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Bryophytes are the second largest group of land plants after angiosperms. There is very less knowledge available about medicinal properties of these plants. Bryophytes are popular remedy among the tribal people of different parts of the world. Tribal people use these plants to cure various ailments in their daily lives. Bryophytes are used to cure hepatic disorders, skin diseases, cardiovascular diseases, used as antipyretic, antimicrobial, wound healing and many more other ailments by different tribal communities of Africa, America, Europe, Poland, Argentina, Australia, New Zealand, Turkey, Japan, Taiwan, Pakistan, China, Nepal and different parts of South, North and Eastern India. Apart from ethno-medicinal uses some bryophytes possesses antitumor activities against different cancer cell lines and this property of bryophytes needs to be more focused in the future. Compile information about medicinal properties and anticancer properties of bryophytes is lacking till date. In the present review, the authors tried to compile all the ethno-medicinal and other related information of bryophytes and fill the knowledge lacuna in this particular field. Some published reviews are available but the information is segregated. This manuscript will help people doing research in the bryophytes.

This article, titled "Editorial: Rhizosphere Manipulation for Sustainable Agriculture," discusses the challenges faced by the agriculture industry in increasing productivity while minimizing the negative effects on ecosystems and public health. The use of chemical pesticides and fertilizers is expensive and harmful to the environment, so there is a need for sustainable agricultural practices. The article highlights the importance of studying the rhizosphere, the soil microbiota that plays a crucial role in plant growth and development. The manipulation of the rhizosphere has the potential to improve agricultural productivity and environmental sustainability. The article also mentions specific studies on the role of non-rhizobial endophytes in promoting plant growth and the use of rhizosphere engineering to increase agricultural yield. Overall, the article emphasizes the importance of rhizosphere manipulation in modern agriculture to increase output and sustainability. [Extracted from the article]

The main ionospheric trough (MIT) is a salient density feature in the mid‐latitude ionosphere and characterizing its structure is important for understanding Global Positioning System and HF signal propagation, and identifying geospace phenomena such as the plasmapause boundary layer. While a number of previous studies have statistically investigated the properties of the MIT utilizing low‐altitude satellite observations, they have been limited to latitudinal cross sections, and have not considered the inherent two‐dimensional structure of the MIT. In this work, we develop a regularized inversion method for identifying the two dimensional structure of the MIT in Total Electron Content maps. Because no ground truth labels exist for the MIT, we extensively characterize the behavior of the algorithm by comparing it to the method developed by Aa, Zou, et al. (2020, doi:https://doi.org/10.1029/2019JA027583). We show that statistics computed on the resulting labels are robust to our choice of algorithm parameters and that we are able to match the results of Aa, Zou, et al. (2020, doi:https://doi.org/10.1029/2019JA027583) with a particular selection of the parameters. In addition to enabling fundamentally different studies, our MIT labels are able to provide statistical MIT properties with higher resolution. Code to reproduce our data set is provided in a GitHub repository: https://github.com/gregstarr/trough. Plain Language Summary: The Earth's ionized outer atmosphere (the ionosphere) serves as the interface between our atmosphere and the near space environment. Signals from Global Positioning System satellites contain information about the total integrated plasma density (referred to as "Total Electron Content," (TEC)) between a given satellite and a given ground receiver. Measurements of TEC from large networks of ground receivers allow us to construct global images of the ionosphere, and thus study its variations in space and time. This paper uses such ionospheric images over a 20 year period to investigate a large‐scale coherent density depletion in the ionosphere known as the "mid‐latitude ionospheric trough" (MIT). In this work we have identified and labeled MIT regions in each images using computer vision techniques, and stored the results in a new image database. Using this "labeled data," we can gain insight into the average location, morphology, and occurrence rates of the MIT, an examine this phenomenon varies with season, time of day, geomagnetic activity, and other correlated parameters. Key Points: A method for identifying the main ionospheric trough (MIT) in global Total Electron Content images is developed and validatedStatistics extracted from a 10‐year data set are found to be in good agreement with a SWARM‐derived satellite modelOur technique enables statistical analyzes of two‐dimensional (lat, lon) MIT properties with high resolution [ABSTRACT FROM AUTHOR]

A general distributed field reconstruction problem is studied by using a dense network of noisy one-bit randomized scalar quantizers in the presence of additive observation noise of unknown distribution. A general condition is derived for the optimization of the rate of decay of the mean squared error (MSE) upper-bound with sensor density.

