Miracle-dz/UniFAR-FTUs huggingface dataset:
Protocol selection for second-order consensus against disturbance

Noticing that both the absolute and relative velocity protocols can solve the second-order consensus of multi-agent systems, this paper aims to investigate which of the above two protocols has better anti-disturbance capability, in which the anti-disturbance capability is measured by the L2 gain from the disturbance to the consensus error. More specifically, by the orthogonal transformation technique, the analytic expression of the L2 gain of the second-order multi-agent system with absolute velocity protocol is firstly derived, followed by the counterpart with relative velocity protocol. It is shown that both the L2 gains for absolute and relative velocity protocols are determined only by the minimum non-zero eigenvalue of Laplacian matrix and the tunable gains of the state and velocity. Then, we establish the graph conditions to tell which protocol has better anti-disturbance capability. Moreover, we propose a two-step scheme to improve the anti-disturbance capability of second-order multi-agent systems. Finally, simulations are given to illustrate the effectiveness of our findings.
Statistical guarantees for sparse deep learning

Neural networks are becoming increasingly popular in applications, but our mathematical understanding of their potential and limitations is still limited. In this paper, we further this understanding by developing statistical guarantees for sparse deep learning. In contrast to previous work, we consider different types of sparsity, such as few active connections, few active nodes, and other norm-based types of sparsity. Moreover, our theories cover important aspects that previous theories have neglected, such as multiple outputs, regularization, and $$\ell_{2}$$ ℓ 2 -loss. The guarantees have a mild dependence on network widths and depths, which means that they support the application of sparse but wide and deep networks from a statistical perspective. Some of the concepts and tools that we use in our derivations are uncommon in deep learning and, hence, might be of additional interest.
Iterative roots of multifunctions

Some easily verifiable sufficient conditions for the nonexistence of iterative roots for multifunctions on arbitrary nonempty sets are presented. Typically if the graph of the multifunction has a distinguished point with a relatively large number of paths leading to it then such a multifunction does not admit any iterative root. These results can be applied to single-valued maps by considering their pullbacks as multifunctions. This has been illustrated by showing the nonexistence of iterative roots of some specified orders for certain complex polynomials.
Thermal stability of the magnetization following thermomagnetic writing in perpendicular media

The thermal decay of the magnetization in granular thin films with high perpendicular anisotropy following thermomagnetic writing is investigated using a semianalytical and a numerical mean-field self-consistent model. We study the effects of cooling rate, dispersions in grain volumes and anisotropy fields, damping, and intergranular interactions. Two distinct contributions to the thermal decay arise from cooling and storage, respectively. The magnetization freezes during cooling as a result of the increase in anisotropy and the net thermal decay for slow cooling is logarithmically dependent on the time before the magnetization is frozen. The areal density advantage of heat-assisted, over conventional magnetic recording increases sharply when the thermal decay starts close to the Curie temperature. The maximum benefits of heat-assisted recording using FePt media are derived by heating above the Curie temperature.
Thin Sidon sets and the nonlinearity of vectorial Boolean functions

The vectorial nonlinearity of a vector valued function is its distance from the set of affine functions. In 2017, Liu, Mesnager and Chen conjectured a general upper bound for the vectorial linearity. Recently, Carlet proved a lower bound in terms of the differential uniformity. In this paper, we improve Carlet's lower bound. Our method is elementary, it relies on the fact that the level sets of an APN functions are Sidon sets. We give a survey on Sidon sets in elementary abelian 2-groups. We study the completeness problem of Sidon sets obtained from hyperbolas and ellipses of the finite affine plane.
DexBERT: Effective, Task-Agnostic and Fine-Grained Representation Learning of Android Bytecode

The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the representation of these artifacts (e.g., source code or executable code) into a form that is suitable for learning. Traditionally, researchers and practitioners have relied on manually selected features, based on expert knowledge, for the task at hand. Such knowledge is sometimes imprecise and generally incomplete. To overcome this limitation, many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable representations and selections of the most relevant features. Yet, in the context of Android problems, existing models are either limited to coarse-grained whole-app level (e.g., apk2vec) or conducted for one specific downstream task (e.g., smali2vec). Thus, the produced representation may turn out to be unsuitable for fine-grained tasks or cannot generalize beyond the task that they have been trained on. Our work is part of a new line of research that investigates effective, task-agnostic, and fine-grained universal representations of bytecode to mitigate both of these two limitations. Such representations aim to capture information relevant to various low-level downstream tasks (e.g., at the class-level). We are inspired by the field of Natural Language Processing, where the problem of universal representation was addressed by building Universal Language Models, such as BERT, whose goal is to capture abstract semantic information about sentences, in a way that is reusable for a variety of tasks. We propose DexBERT, a BERT-like Language Model dedicated to representing chunks of DEX bytecode, the main binary format used in Android applications. We empirically assess whether DexBERT is able to model the DEX language and evaluate the suitability of our model in three distinct class-level software engineering tasks: Malicious Code Localization, Defect Prediction, and Component Type Classification. We also experiment with strategies to deal with the problem of catering to apps having vastly different sizes, and we demonstrate one example of using our technique to investigate what information is relevant to a given task.
Programmable Spectral Filter Arrays using Phase Spatial Light Modulators

