Unknown Subset Research Articles

Perfect Monomial Prediction for Modular Addition

Modular addition is often the most complex component of typical Addition- Rotation-XOR (ARX) ciphers, and the division property is the most effective tool for detecting integral distinguishers. Thus, having a precise division property model for modular addition is crucial in the search for integral distinguishers in ARX ciphers. Current division property models for modular addition either (a) express the operation as a Boolean circuit and apply standard propagation rules for basic operations (COPY, XOR, AND), or (b) treat it as a sequence of smaller functions with carry bits, modeling each function individually. Both approaches were originally proposed for the twosubset bit-based division property (2BDP), which is theoretically imprecise and may overlook some balanced bits.Recently, more precise versions of the division property, such as parity sets, threesubset bit-based division property without unknown subsets (3BDPwoU) or monomial prediction (MP), and algebraic transition matrices have been proposed. However, little attention has been given to modular addition within these precise models.The propagation rule for the precise division property of a vectorial Boolean function f requires that u can propagate to v if and only if the monomial πu(x) appears in πv(f). Braeken and Semaev (FSE 2005) studied the algebraic structure of modular addition and showed that for x ⊞ y = z, the monomial πu(x)πv(y) appears in πw(z) if and only if u + v = w. Their theorem directly leads to a precise division property model for modular addition. Surprisingly, this model has not been applied in division property searches, to the best of our knowledge.In this paper, we apply Braeken and Semaev’s theorem to search for integral distinguishers in ARX ciphers, leading to several new results. First, we improve the state-of-the-art integral distinguishers for all variants of the Speck family, significantly enhancing search efficiency for Speck-32/48/64/96 and detecting new integral distinguishers for Speck-48/64/96/128. Second, we determine the exact degrees of output bits for 7-round Speck-32 and all/16/2 output bits for 2/3/4-round Alzette for the first time. Third, we revisit the choice of rotation parameters in Speck instances, providing a criterion that enhances resistance against integral distinguishers. Additionally, we offer a simpler proof for Braeken and Semaev’s theorem using monomial prediction, demonstrating the potential of division property methods in the study of Boolean functions.We hope that the proposed methods will be valuable in the future design of ARX ciphers.

Dynamic Cube Attacks against Grain-128AEAD

In this paper, we revisit the division property based dynamic cube attack on the full Grain-128 presented by Hao et al. at FSE 2020 and demonstrate that their attack on the full Grain-128 is invalid, that is, no key information could be successfully recovered. The theoretical framework for the dynamic cube attack provided by Hao et al. is correct, but the technique for building the MILP model in the dynamic cube attack has flaws. Besides, strong evidence indicates that their bias estimation method is not applicable to Grain-128AEAD and Grain-128. Accordingly, we introduce the three-subset division property without unknown subset (3SDP/u) into dynamic cube attacks and present a correct MILP modeling technique. In addition, we propose a heuristic technique called Polynomial Approximation with regard to Bias (PAB) to evaluate the bias in superpolies in the dynamic cube attack, which can provide a more accurate bias evaluation for high-dimension cubes. As a result, we implemented the dynamic cube attack based on 3SDP/u on 190-round Grain-128AEAD, and we could recover 3 key bits with a complexity 2103.44 and the success probability was evaluated to be 99.68%. For Grain-128, some zero-sum distinguishers of cube size 80 are given for the first time.

Provable Robustness against a Union of L_0 Adversarial Attacks

Sparse or L0 adversarial attacks arbitrarily perturb an unknown subset of the features. L0 robustness analysis is particularly well-suited for heterogeneous (tabular) data where features have different types or scales. State-of-the-art L0 certified defenses are based on randomized smoothing and apply to evasion attacks only. This paper proposes feature partition aggregation (FPA) -- a certified defense against the union of L0 evasion, backdoor, and poisoning attacks. FPA generates its stronger robustness guarantees via an ensemble whose submodels are trained on disjoint feature sets. Compared to state-of-the-art L0 defenses, FPA is up to 3,000x faster and provides larger median robustness guarantees (e.g., median certificates of 13 pixels over 10 for CIFAR10, 12 pixels over 10 for MNIST, 4 features over 1 for Weather, and 3 features over 1 for Ames), meaning FPA provides the additional dimensions of robustness essentially for free.