A broad set of sufficient conditions that guarantees the existence of the maximum entropy (maxent) distribution consistent with specified bounds on certain generalized moments is derived. Most results in the literature are either focused on the minimum cross-entropy distribution or apply only to distributions with a bounded-volume support or address only equality constraints. The results of this work hold for general moment inequality constraints for probability distributions with possibly unbounded support, and the technical conditions are explicitly on the underlying generalized moment functions. An analytical characterization of the maxent distribution is also derived using results from the theory of constrained optimization in infinite-dimensional normed linear spaces. Several auxiliary results of independent interest pertaining to certain properties of convex coercive functions are also presented. Index Terms--Coercive functions, constrained optimization, convex analysis, cross-entropy, differential entropy, maximum entropy methods.

The novelty of reversing the order of encrypting and compressing the data without compromising either the compression efficiency or the information-theoretic security is described. It is proved that through the use of coding with side information principles, the reversal of order of encrypting and compressing the data is possible in some settings of interest, without loss of either optimal coding efficiency or perfect secrecy.

An equivalence is established between two models of signal estimation under modeling uncertainty. The methods are: set-theoretic estimation; and maximum entropy (maxent) MAP estimation.

Crowdcoding, a method that outsources "coding" tasks to numerous people on the internet, has emerged as a popular approach for annotating texts and visuals. However, the performance of this approach for analyzing social media data in the context of journalism and mass communication research has not been systematically assessed. This study evaluated the validity and efficiency of crowdcoding based on the analysis of 4,000 tweets about the 2016 U.S. presidential election. The results show that compared with the traditional quantitative content analysis, crowdcoding yielded comparably valid results and was superior in efficiency, but was more expensive under most circumstances. [ABSTRACT FROM AUTHOR]

Most motion-compensation methods dealt with in the literature make strong assumptions about the smoothness of the underlying motion field. For instance, block-matching algorithms assume a blockwise-constant motion field and are adequate for translational motion models; control-grid interpolation assumes a blockwise bilinear motion field and captures zooming and warping fairly well. Time-varying imagery, however, often contains both types of motion (as well as others), and hence exhibits a high degree of spatial variability of its motion-field smoothness properties. We develop a simple method to spatially adapt the smoothness of the motion field. The proposed method demonstrates substantial improvements in video quality over a wide range of bit rates. To this end, we introduce the notion of a motion field that is characterized by a set of labels. The labels provide the flexibility to adaptively switch between two different motion models locally. The individual motion models have very different smoothness properties. The switched framework for motion compensation performs significantly better than each of its constituent motion models, in terms of both visual quality and signal-to-noise ratio (0.3--0.7 dB on the average). Finally, we develop an extension of this method that enhances the overlapped block motion compensation scheme by allowing spatial adaptation of the window function. Index Terms--Block matching, interpolation methods, motion estimation, overlapped block motion compensation, video compression.

We develop necessary and sufficient conditions and a novel provably consistent and efficient algorithm for discovering topics (latent factors) from observations (documents) that are realized from a probabilistic mixture of shared latent factors that have certain properties. Our focus is on the class of topic models in which each shared latent factor contains a novel word that is unique to that factor, a property that has come to be known as separability. Our algorithm is based on the key insight that the novel words correspond to the extreme points of the convex hull formed by the row-vectors of a suitably normalized word co-occurrence matrix. We leverage this geometric insight to establish polynomial computational and sample complexity bounds based on a few isotropic random projections of the rows of the normalized word co-occurrence matrix. Our proposed random-projections-based algorithm is naturally amenable to an efficient distributed implementation and is attractive for modern web-scale distributed data mining applications. [ABSTRACT FROM PUBLISHER]