Computational imaging has always benefited from tools that modulate light along the many dimensions of its plenoptic function. This paper provides a practical architecture for achieving spatially varying spectral modulation using a liquid crystal phase spatial light modulator (SLM). The use of a phase SLM, however, results in strong optical aberrations due to the unintended phase modulation, thereby precluding spectral modulation at high spatial resolutions. To mitigate this, we provide a careful and systematic analysis of the aberrations arising out of phase SLMs for the purpose of spatially varying spectral modulation; this analysis results in a dual strategy of “good patterns” that minimize the optical aberrations and a deep restoration network that overcomes any residual aberrations. We show a number of unique operating points with our prototype including single- and multi-image hyperspectral imaging, material classification (fewer than two images), and dynamic spectral filtering at video rates.
Memory-Efficient Model-Based Deep Learning With Convergence and Robustness Guarantees

Computational imaging has been revolutionized by compressed sensing algorithms, which offer guaranteed uniqueness, convergence, and stability properties. Model-based deep learning methods that combine imaging physics with learned regularization priors have emerged as more powerful alternatives for image recovery. The main focus of this paper is to introduce a memory efficient model-based algorithm with similar theoretical guarantees as CS methods. The proposed iterative algorithm alternates between a gradient descent involving the score function and a conjugate gradient algorithm to encourage data consistency. The score function is modeled as a monotone convolutional neural network. Our analysis shows that the monotone constraint is necessary and sufficient to enforce the uniqueness of the fixed point in arbitrary inverse problems. In addition, it also guarantees the convergence to a fixed point, which is robust to input perturbations. We introduce two implementations of the proposed MOL framework, which differ in the way the monotone property is imposed. The first approach enforces a strict monotone constraint, while the second one relies on an approximation. The guarantees are not valid for the second approach in the strict sense. However, our empirical studies show that the convergence and robustness of both approaches are comparable, while the less constrained approximate implementation offers better performance. The proposed deep equilibrium formulation is significantly more memory efficient than unrolled methods, which allows us to apply it to 3D or 2D+time problems that current unrolled algorithms cannot handle.
Enabling All In-Edge Deep Learning: A Literature Review

In recent years, deep learning (DL) models have demonstrated remarkable achievements on non-trivial tasks such as speech recognition, image processing, and natural language understanding. One of the significant contributors to the success of DL is the proliferation of end devices that act as a catalyst to provide data for data-hungry DL models. However, computing DL training and inference still remains the biggest challenge. Moreover, most of the time central cloud servers are used for such computation, thus opening up other significant challenges, such as high latency, increased communication costs, and privacy concerns. To mitigate these drawbacks, considerable efforts have been made to push the processing of DL models to edge servers (a mesh of computing devices near end devices). Recently, the confluence point of DL and edge has given rise to edge intelligence (EI), defined by the International Electrotechnical Commission (IEC) as the concept where the data is acquired, stored, and processed utilizing edge computing with DL and advanced networking capabilities. Broadly, EI has six levels of categories based on the three locations where the training and inference of DL take place, e.g., cloud server, edge server, and end devices. This survey paper focuses primarily on the fifth level of EI, called all in-edge level, where DL training and inference (deployment) are performed solely by edge servers. All in-edge is suitable when the end devices have low computing resources, e.g., Internet-of-Things, and other requirements such as latency and communication cost are important such as in mission-critical applications (e.g., health care). Besides, 5G/6G networks are envisioned to use all in-edge. Firstly, this paper presents all in-edge computing architectures, including centralized, decentralized, and distributed. Secondly, this paper presents enabling technologies, such as model parallelism, data parallelism, and split learning, which facilitates DL training and deployment at edge servers. Thirdly, model adaptation techniques based on model compression and conditional computation are described because the standard cloud-based DL deployment cannot be directly applied to all in-edge due to its limited computational resources. Fourthly, this paper discusses eleven key performance metrics to evaluate the performance of DL at all in-edge efficiently. Finally, several open research challenges in the area of all in-edge are presented.
Forecasting Autoregressive Time Series in the Presence of Deterministic Components

