Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparisons of Pooling Matrices for Pooled Testing of COVID-19

In comparison with individual testing, group testing (also known as pooled testing) is more efficient in reducing the number of tests and potentially leading to tremendous cost reduction. As indicated in the recent article posted on the US FDA website, the group testing approach for COVID-19 has received a lot of interest lately. There are two key elements in a group testing technique: (i) the pooling matrix that directs samples to be pooled into groups, and (ii) the decoding algorithm that uses the group test results to reconstruct the status of each sample. In this paper, we propose a new family of pooling matrices from packing the pencil of lines (PPoL) in a finite projective plane. We compare their performance with various pooling matrices proposed in the literature, including 2D-pooling, P-BEST, and Tapestry, using the two-stage definite defectives (DD) decoding algorithm. By conducting extensive simulations for a range of prevalence rates up to 5%, our numerical results show that there is no pooling matrix with the lowest relative cost in the whole range of the prevalence rates. To optimize the performance, one should choose the right pooling matrix, depending on the prevalence rate. The family of PPoL matrices can dynamically adjust their column weights according to the prevalence rates and could be a better alternative than using a fixed pooling matrix.
Digital Watermarking

-Digital watermarks are often used to provide copyright protection for intellectual property and help the fight against electronic copyright infringement. They can be repeated at random locations within the content to make them difficult to detect. Developments in computer science, cryptography, digital signal processing, and information theory have all contributed to the success of digital watermarking. This paper provides a brief introduction to digital watermarking.
Factors that influence the independence of EIA follow-up verifiers: a developing country perspective

Independent verification is an important aspect of practice for ensuring the credibility of an Environmental Impact Assessment (EIA). However, the independence of verifiers such as checkers, auditors, and Environmental Control Officers, may be influenced by various factors that may lead to a conflict of interest between role-players in EIA and EIA follow-up. Identifying these factors is, therefore, important. A total of 18 factors were identified by analysing literature from established verification professions and data derived within the South African context by means of interviews and workshops dedicated to clarifying independence of verifiers. The factors were divided into five categories: financial; commercial; professional; personal; and other. By shedding light on factors that influence the independence of EIA follow-up verifiers, this research aids in anticipating and avoiding potential conflicts of interest.
Vertical thermal O+flows at 850 km in dynamic auroral boundary coordinates

[1] Contemporary magnetosphere models now include species-dependent dynamics. Energetic O+ has significant consequences for the energy stored in the ring current, the rate of reconnection, and perhaps the timing of substorm injections. The mechanism by which thermal O+ escapes from the top of the ionosphere and into the magnetosphere is not fully understood. Previous studies have used dynamic auroral boundary coordinates to describe the outflowing energetic O+ ions above the ionosphere. In this study we focus on the vertical flow of O+ ions at lower altitudes before they are accelerated to escape velocity. An algorithm has been devised to identify auroral zone boundaries using precipitating electron observations from the Defense Meteorological Satellite Program (DMSP) spacecraft. Vertical ion flows measured by the DMSP special sensor for ions electrons and scintillation ion drift meter and the retarding potential analyzer instruments aboard the F12 (noon-midnight) and F13 (dawn-dusk) spacecraft from 1997 to 1998 were projected into dynamic auroral boundary coordinates and used to investigate the dependence of Southern Hemisphere bulk flows on interplanetary magnetic field (IMF) and geomagnetic conditions. Initial results show that (1) net upward flows occur primarily in the auroral zone and net downward flows occur primarily in the polar cap, (2) there exists a strong upward flow at 9 magnetic local time (MLT) near the polar cap boundary, 3) the downward ion flow orientation is strongly dependent on IMF By, and 4) the auroral boundary does not coincide exactly with the upward/downward boundary for bulk flows.
Large‐scale circulation features in the tropical western Pacific and their representation in climate models

[1] Within the Western Pacific Monsoon (WPM) region the zonal component of the low-level winds tends to weaken and reverse from east to west during the peak monsoon season, which also marks a peak in rainfall. This study examines how well climate models can simulate these phenomena prior to evaluating their projections for later this century. While a seasonal wind reversal or weakening appears to be reasonably well simulated by most models over much of the WPM, the relationships between large-scale average winds and rainfall are not always well simulated. This allows us to discriminate among the models in order to see if this affects the projections. However, it so happens that this has relatively little effect, and the predominant signal is for an increase in rainfall with a weakening or negligible change to the low-level monsoon winds. These results indicate that the WPM climate will respond more to global scale drivers such as an increase in atmospheric water vapor content and a weakening of the global circulation, rather than to more regional changes such as an increase in the land/ocean temperature contrast.
Cloud processing of nitrate

