdiffusive solutions with small relaxation times. This work provides an alternative and more sensitive approach for distinguishing different polymer concentration regimes and measuring the critical overlap concentration and shear viscosity of polymeric solutions, which is useful when conventional rheological characterization methods are unreliable due to the volatility and low viscosity of the samples.
Issues in the design of multi-server file systems to cope with load skew

It is observed that a large number of operations in many environments are to the files that are mostly-read and rarely updated. Such operations can be served by any server without incurring a high coherency. Several dynamic load balancing algorithms of various degrees of complexity that achieve high reduction in load skew albeit reducing the server buffer hit probability are proposed. It is shown using real workload traces that server load skew can be significantly reduced using these policies, and that there is an inherent tradeoff between load balancing and server buffer hit probability. Some variations of the proposed buddy-based policies which limit the number of replications of buffer pages seem to provide a good compromise between the two factors. <<ETX>>
Denoising Task Difficulty-based Curriculum for Training Diffusion Models

Diffusion-based generative models have emerged as powerful tools in the realm of generative modeling. Despite extensive research on denoising across various timesteps and noise levels, a conflict persists regarding the relative difficulties of the denoising tasks. While various studies argue that lower timesteps present more challenging tasks, others contend that higher timesteps are more difficult. To address this conflict, our study undertakes a comprehensive examination of task difficulties, focusing on convergence behavior and changes in relative entropy between consecutive probability distributions across timesteps. Our observational study reveals that denoising at earlier timesteps poses challenges characterized by slower convergence and higher relative entropy, indicating increased task difficulty at these lower timesteps. Building on these observations, we introduce an easy-to-hard learning scheme, drawing from curriculum learning, to enhance the training process of diffusion models. By organizing timesteps or noise levels into clusters and training models with descending orders of difficulty, we facilitate an order-aware training regime, progressing from easier to harder denoising tasks, thereby deviating from the conventional approach of training diffusion models simultaneously across all timesteps. Our approach leads to improved performance and faster convergence by leveraging the benefits of curriculum learning, while maintaining orthogonality with existing improvements in diffusion training techniques. We validate these advantages through comprehensive experiments in image generation tasks, including unconditional, class-conditional, and text-to-image generation.
SuperME: Supervised and Mixture-to-Mixture Co-Learning for Speech Enhancement and Robust ASR

The current dominant approach for neural speech enhancement is based on supervised learning by using simulated training data. The trained models, however, often exhibit limited generalizability to real-recorded data. To address this, we investigate training models directly on real target-domain data, and propose two algorithms, mixture-to-mixture (M2M) training and a co-learning algorithm that improves M2M with the help of supervised algorithms. When paired close-talk and far-field mixtures are available for training, M2M realizes speech enhancement by training a deep neural network (DNN) to produce speech and noise estimates in a way such that they can be linearly filtered to reconstruct the close-talk and far-field mixtures. This way, the DNN can be trained directly on real mixtures, and can leverage close-talk mixtures as a weak supervision to enhance far-field mixtures. To improve M2M, we combine it with supervised approaches to co-train the DNN, where mini-batches of real close-talk and far-field mixture pairs and mini-batches of simulated mixture and clean speech pairs are alternately fed to the DNN, and the loss functions are respectively (a) the mixture reconstruction loss on the real close-talk and far-field mixtures and (b) the regular enhancement loss on the simulated clean speech and noise. We find that, this way, the DNN can learn from real and simulated data to achieve better generalization to real data. We name this algorithm SuperME, $\underline{super}$vised and $\underline{m}$ixture-to-mixtur$\underline{e}$ co-learning. Evaluation results on the CHiME-4 dataset show its effectiveness and potential.
Meta Operator for Complex Query Answering on Knowledge Graphs

Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (CQA) models are proposed to directly learn from the query-answer samples to avoid the direct traversal of incomplete graph data. Existing works formulate the training of complex query answering models as multi-task learning and require a large number of training samples. In this work, we explore the compositional structure of complex queries and argue that the different logical operator types, rather than the different complex query types, are the key to improving generalizability. Accordingly, we propose a meta-learning algorithm to learn the meta-operators with limited data and adapt them to different instances of operators under various complex queries. Empirical results show that learning meta-operators is more effective than learning original CQA or meta-CQA models.
Sun-Jupiter-Saturn System may exist: A verified computation of quasiperiodic solutions for the planar three body problem

In this paper, we present evidence of the stability of a simplified model of the Solar System, a flat (Newtonian) Sun-Jupiter-Saturn system with realistic data: masses of the Sun and the planets, their semi-axes, eccentricities and (apsidal) precessions of the planets close to the real ones. The evidence is based on convincing numerics that a KAM theorem can be applied to the Hamiltonian equations of the model to produce quasiperiodic motion (on an invariant torus) with the appropriate frequencies. To do so, we first use KAM numerical schemes to compute translated tori to continue from the Kepler approximation (two uncoupled two-body problems) up to the actual Hamiltonian of the system, for which the translated torus is an invariant torus. Second, we use KAM numerical schemes for invariant tori to refine the solution giving the desired torus. Lastly, the convergence of the KAM scheme for the invariant torus is (numerically) checked by applying several times a KAM iterative lemma, from which we obtain that the final torus (numerically) satisfies the existence conditions given by a KAM theorem.
Matrix Completion via Nonsmooth Regularization of Fully Connected Neural Networks

Conventional matrix completion methods approximate the missing values by assuming the matrix to be low-rank, which leads to a linear approximation of missing values. It has been shown that enhanced performance could be attained by using nonlinear estimators such as deep neural networks. Deep fully connected neural networks (FCNNs), one of the most suitable architectures for matrix completion, suffer from over-fitting due to their high capacity, which leads to low generalizability. In this paper, we control over-fitting by regularizing the FCNN model in terms of the $\ell_{1}$ norm of intermediate representations and nuclear norm of weight matrices. As such, the resulting regularized objective function becomes nonsmooth and nonconvex, i.e., existing gradient-based methods cannot be applied to our model. We propose a variant of the proximal gradient method and investigate its convergence to a critical point. In the initial epochs of FCNN training, the regularization terms are ignored, and through epochs, the effect of that increases. The gradual addition of nonsmooth regularization terms is the main reason for the better performance of the deep neural network with nonsmooth regularization terms (DNN-NSR) algorithm. Our simulations indicate the superiority of the proposed algorithm in comparison with existing linear and nonlinear algorithms.