Formula omitted]ODNS: A distributed approach to DNS anonymization with collusion resistance

The traditional Domain Name System (DNS) lacks fundamental security and privacy features in its design. As privacy concerns increased on the Internet, security and privacy enhancements of DNS have been actively investigated. Specifically, in the context of user privacy in DNS queries, several relay-based anonymization schemes have been recently introduced. However, these schemes are vulnerable to collusion between relays and full-service resolvers, which means user identities cannot be hidden from resolvers. This paper introduces a new concept for achieving user anonymity in DNS queries through a multiple-relay-based approach, called μODNS (Mutualized Oblivious DNS), by extending the concept of existing relay-based schemes. μODNS introduces a reasonable assumption that each user has at least one trusted or dedicated relay within the network and mutually shares the relay with other users. The user simply sets his trusted relay as the next-hop relay to convey his queries to the resolver and randomly chooses its zero or more subsequent relays shared by other entities. Under this assumption, the user’s identity remains concealed from the target resolver in μODNS even if an unknown subset of relays colludes with the resolver. Namely, in μODNS, users can preserve their anonymity by paying a small cost of sharing their resources. Additionally, we extend existing protocols, Anonymized DNSCrypt and Oblivious DoH, to provide practical Proof-of-Concept specifications and implementations as instances of μODNS. These implementations are publicly available on the Internet as open-source software and public services. Furthermore, we demonstrate, through measurements of round-trip times for DNS messages, that our implementation can minimize the performance degradation resulting from its privacy enhancements, achieving performance levels that maintain the positive user experiences observed in existing schemes.

TAG-DTA: Binding-region-guided strategy to predict drug-target affinity using transformers

The proper assessment of target-specific compound selectivity is paramount in the drug discovery context, promoting the identification of drug-target interactions (DTIs) and the discovery of potential leads. On that account, the accurate prediction of an unbiased drug-target binding affinity (DTA) metric is pivotal to understanding the binding process. Most in silico computational approaches, however, neglect the inter-dependency of the proteomics, chemical, and pharmacological spaces and the explainability during the model construction. Furthermore, these methods have yet to actively include information associated with binding pockets during the learning process, which is essential to DTA prediction performance and model explainability. In this study, we propose an end-to-end binding-region-guided Transformer-based architecture that simultaneously predicts the 1D binding pocket and the binding affinity of DTI pairs, where the prediction of the 1D binding pocket guides and conditions the prediction of DTA. This architecture uses 1D raw sequential and structural data to represent the proteins and compounds, respectively, and combines multiple Transformer-Encoder blocks to capture and learn the proteomics, chemical, and pharmacological contexts. The predicted 1D binding pocket conditions the attention mechanism of the Transformer-Encoder used to learn the pharmacological space in order to model the inter-dependency amongst binding-related positions. The results show that the proposed architecture, TAG-DTA, achieved the best performance in DTA prediction compared to state-of-the-art benchmarks, including in unknown subsets of the proteomics and chemical representation spaces. Moreover, the 1D binding pocket prediction increases the discriminative power and robustness of the aggregate representation of the pharmacological space and improves the DTA prediction performance. Overall, this research study validates the applicability of an end-to-end Transformer-based architecture in the context of drug discovery, and that combining computationally different yet contextually related tasks is critical to new findings in the DTI domain. Additionally, it shows that TAG-DTA is capable of providing increasing DTI and prediction understanding due to the nature of the attention blocks and prediction of the 1D binding pocket. The data and source code used in this study are available at: https://github.com/larngroup/TAG-DTA.

757 Wound healing in aged skin exhibits systems-level alterations in cellular composition and cell-cell communication

Delayed or impaired wound healing in aging skin poses major medical, social, and economic challenges, yet a systematic understanding of the contributing cellular and molecular alterations is lacking. In this work, we use single-cell RNA sequencing to interrogate differences across epithelial, fibroblast, and immune cell types in the skin wounds of young and aged animals. We have identified major changes in their subset compositions and molecular profiles during wound healing. Our data uncover a more inflammatory phenotype in wounds from aged mice, featuring altered immune cell compositions and a previously unknown inflammatory Arg1Hi macrophage subset, compared to young counterparts.

