Invariant Detection Research Articles

Multi-stage progressive change detection on high resolution remote sensing imagery

Change detection in remote sensing images, especially optical high and very high resolution images, is a pivotal technique that enables efficient identification of Earth observation alterations, with widespread applications including land use analysis, urban planning, environmental monitoring, and disaster mapping. Of these applications, change detection is predominantly employed in urbanization studies to monitor developmental changes in construction land. However, the pronounced class imbalance and variances between image domains present formidable challenges to change detection implementations. In response to these issues, we introduce the concept of reverse change discovery, redefining the primary goal of model learning for change detection in optical remote sensing images as stable invariant region detection. In line with this concept, we present a novel Multi-Stage Progressive Change Detection Network (MSP-CD) specifically designed for urbanization change detection, which integrates invariant detection, knowledge distillation, and a coarse-to-fine change detection structure. To validate the effectiveness and robustness of the proposed method, we conducted experiments using two widely recognized public datasets, namely the Lebedev-CD and Levir-CD datasets. The MSP-CD network demonstrated state-of-the-art performance across these datasets. Additionally, we curated an application-specific change detection dataset (JS-CD dataset) using remote sensing images from Jiangsu Province in China, which differs from public datasets in terms of annotations, to further validate the MSP-CD network’s performance. A case study on regional urbanization development in Yangzhou city, conducted using the proposed MSP-CD network, further attests to the applicability of our proposed change detection framework.

Biallelic Landscape of DNMT3A Mutant Myeloid Neoplasia

The structure of DNMT3A comprises an N-terminal regulatory part promoting nuclear localization and targeting chromatin and a C-terminal domain, mainly involved in DNA binding and methylation catalysis. 1DNMT3A mutations in myeloid neoplasia (MN) and clonal hematopoiesis are mostly heterozygous. Homozygous null Dnmt3amice are born runted and Dnmt3a-/- cells express a hypomethylation phenotype. 2 An invariant detection of heterozygous mutations without homozygous, hemizygous, or biallelic configurations suggests absence of acquisition of more malignant phenotypes by progressive inactivation of DNMT3A. Moreover, residual DNMT3A function is essential for cell survival and potentially suggests that synthetic lethality might be a viable therapeutic strategy whereby cases with two intact alleles would be more resistant allowing a therapeutic window. Consequently, identification of biallelic DNMT3A mutant cases may be extremely instructive for the development of therapeutics targeting DNMT3A. We analyzed targeted NGS sequencing results of 4,895 cases with MN enrolled at our institution from 2002 to 2021 and retrospectively reviewed the clinical characteristics of all patients with deleterious DNMT3A mutations. DNMT3A mutations were identified in 461 patients, of whom 39 had double and 1 triple hits. Most of the mutants had AML (n=236) including biphenotypic AML (n=5), followed by MDS (n=142), MDS/MPN overlap (n=46) and MPN (n=37). In addition, mutations were identified in 9% of CHIP/CCUS (n=117) and 4% of aplastic anemia (n=68). Multiple DNMT3A hits were more often encountered in AML than MDS and MDS/MPN overlap (62.5% vs. 32.5% vs. 2.5%; P = .045). Single hits were represented by missense (73%) within the hotspot R882 (35%), frameshifts (10%), nonsense (9%), splice sites (6%) and in-frame ins/del (2%). Double hits configuration had a constellation of missense/missense (40%), missense/frameshifts (25%), missense/nonsense (13%), and missense/splice sites (10%) ( Fig1). R882 accounted for 10% of the multi hit carriers. Of note is that biallelic involvement was not confined to non-R882. Patients harboring multi hits carriedmore likely IDH2 (27.5% vs. 10.5%; OR 2.73; P = .011) and ETV6 (7.5% vs. 1.7%; OR 4.32 P = .043) mutations compared to patients with single hits DNMT3A by multivariate logistic regression analysis. Since deletion or UPD of DNMT3A locus has not been found, one can assume that all cases with one hit harbor monoallelic rather than biallelic subclones. For carriers of multiple mutations, one can stipulate that when the sum of VAFs exceeds the theoretical 60%, the presence of a biallelic subclone might be suspected. Indeed, 24 cases fulfilled this criterion. However, in 40% of patients with double hits, arithmetic VAF did not allow for unequivocal resolution of the mutational configuration, with either smaller biallelic subclones vs. subclonal mosaicism being possible. Next, we compared clinical features and outcomes of patients with mono vs. biallelic cases. In MDS, compared with single hits DNMT3A, biallelic cases showed significantly higher WBC (median 7.49 vs. 3.15 k/µL; P = .0052), ANC (4.88 vs. 1.52 k/µL; P = .0056), and platelet count (264 vs. 83 k/µL; P = .0036). In high-risk MDS and AML, with a median follow-up of 12.5 months (0.2-201), patients with biallelic DNMT3Ashowed significantly poorer 1-year OS rate than patients with single hits (42.8% vs. 65.8%; P = .0095). Longitudinal molecular analysis of 8 cases (2 MDS at onset and upon progression to sAML; 4 AML at diagnosis and following relapse after conventional intensive chemotherapy-induced remission; 1 AML at diagnosis and CR post SCT; 1 AML and donor-derived AML post SCT) demonstrated an early origin of biallelic hits with leukemogenic potential. Indeed, biallelic clones persisted or expanded in 6 transformed cases. Our study describes the likelihood that biallelic configuration of DNMT3A mutations, while rare, is indeed compatible with clonal expansion and thus questions the applicability of a synthetic lethality strategy. We are currently confirming that, indeed, truly biallelic cases vs. mosaicism are permissive using DNASeq of single sorted progenitor cells. The results of this analysis will be available at the time of ASH meeting.