This article presents an empirical study that investigated and compared two “big data” text analysis methods: dictionary-based analysis, perhaps the most popular automated analysis approach in social science research, and unsupervised topic modeling (i.e., Latent Dirichlet Allocation [LDA] analysis), one of the most widely used algorithms in the field of computer science and engineering. By applying two “big data” methods to make sense of the same dataset—77 million tweets about the 2012 U.S. presidential election—the study provides a starting point for scholars to evaluate the efficacy and validity of different computer-assisted methods for conducting journalism and mass communication research, especially in the area of political communication. [ABSTRACT FROM AUTHOR]

Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90 000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries—an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website1 and will be updated with feedback from academia and industry in the future. 1

www.ChangeDetection.net [ABSTRACT FROM AUTHOR]

Biometrics are an important and widely used class of methods for identity verification and access control. Biometrics are attractive because they are inherent properties of an individual. They need not be remembered like passwords and are not easily lost or forged like identifying documents. At the same time, biometrics are fundamentally noisy and irreplaceable. There are always slight variations among the measurements of a given biometric, and, unlike passwords or identification numbers, biometrics are derived from physical characteristics that cannot easily be changed. The proliferation of biometric usage raises critical privacy and security concerns that, due to the noisy nature of biometrics, cannot be addressed using standard cryptographic methods. In this article, we present an overview of secure biometrics, also referred to as biometric template protection, an emerging class of methods that address these concerns. [ABSTRACT FROM PUBLISHER]

A two-terminal interactive function computation problem with alternating messages is studied within the framework of distributed block source coding theory. For any finite number of messages, a single-letter characterization of the sum-rate-distortion function was established in previous works using standard information-theoretic techniques. This, however, does not provide a satisfactory characterization of the infinite-message limit, which is a new, unexplored dimension for asymptotic analysis in distributed block source coding involving potentially an infinite number of infinitesimal-rate messages. In this paper, the infinite-message sum-rate-distortion function, viewed as a functional of the joint source distribution and the distortion levels, is characterized as the least element of a partially ordered family of functionals having certain convex-geometric properties. The new characterization does not involve evaluating the infinite-message limit of a finite-message sum-rate-distortion expression. This characterization leads to a family of lower bounds for the infinite-message sum-rate-distortion expression and a simple criterion to test the optimality of any achievable infinite-message sum-rate-distortion expression. The new convex-geometric characterization is used to develop an iterative algorithm for evaluating any finite-message sum-rate-distortion function. It is also used to construct the first examples which demonstrate that for lossy source reproduction, two messages can strictly improve the one-message Wyner–Ziv rate-distortion function settling an unresolved question from a 1985 paper. It is shown that a single backward message of arbitrarily small rate can lead to an arbitrarily large gain in the sum-rate. [ABSTRACT FROM AUTHOR]

We present a theoretical framework for the analysis of privacy and security trade-offs in secure biometric authentication systems. We use this framework to conduct a comparative information-theoretic analysis of two biometric systems that are based on linear error correction codes, namely fuzzy commitment and secure sketches. We derive upper bounds for the probability of false rejection (PFR) and false acceptance (PFA) for these systems. We use mutual information to quantify the information leaked about a user's biometric identity, in the scenario where one or multiple biometric enrollments of the user are fully or partially compromised. We also quantify the probability of successful attack (PSA) based on the compromised information. Our analysis reveals that fuzzy commitment and secure sketch systems have identical PFR, PFA, PSA, and information leakage, but secure sketch systems have lower storage requirements. We analyze both single-factor (keyless) and two-factor (key-based) variants of secure biometrics, and consider the most general scenarios in which a single user may provide noisy biometric enrollments at several access control devices, some of which may be subsequently compromised by an attacker. Our analysis highlights the revocability and reusability properties of key-based systems and exposes a subtle design trade-off between reducing information leakage from compromised systems and preventing successful attacks on systems whose data have not been compromised. [ABSTRACT FROM PUBLISHER]

