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Studies on the Quantum Private Query Primitive in the Device-Independent Paradigm
No full textIn this thesis, we focus on the Quantum Private Query (QPQ) primitive in the device-independent (DI) paradigm, addressing the challenges of preserving user and database privacy without trusting the devices. Existing cryptographic primitives, such as Symmetric Private Information Retrieval (SPIR) and 1 out of N Oblivious Transfer (OT), lack unconditional security with a single server in both classical and quantum domains. The QPQ primitive addresses this limitation by allowing the client to gain probabilistic knowledge about unintended data bits while expecting the server not to cheat if a non-zero probability exists of being caught. The contributions of this thesis include proposing and analyzing QPQ schemes within the DI framework. We introduce a novel QPQ scheme using EPR pairs, exploiting self-testing of shared Bell states, projective measurement operators, and a specific class of POVM operators to achieve complete device independence. We address the limitations of a semi-DI-QPQ proposal and utilize the tilted version of the actual CHSH game and self-testing of observables to enhance security and certify full device independence. Furthermore, we suggest several strategies to reduce the overall sample size required for DI testing of that semi-DI-QPQ proposal in the finite sample scenario. Moreover, we address the limitations of the existing multi-user QPQ schemes and propose a semi-DI multi-user QPQ scheme where each user can retrieve different items simultaneously without revealing their choices to others or relying on a semi-trusted server. We formally conduct security assessments for all our DI-QPQ proposals and derive upper limits on the cheating probabilities to ensure robust DI-QPQ implementations. Overall, in this thesis, we contribute to advancing the QPQ primitive in the DI paradigm, offering novel schemes and addressing the challenges posed by distrustful settings and multi-user scenarios
Regularity of 3-Path Ideals of Trees and Unicyclic Graphs
No full textLet G be a simple graph and I3(G) be its 3-path ideal in the corresponding polynomial ring R. In this article, we prove that for an arbitrary graph G, reg (R/ I3(G)) is bounded below by 2 ν3(G) , where ν3(G) denotes the 3-path induced matching number of G. We give a class of graphs, namely trees for which the lower bound is attained. Also, for a unicyclic graph G, we show that reg (R/ I3(G)) ≤ 2 ν3(G) + 2 and provide an example that shows that the given upper bound is sharp
Revealing soil microbial ecophysiological indicators in acidic environments laden with heavy metals via predictive modeling: Understanding the impacts of black diamond excavation
No full textAppraising the activity of soil microbial community in relation to soil acidity and heavy metal (HM) content can help evaluate it\u27s quality and health. Coal mining has been reported to mobilize locked HM in soil and induce acid mine drainage. In this study, agricultural soils around coal mining areas were studied and compared to baseline soils in order to comprehend the former\u27s effect in downgrading soil quality. Acidity as well as HM fractions were significantly higher in the two contaminated zones as compared to baseline soils (p \u3c 0.01). Moreover, self-organizing and geostatistical maps show a similar pattern of localization in metal availability and soil acidity thereby indicating a causal relationship. Sobol sensitivity, cluster, and principal component analyses were employed to enunciate the relationship between the various metal and acidity fractions with that of soil microbial properties. The results indicate a significant negative impact of metal bioavailability, and acidity on soil microbial activity. Lastly, Taylor diagrams were employed to predict soil microbial quality and health based on soil physicochemical inputs. The efficiency of several machine learning algorithms was tested to identify Random Forrest as the best model for prediction. Thus, the study imparts knowledge about soil pollution parameters, and acidity status thereby projecting soil quality which can be a pioneer in sustainable agricultural practices
Robust estimation of average treatment effects from panel data
No full textIn order to evaluate the impact of a policy intervention on a group of units over time, it is important to correctly estimate the average treatment effect (ATE) measure. Due to lack of robustness of the existing procedures of estimating ATE from panel data, in this paper, we introduce a robust estimator of the ATE and the subsequent inference procedures using the popular approach of minimum density power divergence inference. Asymptotic properties of the proposed ATE estimator are derived and used to construct robust test statistics for testing parametric hypotheses related to the ATE. Besides asymptotic analyses of efficiency and power, extensive simulation studies are conducted to study the finite-sample performances of our proposed estimation and testing procedures under both pure and contaminated data. The robustness of the ATE estimator is further investigated theoretically through the influence function analyses. Finally our proposal is applied to study the long-term economic effects of the 2004 Indian Ocean earthquake and tsunami on the (per-capita) gross domestic products (GDP) of five mostly affected countries, namely Indonesia, Sri Lanka, Thailand, India and Maldives
Selling to a manager and a budget-constrained agent
No full textWe analyze a model of selling a single object to a manager-agent pair who want to acquire the object for a firm. The manager and the agent have different assessments of the object\u27s value to the firm. The agent is budget-constrained while the manager is not. The agent participates in the mechanism, but she can (strategically) approach the manager for decision-making. We derive the revenue-maximizing mechanism in a two-dimensional type space (values of the agent and the manager). We show that below a threshold budget, a mechanism involving two posted prices and three outcomes (one of which involves randomization) is the optimal mechanism for the seller. Otherwise, a single posted price mechanism is optimal
Silver nanoparticles in plant health: Physiological response to phytotoxicity and oxidative stress