Dendritic plateau potentials can process spike sequences across multiple time-scales

The brain constantly processes information encoded in temporal sequences of spiking activity. This sequential activity emerges from sensory inputs as well as from the brain's own recurrent connectivity and spans multiple dynamically changing timescales. Decoding the temporal order of spiking activity across these varying timescales is a critical function of the brain, but we do not yet understand its neural implementation. The problem is, that the passive dynamics of neural membrane potentials occur on a short millisecond timescale, whereas many cognitive tasks require the integration of information across much slower behavioral timescales. However, actively generated dendritic plateau potentials do occur on such longer timescales, and their essential role for many aspects of cognition has been firmly established by recent experiments. Here, we build on these discoveries and propose a new model of neural computation that emerges from the interaction of localized plateau potentials across a functionally compartmentalized dendritic tree. We show how this interaction offers a robust solution to the timing invariant detection and processing of sequential spike patterns in single neurons. Stochastic synaptic transmission complements the deterministic all-or-none plateau process and improves information transmission by allowing ensembles of neurons to produce graded responses to continuous combinations of features. We found that networks of such neurons can solve highly complex sequence detection tasks by breaking down long inputs into sequences of shorter, random features that can be classified reliably. These results suggest that active dendritic processes are fundamental to neural computation.

Detecting Non-Bloch Topological Invariants in Quantum Dynamics.

Non-Bloch topological invariants preserve the bulk-boundary correspondence in non-Hermitian topological systems, and are a key concept in the contemporary study of non-Hermitian topology. Here we report the dynamic detection of non-Bloch topological invariants in single-photon quantum walks, revealed through the biorthogonal chiral displacement, and crosschecked with the dynamic spin textures in the generalized quasimomentum-time domain following a quantum quench. Both detection schemes are robust against symmetry-preserving disorders, and yield consistent results with theoretical predictions. Our experiments are performed far away from any boundaries, and therefore underline non-Bloch topological invariants as intrinsic properties of the system that persist in the thermodynamic limit. Our work sheds new light on the experimental investigation of non-Hermitian topology.

Detection of invariants in probabilistic relations between events through exploratory behaviors

Research has demonstrated that individuals are sensitive to contingent probabilistic relations between events in the environment. However, the factors that underlie this adaptation are not fully understood. Thus, the aim of this study was to assess whether repetitions and variations in participants’ responses contributed to the detection of invariance in probabilistic environments. In Experiment 1, participants were exposed to relations between the initial and final trajectories of an arrow under probabilities of 1.0, 0.9 and 0.8. In the first half of the session, the arrows followed predominantly straight trajectories, but in the second half they changed to broken ones, or vice versa. In Experiment 2, sessions began with no relation between the arrows’ initial and final trajectories (p= 0.5), but in the second half probabilities of 1.0, 0.9 and 0.8 were presented for straight or broken trajectories. In both experiments, as the relation between the arrow’s initial and final trajectories became more changeable variations of behavior increased, while under more constant relations repetitions increased. Results support the notion that adaptation to probabilistic relations between events entails exploratory behaviors performed to detect invariant information of the environment.