A problem of interactive function computation in a collocated network is studied in a distributed block source coding framework. With the goal of computing samples of a desired function of sources at the sink, the source nodes exchange messages through a sequence of error-free broadcasts. For any function of independent sources, a computable characterization of the set of all feasible message coding rates—the rate region—is derived in terms of single-letter information measures. In the limit as the number of messages tends to infinity, the infinite-message minimum sum rate, viewed as a functional of the joint source probability mass function, is characterized as the least element of a partially ordered family of functionals having certain convex-geometric properties. This characterization leads to a family of lower bounds for the infinite-message minimum sum rate and a simple criterion to test the optimality of any achievable infinite-message sum rate. An iterative algorithm for evaluating the infinite-message minimum sum-rate functional is proposed and is demonstrated through an example of computing the minimum function of three Bernoulli sources. Based on the characterizations of the rate regions, it is shown that when computing symmetric functions of binary sources, the sink will inevitably learn certain additional information that is not demanded in computing the function. This conceptual understanding leads to new improved bounds for the minimum sum rate. The new bounds are shown to be orderwise better than those based on cut-sets as the network scales. The scaling law of the minimum sum rate is explored for different classes of symmetric functions and source parameters. [ABSTRACT FROM PUBLISHER]

A two-terminal interactive distributed source coding problem with alternating messages for function computation at both locations is studied. For any number of messages, a computable characterization of the rate region is provided in terms of single-letter information measures. While interaction is useless in terms of the minimum sum-rate for lossless source reproduction at one or both locations, the gains can be arbitrarily large for function computation even when the sources are independent. For a class of sources and functions, interaction is shown to be useless, even with infinite messages, when a function has to be computed at only one location, but is shown to be useful, if functions have to be computed at both locations. For computing the Boolean AND function of two independent Bernoulli sources at both locations, an achievable infinite-message sum-rate with infinitesimal-rate messages is derived in terms of a 2-D definite integral and a rate-allocation curve. The benefit of interaction is highlighted in multiterminal function computation problem through examples. For networks with a star topology, multiple rounds of interactive coding is shown to decrease the scaling law of the total network rate by an order of magnitude as the network grows. [ABSTRACT FROM AUTHOR]

Motivated by video coding applications, the problem of sequential coding of correlated sources with encoding and/or decoding frame-delays is studied. The fundamental tradeoffs between individual frame rates, individual frame distortions, and encoding/decoding frame-delays are derived in terms of a single-letter information-theoretic characterization of the rate-distortion region for general interframe source correlations and certain types of potentially frame specific and coupled single-letter fidelity criteria. The sum-rate-distortion region is characterized in terms of generalized directed information measures highlighting their role in delayed sequential source coding problems. For video sources which are spatially stationary memoryless and temporally Gauss–Markov, MSE frame distortions, and a sum-rate constraint, our results expose the optimality of idealized differential predictive coding among all causal sequential coders, when the encoder uses a positive rate to describe each frame. Somewhat surprisingly, causal sequential encoding with one-frame-delayed noncausal sequential decoding can exactly match the sum-rate-MSE performance of joint coding for all nontrivial MSE-tuples satisfying certain positive semidefiniteness conditions. Thus, even a single frame-delay holds potential for yielding significant performance improvements. Generalizations to higher order Markov sources are also presented and discussed. A rate-distortion performance equivalence between, causal sequential encoding with delayed noncausal sequential decoding, and delayed noncausal sequential encoding with causal sequential decoding, is also established. [ABSTRACT FROM AUTHOR]