This paper studies the error in forecasting an autoregressive process with a deterministic component. We show that when the data are strongly serially correlated, forecasts based on a model that detrends the data using OLS before estimating the autoregressive parameters are much less precise than those based on an autoregression that includes the deterministic components, and the asymptotic distribution of the forecast errors under the two-step procedure exhibits bimodality. We explore the conditions under which feasible GLS trend estimation can lead to forecast error reduction. The finite sample properties of OLS and feasible GLS forecasts are compared with forecasts based on unit root pretesting. The procedures are applied to 15 macroeconomic time series to obtain real time forecasts. Forecasts based on feasible GLS trend estimation tend to be more efficient than forecasts based on OLS trend estimation. A new finding is when a unit root pretest rejects non-stationarity, use of GLS yields smaller forecast errors than OLS. When the series to be forecasted is highly persistent, GLS trend estimation in conjunction with unit root pretests can lead to sharp reduction in forecast errors.
Stereopsis without correspondence

Stereopsis has traditionally been considered a complex visual ability, restricted to large-brained animals. The discovery in the 1980s that insects, too, have stereopsis, therefore, challenged theories of stereopsis. How can such simple brains see in three dimensions? A likely answer is that insect stereopsis has evolved to produce simple behaviour, such as orienting towards the closer of two objects or triggering a strike when prey comes within range. Scientific thinking about stereopsis has been unduly anthropomorphic, for example assuming that stereopsis must require binocular fusion or a solution of the stereo correspondence problem. In fact, useful behaviour can be produced with very basic stereoscopic algorithms which make no attempt to achieve fusion or correspondence, or to produce even a coarse map of depth across the visual field. This may explain why some aspects of insect stereopsis seem poorly designed from an engineering point of view: for example, paying no attention to whether interocular contrast or velocities match. Such algorithms demonstrably work well enough in practice for their species, and may prove useful in particular autonomous applications. This article is part of a discussion meeting issue ‘New approaches to 3D vision’.
LED-based digital holographic microscopy with slightly off-axis interferometry

An LED illuminated Linnik-type digital holographic microscope (DHM) for high-quality phase imaging is presented by the adoption of slightly off-axis two-step blind-phase-shifting interferometry (TB-PSI). Slightly off-axis interferometry lowers the requirement on the angle between the object and the reference waves as well as the requirement on the resolving power of the charge-coupled device (CCD) camera. In addition, the apparatus is cost-effective and offers ease of alignment. The phase-shifting DHM is simply implemented by mechanically moving the reference mirror while disposing of a precise phase modulator such as a piezoelectric transducer (PZT). The phase shift between the two interferograms is extracted by Fourier transformation analysis, and then the phase image is reconstructed. The performance of the TB-PSI used in the scheme is analyzed. The phase imaging for nanostructured specimens is conducted, and the results demonstrate the feasibility of the scheme. The phase noise is reduced by 73% when compared to the result obtained with coherent illumination.
Perceiving distance in virtual reality: theoretical insights from contemporary technologies

Decades of research have shown that absolute egocentric distance is underestimated in virtual environments (VEs) when compared with the real world. This finding has implications on the use of VEs for applications that require an accurate sense of absolute scale. Fortunately, this underperception of scale can be attenuated by several factors, making perception more similar to (but still not the same as) that of the real world. Here, we examine these factors as two categories: (i) experience inherent to the observer, and (ii) characteristics inherent to the display technology. We analyse how these factors influence the sources of information for absolute distance perception with the goal of understanding how the scale of virtual spaces is calibrated. We identify six types of cues that change with these approaches, contributing both to a theoretical understanding of depth perception in VEs and a call for future research that can benefit from changing technologies. This article is part of the theme issue ‘New approaches to 3D vision’.
Salmonella Subpopulations Identified from Human Specimens Express Heterogenous Phenotypes That Are Relevant to Clinical Diagnosis

In animal model systems, heterogenous Salmonella phenotypes were found previously to regulate bacterial infections. We describe in this communication that different Salmonella phenotypes also exist in infected humans at a single disease time point and that their phenotypic and molecular traits are associated with different aspects of pathogenicity. ABSTRACT Clonal bacterial cells can give rise to functionally heterogeneous subpopulations. This diversification is considered an adaptation strategy that has been demonstrated for several bacterial species, including Salmonella enterica serovar Typhimurium. In previous studies on mouse models infected orally with pure Salmonella cultures, derived bacterial cells collected from animal tissues were found to express heterogenous phenotypes. Here, we show mixed Salmonella populations, apparently derived from the same progenitor, present in human specimens collected at a single disease time point, and in a long-term-infected patient, these Salmonella were no longer expressing surface-exposed antigen epitopes by isolates collected at earlier days of the disease. The subpopulations express different phenotypes related to cell surface antigen expression, motility, biofilm formation, biochemical metabolism, and antibiotic resistance, which can all contribute to pathogenicity. Some of the phenotypes correlate with single nucleotide polymorphisms or other sequence changes in bacterial genomes. These genetic variations can alter synthesis of cell membrane-associated molecules such as lipopolysaccharides and lipoproteins, leading to changes in bacterial surface structure and function. This study demonstrates the limitation of Salmonella diagnostic methods that are based on a single-cell population which may not represent the heterogenous bacterial community in infected humans. IMPORTANCE In animal model systems, heterogenous Salmonella phenotypes were found previously to regulate bacterial infections. We describe in this communication that different Salmonella phenotypes also exist in infected humans at a single disease time point and that their phenotypic and molecular traits are associated with different aspects of pathogenicity. Notably, variation in genes encoding antibiotic resistance and two-component systems were observed from the subpopulations of a patient suffering from persistent salmonellosis. Therefore, clinical and public health interventions of the disease that are based on diagnosis of a single-cell population may miss other subpopulations that can cause residual human infections.
Comparison of Pharmacokinetics of the GalNAc-Conjugated Antisense Oligonucleotide GSK3389404 in Participants with Chronic Hepatitis B Infection across the Asia-Pacific Region