Byzantine-Resilient Distributed Hypothesis Testing With Time-Varying Network Topology

In this article, we study the problem of distributed hypothesis testing over a network of mobile agents with limited communication and sensing ranges to infer the true hypothesis collaboratively. In particular, we consider a scenario where there is an unknown subset of compromised agents that may deliberately share altered information to undermine the team objective. We propose two distributed algorithms where each agent maintains and updates two sets of beliefs (i.e., probability distributions over the hypotheses), namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">actual</i> beliefs (LB and AB, respectively, for brevity). In both algorithms, at every time step, each agent shares its AB with other agents within its communication range and makes a local observation to update its LB. Then, both algorithms can use the shared information to update ABs under certain conditions. One requires receiving a certain number of shared ABs at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">each time instant</i> ; the other accumulates shared ABs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">over time</i> and updates after the number of shared ABs exceeds a prescribed threshold. Otherwise, both algorithms rely on the agent’s current LB and AB to update the new AB. We prove under mild assumptions that the AB for every noncompromised agent converges almost surely to the true hypothesis, without requiring connectivity in the underlying time-varying network topology. Using a simulation of a team of unmanned aerial vehicles aiming to classify adversarial agents among themselves, we illustrate and compare the proposed algorithms. Finally, we show experimentally that the second algorithm consistently outperforms the first algorithm in terms of the speed of convergence.

How to Secure Distributed Filters Under Sensor Attacks

In this article, we study how to secure distributed filters for linear time-invariant systems with bounded noise under false-data injection attacks. A malicious attacker is able to arbitrarily manipulate the observations for a time-varying and unknown subset of the sensors. We first propose a recursive distributed filter consisting of two steps at each update. The first step employs a saturation-like scheme, which gives a small gain if the innovation is large corresponding to a potential attack. The second step is a consensus operation of state estimates among neighboring sensors. We prove the estimation error is upper bounded if the filter parameters satisfy a condition. We further analyze the feasibility of the condition and connect it to sparse observability in the centralized case. When the attacked sensor set is known to be time-invariant, the secured filter is modified by adding an online local attack detector. The detector is able to identify the attacked sensors whose observation innovations are larger than the detection thresholds. Also, with more attacked sensors being detected, the thresholds will adaptively adjust to reduce the space of the stealthy attack signals. The resilience of the secured filter with detection is verified by an explicit relationship between the upper bound of the estimation error and the number of detected attacked sensors. Moreover, for the noise-free case, we prove that the state estimate of each sensor asymptotically converges to the system state under certain conditions. Numerical simulations are provided to illustrate the developed results.

Robust change detection for large-scale data streams

Robust change point detection for large-scale data streams has many real-world applications in industrial quality control, signal detection, and biosurveillance. Unfortunately, it is highly nontrivial to develop efficient schemes due to three challenges: (1) the unknown sparse subset of affected data streams, (2) the unexpected outliers, and (3) computational scalability for real-time monitoring and detection. In this article, we develop a family of efficient real-time robust detection schemes for monitoring large-scale independent data streams. For each data stream, we propose to construct a new local robust detection statistic called the -CUSUM (cumulative sum) statistic that can reduce the effect of outliers by using the Box-Cox transformation of the likelihood function. Then the global scheme will raise an alarm based upon the sum of the shrinkage transformation of these local -CUSUM statistics to filter out unaffected data streams. In addition, we propose a new concept called false alarm breakdown point to measure the robustness of online monitoring schemes and propose a worst-case detection efficiency score to measure the detection efficiency when the data contain outliers. We then characterize the breakdown point and the efficiency score of our proposed schemes. Asymptotic analysis and numerical simulations are conducted to illustrate the robustness and efficiency of our proposed schemes.