The problem of sampling a discrete-time sequence of spatially bandlimited fields, with a bounded dynamic range, in a distributed, communication-constrained processing environment is studied. A central unit having access to the data gathered by a dense network of low-precision sensors, is required to reconstruct the field snapshots to maximum accuracy. Both deterministic and stochastic field models are considered. For stochastic fields, results are established in the almost-sure sense. The feasibility of having a flexible tradeoff between the oversampling rate (sensor density) and the analog-to-digital converter (ADC) precision, while achieving an exponential accuracy in the number of bits per Nyquist-interval per snapshot is demonstrated. This exposes an underlying “conservation of bits” principle: the bit-budget per Nyquist-interval per snapshot (the rate) can be distributed along the amplitude axis (sensor-precision) and space (sensor density) in an almost arbitrary discrete-valued manner, while retaining the same (exponential) distortion-rate characteristics. [ABSTRACT FROM AUTHOR]

The classical method for sampling a smooth non-bandlimited signal requires a lowpass anti-aliasing filter. In applications like distributed sampling where sampling and quantization operations precede filtering, aliasing-error is inevitable. Motivated by such applications, the sampling of smooth and bounded non-bandlimited signals whose spectra have a finite absolute first moment, without the use of an analog anti-alias lowpass filter, is studied in a centralized setup. Upper bounds for the distortion-rate function are derived by first upper-bounding the distortion with a linear combination of errors due to aliasing and quantization and then balancing their contributions by selecting an appropriate reconstruction bandwidth. For a class of dithered sampling methods, it is shown that a lower quantizer-precision can be traded for a higher sampling-rate without affecting the realizable high-rate asymptotic distortion-rate characteristics. These results are applied to signals with exponentially and polynomially decaying spectral characteristics and truncated bandlimited signals to uncover the realizable distortion-rate characteristics for these signal classes. [ABSTRACT FROM AUTHOR]

Efficient browsing of long video sequences is a key tool in visual surveillance, e.g., for postevent video forensics, but can also be used for fast review of motion pictures and home videos. While frame skipping (fixed or adaptive) is straightforward to implement, its performance is quite limited. Although more efficient techniques have been developed, such as video summarization and video montage, they lose either the temporal or semantic context of events. A recently proposed method called video synopsis deals with some of these issues but involves multiple processing stages and is fairly complex. Video condensation, that we propose here, is novel in the way information is removed from the space-time video volume, is conceptually simple and relatively easy to implement. We introduce the concept of a video ribbon inspired by that of a seam recently proposed for image resizing. We recursively carve ribbons out by minimizing an activity-aware cost function using dynamic programming. The ribbon model we develop is flexible and permits an easy adjustment of the compromise between temporal condensation ratio and anachronism of events. We also propose sliding-window ribbon carving to handle streaming video and demonstrate the method's efficiency on motor and pedestrian traffic data. [ABSTRACT FROM AUTHOR]

The reconstruction of a bounded deterministic field from binary-quantized observations of sensors which are randomly deployed over the field domain is studied. The sensor observations are corrupted by bounded additive noise. The study focuses on the extremes of lack of deterministic control in the sensor deployment, lack of knowledge of the noise distribution, and lack of sensing precision and reliability. Such adverse conditions are motivated by possible real-world scenarios where a large collection of low-cost, crudely manufactured sensors are mass-deployed in an environment where little can be assumed about the ambient noise. A simple estimator that reconstructs the entire field from these unreliable, binary-quantized, noisy observations is proposed. Technical conditions for the almost sure and mean squared error (MSE) convergence of the estimate to the field, as the number of sensors tends to infinity, are derived and their implications are discussed. For finite-dimensional, bounded-variation, and Sobolev-differentiable function classes, specific MSE decay rates are derived. [ABSTRACT FROM AUTHOR]

We study the problem of estimating a physical process at a central processing unit (CPU) based on noisy measurements collected from a distributed, bandwidth-constrained, unreliable, network of sensors, modeled as an erasure network of unreliable "bit-pipes" between each sensor and the CPU. The CPU is guaranteed to receive data from a minimum fraction of the sensors and is tasked with optimally estimating the physical process under a specified distortion criterion. We study the noncollaborative (i.e., fully distributed) sensor network regime, and derive an information-theoretic achievable rate-distortion region for this network based on distributed source-coding insights. Specializing these results to the Gaussian setting and the mean-squared-error (MSE) distortion criterion reveals interesting robust-optimality properties of the solution. We also study the regime of clusters of collaborative sensors, where we address the important question: given a communication rate constraint between the sensor clusters and the CPU, should these clusters transmit their "raw data" or some low-dimensional "local estimates"? For a broad set of distortion criteria and sensor correlation statistics, we derive conditions under which rate-distortion-optimal compression of correlated cluster-observations separates into the tasks of dimension-reducing local estimation followed by optimal distributed compression of the local estimates. [ABSTRACT FROM AUTHOR]