GSK3389404, an N-acetyl galactosamine-conjugated antisense oligonucleotide (ASO), was in clinical development for chronic hepatitis B (CHB) treatment. Few studies have examined ASOs in Asian participants. ABSTRACT GSK3389404, an N-acetyl galactosamine-conjugated antisense oligonucleotide (ASO), was in clinical development for chronic hepatitis B (CHB) treatment. Few studies have examined ASOs in Asian participants. In this analysis, the plasma pharmacokinetics (PK) of GSK3389404 were characterized and compared in patients with CHB across the Asia-Pacific region (N = 64), including mainland China (n = 16), Hong Kong (n = 8), Japan (n = 21), South Korea (n = 12), Singapore (n = 4), and the Philippines (n = 3), from a phase 2a, multicenter, randomized, double-blind, placebo-controlled study (NCT03020745). Hepatitis B(e) antigen-positive and -negative patients (on or not on stable nucleos[t]ide regimens) received single (30 mg or 120 mg) or multiple (30 mg, 60 mg, or 120 mg weekly or 120 mg biweekly) subcutaneous GSK3389404 injections. The plasma concentrations were measured on day 1 in all cohorts as well as on days 29 and 57 in the multiple-dose cohorts. The GSK3389404 plasma PK were similar to those reported in a previous study in non-Asian healthy participants with a median time to peak concentration (tmax) of 1 to 4 h postdose, a mean half-life of 3 to 5 h across cohorts, and no accumulation following repeat dosing. The GSK3389404 plasma tmax and half-life values were dose-independent. The increase in the plasma peak concentration (Cmax) and the area under the concentration versus time curve (AUC) was dose-proportional from 60 to 120 mg and greater than dose-proportional from 30 to 60 or 120 mg. The GSK3389404 plasma concentration versus time profiles, half-life, tmax, Cmax, and AUC values were all comparable across the Asia-Pacific populations. Given the similarity of the PK among ASOs, this analysis suggests that the PK from any Asia-Pacific population may be used to guide ASO dose selection in the Asia-Pacific region.
Asymptotic Scattering of Electromagnetic Waves from an Array of Dielectrically Loaded Conducting Strips

A rigorous field analysis of the electromagnetic scattering from a perfectly conducting strip loaded with a dielectric cylinder is presented. The dielectric cylinder is of circular cross-section and the dielectric medium is assumed to be linear, homogeneous, and isotropic. The strip is oriented radially with respect to the center of the cylinder and can be located inside or outside, but touching, the outer boundary of the cylinder. The structure is excited by a plane wave or a line current field and both TM and TE to z excitations are considered. The solution of the scattering from a single loaded strip is based on a boundary value approach and a high frequency asymptotic technique is also developed for the scattering from an array of loaded strips. The validity of this proposed solution is investigated by comparing the numerical results of selected cases with those based on exact and method of moments solutions. Sample numerical results for the scattering from a single loaded strip and an array of loaded.
Improving Imbalanced Machine Learning with Neighborhood-Informed Synthetic Sample Placement

ABSTRACT Machine learning is widely used in information systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, fintech, or cybersecurity contexts, certain subclasses are difficult to learn because they are underrepresented in training data. Our study offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm (SANSA), which, in contrast to other solutions, introduces a novel “placement” parameter that can be tuned to adapt to each dataset’s unique manifestation of the imbalance. This package can be downloaded for R 1 . We tested SANSA against seven existing sampling methods used in conjunction with the four most frequently used machine learning models trained on 14 benchmark datasets. Our results provide suggestive evidence that SANSA offers a feasible solution to the imbalance problem for most datasets. Our findings provide practical recommendations for how SANSA can be effectively implemented while reducing the complexity level of an imbalanced learning pipeline.