Optimal Reordering Trace Files for Improving Software Testing Suitcase

An invariant can be described as an essential relationship between program variables. The invariants are very useful in software checking and verification. The tools that are used to detect invariants are invariant detectors. There are two types of invariant detectors: dynamic invariant detectors and static invariant detectors. Daikon software is an available computer program that implements a special case of a dynamic invariant detection algorithm. Daikon proposes a dynamic invariant detection algorithm based on several runs of the tested program; then, it gathers the values of its variables, and finally, it detects relationships between the variables based on a simple statistical analysis. This method has some drawbacks. One of its biggest drawbacks is its overwhelming time order. It is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file. A mechanism is proposed in order to reduce differences in adjacent trace files. It is done by applying some special techniques of mutation/crossover in genetic algorithm (GA). An experiment is run to assess the benefits of this approach. Experimental findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

On the decomposability of mod 2 cohomological invariants of Weyl groups

We compute the invariants of Weyl groups in mod 2 Milnor K -theory and more general cycle modules, which are annihilated by 2. Over a base field of characteristic coprime to the group order, the invariants decompose as direct sums of the coefficient module. All basis elements are induced either by Stiefel–Whitney classes or specific invariants in the Witt ring. The proof is based on Serre’s splitting principle that guarantees detection of invariants on elementary abelian 2-subgroups generated by reflections.

Circular dichroism in higher-order harmonic generation: Heralding topological phases and transitions in Chern insulators

Topological materials are of interest to both fundamental science and advanced technologies, because topological states are robust with respect to perturbations and dissipation. Experimental detection of topological invariants is thus in great demand, but it remains extremely challenging. Ultrafast laser-matter interactions, and in particular high-harmonic generation (HHG), meanwhile, were proposed several years ago as tools to explore the structural and dynamical properties of various matter targets. Here we show that the high-harmonic emission signal produced by a circularly-polarized laser contains signatures of the topological phase transition in the paradigmatic Haldane model. In addition to clear shifts of the overall emissivity and harmonic cutoff, the high-harmonic emission shows a unique circular dichroism, which exhibits clear changes in behavior at the topological phase boundary. Our findings pave the way to understand fundamental questions about the ultrafast electron-hole pair dynamics in topological materials via non-linear high-harmonic generation spectroscopy.

A New Training Model for Object Detection in Aerial Images

This paper presents a new training model for orientation invariant object detection in aerial images by extending a deep learning based RetinaNet which is a single-stage detector based on feature pyramid networks and focal loss for dense object detection. Unlike R3Det which applies feature refinement to handle rotating objects, we proposed further improvement to cope with the densely arranged and class imbalance problems in aerial imaging, on three aspects: 1) All training images are traversed in each iteration instead of only one image in each iteration in order to cover all possibilities; 2) The learning rate is reduced if losses are not reduced; and 3) The learning rate is reduced if losses are not changed. The proposed method was calibrated and validated by comprehensive for performance evaluation and benchmarking. The experiment results demonstrate the significant improvement in comparison with R3Dec approach on the same data set. In addition to the well-known public data set DOTA for benchmarking, a new data set is also established by considering the balance between the training set and testing set. The map of losses which dropped down smoothly without jitter and overfitting also illustrates the advantages of the proposed newmodel.

Mapping of Substrate Categories Changes in Exposure to Coral Reefs Using Alos Avnir-2 and Spot 7 Images in The South Area of Kotabaru District

The South of Pulau Laut is an area in the which is included in the Coral Triangle and Aquatic Conservation Areas. The purpose of this study was to map changes in the extent of coral reefs based on the class of substrate categories and objects on exposure to coral reefs in the southern waters of Sea Island within 6 years from 2011- 2017 using SPOT 7 satellite imagery in 2017 and AVNIR ALOS-2 in 2011. Using the method of Lyzenga Dept Index for basic invariant detection of waters. Based on the results of substrate-based classification using both satellite images overlaye, there has been a decline for the coral category as much as -19.21 ha, seagrass and algae as much as -3.57 ha. On the Contrary, the category of open substrate has Increased by 22.77 ha. Object-based classification results using the Object Based Image Analysis (OBIA) method have a reduction in area for the hard coral category (HC) of -1.66 ha, hard sand mixed with sand (HC S) of -15.99 ha, dead coral overgrown with algae and sand (DCA S) of -0.39 ha, algae and seagrass (FS) covering an area of ​​-1.65 ha, sand-mixed coral fragments (RS) of 4:45 ha. On the other hand, there was an increase of in the area.