A correction to the article "On Delayed Sequential Coding of Correlated Sources" that was published in previous issue is presented.

Background: Although speech-language therapy (SLT) is proven to be beneficial to recovery of post-stroke aphasia, delivering sufficiently high amounts of dosage remains a problem in real-world clinical practice. Self-managed SLT was introduced to solve the problem. Previous research showed in a 10-week period, increased dosage frequency could lead to better performance, however, it is uncertain if dosage still affects performance over a longer period of practice time and whether gains can be seen following practice over several months., Objective: This study aims to evaluate data from a health app (Constant Therapy) to investigate the relationship between dosage amount and improvements following a 30-week treatment period. Two cohorts of users were analyzed. One was comprised of patients with a consistent average weekly dosage amount and the other cohort was comprised of users whose practice had higher variability., Methods: We conducted two analyses with two cohorts of post-stroke patients who used Constant Therapy. The first cohort contains 537 "consistent" users, while the second cohort contains 2,159. The 30-week practice period was split into three consecutive 10-week practice windows to calculate average dosage amount. In each 10-week practice period, patients were grouped by their average dosage into low (0-15 min/week), medium (15-40 min/week) and moderate dosage (greater than 40 min/week) groups. Linear mixed-effects models were employed to evaluate if dosage amount was a significant factor affecting performance. Pairwise comparison was also applied to evaluate the slope difference between groups., Results: For the consistent cohort, medium ( β  =   .002, t 17,700  = 7.64, P  < .001) and moderate ( β  =   .003, t 9,297  = 7.94, P  < .001) dosage groups showed significant improvement compared to the low dosage group. The moderate group also showed greater improvement compared to the medium group. For the variable cohort in analysis 2, the same trend was shown in the first two 10-week windows, however, in weeks 21-30, the difference was insignificant between low and medium groups ( β  =   .001, t  = 1.76, P  = .078)., Conclusions: This study showed a higher dosage amount is related to greater therapy outcomes in over 6 months of digital self-managed therapy. It also showed that regardless of the exact pattern of practice, self-managed SLT leads to significant and sustained performance gains., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Liu, Cordella, Ishwar, Betke and Kiran.)

Secondary metabolites have potential benefits to human being. They are used in the food, agricultural and pharmaceutical industries. The secondary metabolite of furanocoumarins from different plant sources is essential in various skin-related ailments. Biologically, these chemicals are isolated from different plants in the Apiaceae, Fabaceae, Rutaceae and Moraceae families. Ammi majus L. is one of the most common plants in the family of Apiaceae with a large quantity of derivatives. The furanocoumarin derivatives defend the plant by fighting external enemies by Systemic Acquired Resistance (SAR). Via suppressing or retarding microbial growth in infected parts, these derivatives, along with SAR, help to alleviate inflammation in the human body. Latest evidence of these compounds has been established in the treatment of cancer, but the mechanism that needs to be elaborated is not yet understood. Recent studies have shown that furanocoumarin derivatives bind to DNA base pairs and block DNA replication. This may be a potential pathway that helps to regulate the growth of cancerous cells. This article reflects on the pharmaceutical data of furanocoumarins and their different mechanisms in these cases., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Bryophytes are the second largest group of land plants after angiosperms. There is very less knowledge available about medicinal properties of these plants. Bryophytes are popular remedy among the tribal people of different parts of the world. Tribal people use these plants to cure various ailments in their daily lives. Bryophytes are used to cure hepatic disorders, skin diseases, cardiovascular diseases, used as antipyretic, antimicrobial, wound healing and many more other ailments by different tribal communities of Africa, America, Europe, Poland, Argentina, Australia, New Zealand, Turkey, Japan, Taiwan, Pakistan, China, Nepal and different parts of South, North and Eastern India. Apart from ethno-medicinal uses some bryophytes possesses antitumor activities against different cancer cell lines and this property of bryophytes needs to be more focused in the future. Compile information about medicinal properties and anticancer properties of bryophytes is lacking till date. In the present review, the authors tried to compile all the ethno-medicinal and other related information of bryophytes and fill the knowledge lacuna in this particular field. Some published reviews are available but the information is segregated. This manuscript will help people doing research in the bryophytes.