A topological approach to inferring the intrinsic dimension of convex sensing data

We consider a common measurement paradigm, where an unknown subset of an affine space is measured by unknown continuous quasi-convex functions. Given the measurement data, can one determine the dimension of this space? In this paper, we develop a method for inferring the intrinsic dimension of the data from measurements by quasi-convex functions, under natural assumptions. The dimension inference problem depends only on discrete data of the ordering of the measured points of space, induced by the sensor functions. We construct a filtration of Dowker complexes, associated to measurements by quasi-convex functions. Topological features of these complexes are then used to infer the intrinsic dimension. We prove convergence theorems that guarantee obtaining the correct intrinsic dimension in the limit of large data, under natural assumptions. We also illustrate the usability of this method in simulations.

Optimal rendezvous on a line by location-aware robots in the presence of spies*

A set of mobile robots is placed at arbitrary points of an infinite line. The robots are equipped with GPS devices and they may communicate their positions on the line to a central authority. The collection contains an unknown subset of “spies”, i.e., byzantine robots, which are indistinguishable from the non-faulty ones. The set of the non-faulty robots needs to rendezvous in the shortest possible time in order to perform some task, while the byzantine robots may try to delay their rendezvous for as long as possible. The problem facing a central authority is to determine trajectories for all robots so as to minimize the time until all the non-faulty robots have met. The trajectories must be determined without knowledge of which robots are faulty. Our goal is to minimize the competitive ratio between the time required to achieve the first rendezvous of the non-faulty robots and the time required for such a rendezvous to occur under the assumption that the faulty robots are known at the start. In this paper, we give rendezvous algorithms with bounded competitive ratio, where the central authority is informed only of the set of initial robot positions, without knowing which ones or how many of them are faulty. In general, regardless of the number of faults [Formula: see text] it can be shown that there is an algorithm with bounded competitive ratio. Further, we are able to give a rendezvous algorithm with optimal competitive ratio provided that the number [Formula: see text] of faults is strictly less than [Formula: see text]. Note, however, that in general this algorithm does not give an estimate on the actual value of the competitive ratio. However, when an upper bound on the number of byzantine robots is known to the central authority, we can provide algorithms with constant competitive ratios and in some instances we are able to show that these algorithms are optimal. Moreover, in the cases where the number of faults is either [Formula: see text] or [Formula: see text] we are able to compute the competitive ratio of an optimal rendezvous algorithm, for a small number of robots.

A Dirichlet process model for change‐point detection with multivariate bioclimatic data

AbstractMotivated by real‐world data of monthly values of precipitation, minimum, and maximum temperature recorded at 360 monitoring stations covering the Italian territory for 60 years ( months), in this work we propose a change‐point model for multiple multivariate time series, inspired by the hierarchical Dirichlet process. We assume that each station has its change‐point structure and, as main novelties, we allow unknown subsets of the parameters in the data likelihood to stay unchanged before and after a change‐point, that stations possibly share values of the same parameters and that the unknown number of weather regimes is estimated as a random quantity. Owing to the richness of the formalization, our proposal enables us to identify clusters of spatial units for each parameter, evaluate which parameters are more likely to change simultaneously, and distinguish between abrupt changes and smooth ones. The proposed model provides useful benchmarks to focus monitoring programs regarding ecosystem responses. Results are shown for the whole data, and a detailed description is given for three monitoring stations. Evidence of local behaviors includes highlighting differences in the potential vulnerability to climate change of the Mediterranean ecosystems from the Temperate ones and locating change trends distinguishing between continental plains and mountain ranges.

Factor models with local factors — Determining the number of relevant factors

We extend the theory on factor models by incorporating “local” factors into the model. Local factors affect only an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. We derive which factors are pervasive enough to be economically important and which factors are pervasive enough to be estimable using the common principal component estimator. We then introduce a new class of estimators to determine the number of those relevant factors. Unlike existing estimators, our estimators use not only the eigenvalues of the covariance matrix, but also its eigenvectors. We find that incorporating partial sums of the eigenvectors into our estimators leads to significant gains in performance in simulations.