Invariant set-based robust fault detection and optimal fault estimation for discrete-time LPV systems with bounded uncertainties

ABSTRACTThis paper proposes an invariant set-based robust fault detection (FD) and optimal fault estimation (FE) method for discrete-time linear parameter varying (LPV) systems with bounded uncertainties. Firstly, a novel invariant-set construction method for discrete-time LPV systems is proposed if and only if the system is poly-quadratically stable, which need not satisfy the condition that there must exist a common quadratic Lyapunov function for all vertex matrices of the system compared to the traditional invariant-set construction methods. Furthermore, by using a shrinking procedure, we provide minimal robust positively invariant (mRPI) set approximations that are always positively invariant at each step of iteration and allow a priori desired precision to obtain a high sensitivity of FD. Owing to the existence of invariant set-based FD phase, the assumption that the initial faults should be bounded by a given set can be avoided for FE. We compute an optimal parametric matrix gain by minimising the Frobenius norm-based size of the corresponding FE set to obtain the optimal FE performance. Theoretically, any trajectory in the FE tube can be chosen as a specific-value estimation for the real fault signals. Finally, a vehicle dynamics system is used to illustrate the effectiveness of the proposed method.

Fault estimation and controller compensation in Lure systems by LPV-embedding

ABSTRACTPrevious works have considered the use of set invariance theory for fault detection and isolation in nonlinear Lure systems. This paper extends those results and proposes a new actuator fault-tolerant control approach. The fault-tolerant control scheme is designed based on linear parameter-varying (LPV) models of Lure systems. The actuator fault situation is diagnosed by an invariant set-based fault detection and isolation algorithm. Faults are compensated by adapting the controller gain based on estimates of the fault magnitude. Conditions for correct fault detection and isolation, and closed-loop stability are derived. The proposed fault-tolerant control scheme is compared with a linearised model approach and the performance of both, LPV-embedding and linearised, approaches are analysed for scalar and second-order systems. An example of a chaotic Chua circuit is also provided to illustrate the proposed fault-tolerant control scheme in higher-order systems.

Detection Performance of Packet Arrival under Downclocking for Mobile Edge Computing

Mobile edge computing (MEC) enables battery‐powered mobile nodes to acquire information technology services at the network edge. These nodes desire to enjoy their service under power saving. The sampling rate invariant detection (SRID) is the first downclocking WiFi technique that can achieve this objective. With SRID, a node detects one packet arrival at a downclocked rate. Upon a successful detection, the node reverts to a full‐clocked rate to receive the packet immediately. To ensure that a node acquires its service immediately, the detection performance (namely, the miss‐detection probability and the false‐alarm probability) of SRID is of importance. This paper is the first one to theoretically study the crucial impact of SRID attributes (e.g., tolerance threshold, correlation threshold, and energy ratio threshold) on the packet detection performance. Extensive Monte Carlo experiments show that our theoretical model is very accurate. This study can help system developers set reasonable system parameters for WiFi downclocking.

Performance Analysis of Detecting Packet Arrival for Downclocking Wi-Fi

Energy-Minimizing idle Listening (E-MiLi) is a Wi-Fi power-saving amendment using a state-of-the-art downclocking technique that reduces power consumption by lowering sampling rate. It introduces a Sampling Rate Invariant Detection (SRID) algorithm to detect packet arrival at a low clock rate. Upon a successful detection, the node reverts to a full clock rate to receive the packet immediately. In this paper, we theoretically study the crucial impact of SRID attributes (e.g., downclocking factor, correlation threshold, energy ratio threshold and tolerance threshold) on the packet detection performance, namely, the false-alarm probability and the successful detection probability. Extensive Monte Carlo results show that our performance model is very accurate.

Detecting Topological Invariants in Nonunitary Discrete-Time Quantum Walks.

We report the experimental detection of bulk topological invariants in nonunitary discrete-time quantum walks with single photons. The nonunitarity of the quantum dynamics is enforced by periodically performing partial measurements on the polarization of the walker photon, which effectively introduces loss to the dynamics. The topological invariant of the nonunitary quantum walk is manifested in the quantized average displacement of the walker, which is probed by monitoring the photon loss. We confirm the topological properties of the system by observing localized edge states at the boundary of regions with different topological invariants. We further demonstrate the robustness of both the topological properties and the measurement scheme of the topological invariants against disorder.

Detection of Zak phases and topological invariants in a chiral quantum walk of twisted photons

Topological insulators are fascinating states of matter exhibiting protected edge states and robust quantized features in their bulk. Here we propose and validate experimentally a method to detect topological properties in the bulk of one-dimensional chiral systems. We first introduce the mean chiral displacement, an observable that rapidly approaches a value proportional to the Zak phase during the free evolution of the system. Then we measure the Zak phase in a photonic quantum walk of twisted photons, by observing the mean chiral displacement in its bulk. Next, we measure the Zak phase in an alternative, inequivalent timeframe and combine the two windings to characterize the full phase diagram of this Floquet system. Finally, we prove the robustness of the measure by introducing dynamical disorder in the system. This detection method is extremely general and readily applicable to all present one-dimensional platforms simulating static or Floquet chiral systems.