No full textSilver nanoparticles (AgNPs) have gained significant attention in various fields due to their unique properties, but their release into the environment has raised concerns about their environmental and biological impacts. Silver nanoparticles can enter plants following their exposure to roots or via stomata following foliar exposure. Upon penetrating the plant cells, AgNPs interact with cellular components and alter physiological and biochemical processes. One of the key concerns associated with plant exposure to AgNPs is the potential of these materials to induce oxidative stress. Silver nanoparticles can also suppress plant growth and development by disrupting essential plant physiological processes, such as photosynthesis, nutrient uptake, water transport, and hormonal regulation. In crop plants, these disruptions may, in turn, affect the productivity and quality of the harvested components and therefore represent a potential threat to agricultural productivity and ecosystem stability. Understanding the phytotoxic effects of AgNPs is crucial for assessing their environmental implications and guiding the development of safe nanomaterials. By delving into the phytotoxic effects of AgNPs, this review contributes to the existing knowledge regarding their environmental risks and promotes the advancement of sustainable nanotechnological practices
Simultaneous Confidence Intervals for Multi-way Clustered Stock Return Data
No full textThe purpose of this study is to investigate how returns of stocks in different sectors are affected by the months of the year. An exploratory data analysis is performed to study the impact of months on different stocks. The findings encourage us to construct confidence intervals first by considering the fact that the residuals of the model exhibit correlation either across stocks or across months. We next build simultaneous confidence intervals by taking into account the correlation across both stocks and months on the residuals. This type of study can be used by investors to identify the riskiest assets and the ideal months to make investments. In order to account for market uncertainty, we design specific intervals for the stock returns and use the range that results from the duration of these intervals as a measure of stock volatility throughout our analysis. A study is conducted to compare the volatility of the US and Indian stock markets in order to get some understanding for investment purposes
Spatiotemporal Edges for Arbitrarily Moving Video Classification in Protected and Sensitive Scenes
No full textClassification of arbitrary moving objects including vehicles and human beings in a real environment (such as protected and sensitive areas) is challenging due to arbitrary deformation and directions caused by shaky camera and wind. This work aims at adopting a spatiotemporal approach for classifying arbitrarily moving objects. The intuition to propose the approach is that the behavior of the arbitrary moving objects caused by wind and shaky camera is inconsistent and unstable, while, for static objects, the behavior is consistent and stable. The proposed method segments foreground objects from background using the frame difference between median frame and individual frame. This step outputs several different foreground information. The method finds static and dynamic edges by subtracting Canny of foreground information from the Canny edges of respective input frames. The ratio of the number of static and dynamic edges of each frame is considered as features. The features are normalized to avoid the problems of imbalanced feature size and irrelevant features. For classification, the work uses 10-fold cross-validation to choose the number of training and testing samples, and the random forest classifier is used for the final classification of frames with static objects and arbitrarily moving objects. For evaluating the proposed method, we construct our own dataset, which contains video of static and arbitrarily moving objects caused by shaky camera and wind. The results on the video dataset show that the proposed method achieves the state-of-the-art performance (76% classification rate) which is 14% better than the best existing method
Speckle Noise Removal: A Local Structure Preserving Approach
No full textThis paper proposes a speckle noise removal approach for clinical ultrasound images by doing outlier removal and smoothening operations alternately. During the initial investigation, it was found that the log-transformed ultrasound image follows Fisher–Tippett distribution and has fixed median absolute deviation (MAD). Hence, the noise in log-transformed ultrasound images behaves like white Gaussian noise with transients or outliers. Therefore, the de-noising problem can be considered as the removal of outliers followed by smoothening. These two processes are unified in one framework by defining a Bayesian Maximum-a-Posteriori (MAP) estimation function. This function has two terms: fidelity and regularizer. The fidelity is derived using the proposed generalized Fisher–Tippett distribution, whereas a weighted total variation is used as a regularizer. A regularizer weigh scheme is introduced to preserve edges in the images. The weights are computed using echo-texture graded local-oriented structure information present in an image. To obtain tissue-specific echo-texture, fuzzy C-means clustering is deployed for grouping similar tissue echo-textures. This grouping will help to discriminate the proper boundary of the tissue. To extract the original image, the MAP function is minimized and is performed using the generalized Bregman alternate method of multipliers. Ten different existing techniques are used to compare the performance of the proposed method on both phantom and clinical ultrasound images. The proposed approach achieved a signal-to-noise ratio in the range of 5–10 and a peak signal-to-noise ratio in the range of 67–70. Structural preservation metrics like figure of merit came out to be as high as 0.8. Moreover, using the proposed approach lower signal suppression index and higher effective number of lookup values are achieved for the restored clinical ultrasound images. The proposed algorithm can provide better piecewise smoothness and high contrast in despeckled images. Along with it, the edges are seen to be well preserved. Both qualitative and quantitative analysis support the efficacy of the approach compared to state-of-the-art methods
Stability of certain higher degree polynomials
No full textOne of the interesting problems in arithmetic dynamics is to study the stability of polynomials over a field. A polynomial f(z) ∈ Q[z] is stable over Q if irreducibility of f(z) implies that all its iterates are also irreducible over Q, that is, fn(z) is irreducible over Q for all n ≥ 1, where fn(z) denotes the n-fold composition of f(z). In this paper, we study the stability of f(z) = zd + 1/c for d ≥ 2, c ∈ Z\{0}. We show that for infinite families of d ≥ 3, whenever f(z) is irreducible, all its iterates are irreducible, that is, f(z) is stable. Under the assumption of explicit abc-conjecture, we further prove the stability of f(z) = zd + 1/c for the remaining values of d. Also for d = 3, if f(z) is reducible, then the number of irreducible factors of each iterate of f(z) is exactly 2 for |c| ≤ 1012. 1012