In recent years, baggage screening at airports has included the use of dual-energy X-ray computed tomography (DECT), an advanced technology for nondestructive evaluation. The main challenge remains to reliably find and identify threat objects in the bag from DECT data. This task is particularly hard due to the wide variety of objects, the high clutter, and the presence of metal, which causes streaks and shading in the scanner images. Image noise and artifacts are generally much more severe than in medical CT and can lead to splitting of objects and inaccurate object labeling. The conventional approach performs object segmentation and material identification in two decoupled processes. Dual-energy information is typically not used for the segmentation, and object localization is not explicitly used to stabilize the material parameter estimates. We propose a novel learning-based framework for joint segmentation and identification of objects directly from volumetric DECT images, which is robust to streaks, noise and variability due to clutter. We focus on segmenting and identifying a small set of objects of interest with characteristics that are learned from training images, and consider everything else as background. We include data weighting to mitigate metal artifacts and incorporate an object boundary field to reduce object splitting. The overall formulation is posed as a multilabel discrete optimization problem and solved using an efficient graph-cut algorithm. We test the method on real data and show its potential for producing accurate labels of the objects of interest without splits in the presence of metal and clutter.

Despite a significant growth in the last few years, the availability of 3D content is still dwarfed by that of its 2D counterpart. To close this gap, many 2D-to-3D image and video conversion methods have been proposed. Methods involving human operators have been most successful but also time-consuming and costly. Automatic methods, which typically make use of a deterministic 3D scene model, have not yet achieved the same level of quality for they rely on assumptions that are often violated in practice. In this paper, we propose a new class of methods that are based on the radically different approach of learning the 2D-to-3D conversion from examples. We develop two types of methods. The first is based on learning a point mapping from local image/video attributes, such as color, spatial position, and, in the case of video, motion at each pixel, to scene-depth at that pixel using a regression type idea. The second method is based on globally estimating the entire depth map of a query image directly from a repository of 3D images ( image+depth pairs or stereopairs) using a nearest-neighbor regression type idea. We demonstrate both the efficacy and the computational efficiency of our methods on numerous 2D images and discuss their drawbacks and benefits. Although far from perfect, our results demonstrate that repositories of 3D content can be used for effective 2D-to-3D image conversion. An extension to video is immediate by enforcing temporal continuity of computed depth maps.

We propose a general framework for fast and accurate recognition of actions in video using empirical covariance matrices of features. A dense set of spatio-temporal feature vectors are computed from video to provide a localized description of the action, and subsequently aggregated in an empirical covariance matrix to compactly represent the action. Two supervised learning methods for action recognition are developed using feature covariance matrices. Common to both methods is the transformation of the classification problem in the closed convex cone of covariance matrices into an equivalent problem in the vector space of symmetric matrices via the matrix logarithm. The first method applies nearest-neighbor classification using a suitable Riemannian metric for covariance matrices. The second method approximates the logarithm of a query covariance matrix by a sparse linear combination of the logarithms of training covariance matrices. The action label is then determined from the sparse coefficients. Both methods achieve state-of-the-art classification performance on several datasets, and are robust to action variability, viewpoint changes, and low object resolution. The proposed framework is conceptually simple and has low storage and computational requirements making it attractive for real-time implementation.