Modeling for Three-Subset Division Property without Unknown Subset

A division property is a generic tool to search for integral distinguishers, and automatic tools such as MILP or SAT/SMT allow us to evaluate the propagation efficiently. In the application to stream ciphers, it enables us to estimate the security of cube attacks theoretically, and it leads to the best key-recovery attacks against well-known stream ciphers. However, it was reported that some of the key-recovery attacks based on the division property degenerate to distinguishing attacks due to the inaccuracy of the division property. Three-subset division property (without unknown subset) is a promising method to solve this inaccuracy problem, and a new algorithm using automatic tools for the three-subset division property was recently proposed at Asiacrypt2019. In this paper, we first show that this state-of-the-art algorithm is not always efficient and we cannot improve the existing key-recovery attacks. Then, we focus on the three-subset division property without unknown subset and propose another new efficient algorithm using automatic tools. Our algorithm is more efficient than existing algorithms, and it can improve existing key-recovery attacks. In the application to Trivium, we show a 842-round key-recovery attack. We also show that a 855-round key-recovery attack, which was proposed at CRYPTO2018, has a critical flaw and does not work. As a result, our 842-round attack becomes the best key-recovery attack. In the application to Grain-128AEAD, we show that the known 184-round key-recovery attack degenerates to a distinguishing attack. Then, the distinguishing attacks are improved up to 189 rounds, and we also show the best key-recovery attack against 190 rounds. In the application to ACORN, we prove that the 772-round key-recovery attack at ISC2019 is in fact a constant-sum distinguisher. We then give new key-recovery attacks mounting to 773-, 774- and 775-round ACORN. We verify the current best key-recovery attack on 892-round Kreyvium and recover the exact superpoly. We further propose a new attack mounting to 893 rounds.

On the weakest information on failures to solve mutual exclusion and consensus in asynchronous crash-prone read/write systems

Mutual exclusion and consensus are among the most important coordination problems encountered in asynchronous concurrent systems, whether processes communicate using read/write registers or message passing. Unfortunately, neither can be solved in crash-prone systems, as soon as even a single process may crash. Hence, an important question: which is the weakest information on failures that must be given to the processes so that these problems can be solved whatever the number of crashes. This approach to circumvent impossibility results is known under the name failure detectors.Considering mutual exclusion and consensus in a crash-prone asynchronous system where the processes communicate through read/write registers, this article answers the previous question by presenting two failure detectors. The first, called Quasi-Perfect (QP) allows mutual exclusion to be solved in the presence of any number of process crashes. The second, called Ω⁎, allows consensus to be solved in the general model where not all but an a priori unknown subset of processes participates in consensus. In addition to algorithms solving each of the previous problems with the help of the associated failure detector, the article shows that QP and Ω⁎ provides the weakest information on failures needed to solve mutex exclusion and participant-restricted consensus respectively.

Identification and characterization of neutrophil heterogeneity in sepsis

BackgroundAlthough the immune function of neutrophils in sepsis has been well described, the heterogeneity of neutrophils remains unclear during the process of sepsis.MethodsIn this study, we used a mouse CLP model to simulate the clinical scenario of patients with sepsis, neutrophil infiltration, abnormal distribution and dysfunction was analyzed. LPS was used to stimulate neutrophils in vitro to simulate sepsis; single-cell gene sequencing technology was used to explore the immunological typing. To explore the immunological function of immunosuppressive neutrophils, PD-L1 knockout neutrophils were cocultured with lymphocytes from wild-type mice.ResultsWe found that neutrophils presented variant dysfunction at the late stage of sepsis, including inhibition of apoptosis, seriously damaged chemotaxis and extensive infiltration into the tissues. Single-cell RNA sequencing revealed that multiple subclusters of neutrophils were differentiated after LPS stimulation. The two-dimensional spatial distribution analysis showed that Foxp3+ T cells were much closer to Ly-6G than the CD4+ and CD8+ cells, indicating that infiltrated neutrophils may play immunomodulatory effect on surrounding T-regs. Further observations showed that LPS mediates PD-L1 over expression through p38α-MSK1/-MK2 pathway in neutrophils. The subsets of highly expressed PD-L1 exert immunosuppressive effect under direct contact mode, including inhibition of T cell activation and induction of T cell apoptosis and trans-differentiation.ConclusionsTaken together, our data identify a previously unknown immunosuppressive subset of neutrophils as inhibitory neutrophil in order to more accurately describe the phenotype and characteristics of these cells in sepsis.