RST Invariant Image Forgery Detection

Background/Objectives: Copy-Move Forgery Detection (CMFD) is a very prevalent approach used to detect copy and pasted portions of the same image. The copied portion is rotated, flipped or scaled. The detection method should be invariant to rotation, scaling and translation. Many CMFD methods came into existence; however, some methods fail to withstand attacks such as Contrast adjustment, Gaussian blur and JPEG Compression. Although the methods are able to resist the attacks, they are computationally complex. This paper proposes a Rotation, Scaling, Translation (RST) invariant image forgery detection. Methods: Local Binary Pattern (LBP) is applied on the low frequency content of Discrete Wavelet Transform (DWT) decomposed image for feature extraction. Findings: The proposed method is invariant to rotation, scaling and translation attacks on the pasted portions of the image and able to resist post-processing attacks and has low computational effort. It is evaluated qualitatively and quantitatively on a CASIA database. Morphological operations are performed to reduce the false alarms. The correct detection ratio is in the range of 80% to 99% and false detection ratio in the range of 7% to 30%. Applications: There is a great demand to detect the forgery, which aids in the digital forensic analysis, in legal document substantiation, and various other fields.

How the brain detects invariance and inhibits variance during category induction

A stepwise category-learning task was designed to examine brain activation associated with invariance detection and variance inhibition during category induction (CI). Three stimuli were displayed sequentially and participants were asked to learn the target category based on the invariant feature among stimuli. The processes of invariance detection and variance inhibition were necessary during certain events; however, these processes were not required for other events. Functional magnetic resonance imaging (fMRI) results indicated that the processes of detecting invariant features and inhibiting variant features were associated with significant activation in the left prefrontal cortex, including the left superior frontal gyrus, middle frontal gyrus, and mid-ventrolateral prefrontal cortex, as well as other regions (e.g., bilateral parietal cortex and putamen). These findings confirmed the important role of the fronto-parietal network and striatum in the invariance detection of category learning.

Unraveling the nature of autism: finding order amid change.

In this article, we hypothesize that individuals with autism spectrum disorder (ASD) are born with a deficit in invariance detection, which is a learning process whereby people and animals come to attend the relatively stable patterns or structural regularities in the changing stimulus array. This paper synthesizes a substantial body of research which suggests that a deficit in the domain-general perceptual learning process of invariant detection in ASD can lead to a cascade of consequences in different developmental domains. We will outline how this deficit in invariant detection can cause uncertainty, unpredictability, and a lack of control for individuals with ASD and how varying degrees of impairments in this learning process can account for the heterogeneity of the ASD phenotype. We also describe how differences in neural plasticity in ASD underlie the impairments in perceptual learning. The present account offers an alternative to prior theories and contributes to the challenge of understanding the developmental trajectories that result in the variety of autistic behaviors.

Toward shift invariant detection of event-related potentials in non-invasive brain-computer interface

Non-invasive brain-computer interface (BCI) is a relatively new type of human-computer interaction. BCIs that are based on the detection of event-related potentials (ERPs) are usually synchronous. They require the knowledge of the stimulus onsets that evoke ERPs, which is time locked to the presence of a potentially relevant stimulus. The detection of ERPs like the P300 has been successfully used in BCI thanks to the oddball paradigm. The time locked detection is directly related to the synchronous aspect of a BCI. However, asynchronous detection is a critical issue in developing BCIs for real-life applications, where the machine should be able to detect the presence of an ERP independently from the knowledge of the stimulus onsets, or when wireless devices do not allow a precise knowledge of the stimulus onsets. Although the detection of single-trial ERP is already a challenge, when the stimulus onsets are well identified, we propose to investigate further the detection of single-trial ERP by considering different time locked stimuli. We propose and compare shift invariant ERP detection strategies on data from ten subjects obtained in a P300 speller experiment. With a shift invariant distance, we show that it is possible to obtain an AUC of 0.834 while allowing a jitter of ± 40 ms. With inputs in the Fourier domain, the mean area under the ROC curves of 0.683 allowing a jitter of ± 200 ms in the stimulus onsets. The results support the conclusion that ERP detection can be achieved without a precise knowledge of the stimulus onsets, and hence can be used with EEG amplifiers that do not allow a precise synchronization between the EEG signal and stimulus onsets.