Geometric regularity estimates for fully nonlinear elliptic equations with free boundaries

AbstractIn this manuscript we study geometric regularity estimates for problems driven by fully nonlinear elliptic operators (which can be either degenerate or singular when “the gradient is small”) under strong absorption conditions of the general form: where the mapping fails to decrease fast enough at the origin, so allowing that nonnegative solutions may create plateau regions, that is, a priori unknown subsets where a given solution vanishes identically. We establish improved geometric regularity along the set (the free boundary of the model), for a sharp value of (obtained explicitly) depending only on structural parameters. Non‐degeneracy among others measure theoretical properties are also obtained. A sharp Liouville result for entire solutions with controlled growth at infinity is proved. We also present a number of applications consequential of our findings.

Byzantine-Resilient Multiagent Optimization

We consider the problem of multiagent optimization wherein an unknown subset of agents suffer Byzantine faults and thus behave adversarially. We assume that each agent i has a local cost function f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , and the overarching goal of the good agents is to collaboratively minimize a global objective that properly aggregates these local cost functions. To the best of our knowledge, we are among the first to study Byzantine-resilient optimization where no central coordinating agent exists, and we are the first to characterize the structures of the convex coefficients of the achievable global objectives. Dealing with Byzantine faults is very challenging. For example, in contrast to fault-free networks, reaching Byzantine-resilient agreement even in the simplest setting is far from trivial. We take a step toward solving the proposed Byzantine-resilient multiagent optimization problem by focusing on scalar local cost functions. Our results might provide useful insights for the general local cost functions.

Gathering in the plane of location-aware robots in the presence of spies

A set of mobile robots (represented as points) is distributed in the Cartesian plane. The collection contains an unknown subset of byzantine robots which are indistinguishable from the reliable ones. The reliable robots need to gather, i.e., arrive to a configuration in which at the same time, all of them occupy the same point on the plane. The robots are equipped with GPS devices and at the beginning of the gathering process they communicate the Cartesian coordinates of their respective positions to a central authority. On the basis of this information, and without the knowledge of which robots are faulty, the central authority designs a trajectory for every robot. The central authority aims to provide the trajectories which result in the shortest possible gathering time of the reliable robots. The efficiency of a gathering strategy is measured by its competitive ratio, i.e., the maximal ratio between the time required for gathering achieved by the given trajectories and the optimal time required for gathering in the offline case, i.e., when the faulty robots are known to the central authority in advance. The role of the byzantine robots, controlled by the adversary, is to act so that the gathering is delayed and the resulting competitive ratio is maximized.The objective of our paper is to propose efficient algorithms when the central authority is aware of an upper bound on the number of byzantine robots. We give optimal algorithms for collections of robots known to contain at most one faulty robot. When the proportion of byzantine robots is known to be less than one half or one third, we provide algorithms with small constant competitive ratios. We also propose algorithms with bounded competitive ratio in the case where the proportion of faulty robots is arbitrary.

Feature selection strategies for drug sensitivity prediction

Drug sensitivity prediction constitutes one of the main challenges in personalized medicine. Critically, the sensitivity of cancer cells to treatment depends on an unknown subset of a large number of biological features. Here, we compare standard, data-driven feature selection approaches to feature selection driven by prior knowledge of drug targets, target pathways, and gene expression signatures. We asses these methodologies on Genomics of Drug Sensitivity in Cancer (GDSC) dataset, evaluating 2484 unique models. For 23 drugs, better predictive performance is achieved when the features are selected according to prior knowledge of drug targets and pathways. The best correlation of observed and predicted response using the test set is achieved for Linifanib (r = 0.75). Extending the drug-dependent features with gene expression signatures yields the most predictive models for 60 drugs, with the best performing example of Dabrafenib. For many compounds, even a very small subset of drug-related features is highly predictive of drug sensitivity. Small feature sets selected using prior knowledge are more predictive for drugs targeting specific genes and pathways, while models with wider feature sets perform better for drugs affecting general cellular mechanisms. Appropriate feature selection strategies facilitate the development of interpretable models that are indicative for therapy design.