[1] The role of clouds in the transport and transformation of tropospheric pollutants was investigated through airborne measurements made out of Cleveland, Ohio, from 21 July to 18 August 2004, as part of the International Consortium for Atmospheric Research on Transport and Transformation 2004 program. Observations of gas-phase nitrate, size-resolved particulate nitrate, cloud water nitrate, and size-distributed cloud residual nitrate are used to examine changes in the partitioning of nitrate from precloud to postcloud as a function of particle size. The [NO3−]/[SO42−] ratio was highest in the bulk cloud water and higher in the cloud droplet residuals compared with the below-cloud aerosols. Most of the nitrate entered the cloud water as HNO3, and in 30% of 43 size distributions examined, the nitrate in the cloud droplets was found in residual particle sizes smaller than those of sulfate. Simulations from a trace gas-aerosol-cloud parcel model show that this size difference results from differences in the processes by which nitrate and sulfate enter cloud water. The transfer of HNO3 to cloud droplets is governed primarily by gas-phase mass transfer to the droplets, leading to greater accumulation in the smaller, more numerous droplets with higher total surface area. In contrast, much of the sulfate in the cloud water is the result of nucleation scavenging, which distributes the sulfate mass toward slightly larger sizes. The extent of separation between nitrate and sulfate is dependent on the cloud base sulfate size distribution and the factors that govern both HNO3 and SO2 uptake, with subsequent S(IV) oxidation.
Development of a modelling methodology for the investigation of riparian hydrological processes

The riparian zone is in intimate contact with the river and, as such, is a critical zone for understanding hydrological problems. This paper presents a general modelling methodology for the assessment of riparian hydrological processes. It is applicable to a wide range of riparian spaces and incorporates current expertise in numerical methods. A core part of the modelling methodology is the random walk particle method (RWPM). We develop an RWPM as part of the ESTEL2D subsurface flow model, test it against analytical solutions and apply it to the simulation of parcels of water as they move through the riparian zone. The modelling methodology provides a new opportunity to assess fundamental hydrological process issues such as the proportioning of pre‐event and event water storm runoff, and reversals of flow in floodplains. Copyright © 2005 John Wiley & Sons, Ltd.
Does the 𝓁1-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models?

We consider the problem of learning a sparse graph under Laplacian constrained Gaussian graphical models. This problem can be formulated as a penalized maximum likelihood estimation of the precision matrix under Laplacian structural constraints. Like in the classical graphical lasso problem, recent works made use of the $\ell_1$-norm regularization with the goal of promoting sparsity in Laplacian structural precision matrix estimation. However, we find that the widely used $\ell_1$-norm is not effective in imposing a sparse solution in this problem. Through empirical evidence, we observe that the number of nonzero graph weights grows with the increase of the regularization parameter. From a theoretical perspective, we prove that a large regularization parameter will surprisingly lead to a fully connected graph. To address this issue, we propose a nonconvex estimation method by solving a sequence of weighted $\ell_1$-norm penalized sub-problems and prove that the statistical error of the proposed estimator matches the minimax lower bound. To solve each sub-problem, we develop a projected gradient descent algorithm that enjoys a linear convergence rate. Numerical experiments involving synthetic and real-world data sets from the recent COVID-19 pandemic and financial stock markets demonstrate the effectiveness of the proposed method. An open source $\mathsf{R}$ package containing the code for all the experiments is available at this https URL.
Effective cloud optical depth for overcast conditions determined with a UV radiometers

Using a sky camera, episodes characterized by overcast low clouds in the Valencia region of Spain have been selected for analysis. One year of cloud optical depth data have been produced by inverting UV erythemal irradiance measured with a UVB‐1 radiometer from Yankee Environmental Systems using the LibRadtran radiative transfer model. Measurement uncertainties, aerosol single scattering albedo and cloud properties are, in order of decreasing importance, the most important factors influencing the accuracy of cloud optical depth retrieval when using UV erythemal radiometers. Statistics for cloud optical depth show a highly skewed frequency distribution best described by a gamma distribution with highest frequency between optical depths of 10 and 15 and optical depths exceeding 100 at the extreme high end.
Secure Outsourced Attribute-Based Signatures

Attribute-based signature (ABS) enables users to sign messages over attributes without revealing any information other than the fact that they have attested to the messages. However, heavy computational cost is required during signing in existing work of ABS, which grows linearly with the size of the predicate formula. As a result, this presents a significant challenge for resource-constrained devices (such as mobile devices or RFID tags) to perform such heavy computations independently. Aiming at tackling the challenge above, we first propose and formalize a new paradigm called Outsourced ABS, i.e., OABS, in which the computational overhead at user side is greatly reduced through outsourcing intensive computations to an untrusted signing-cloud service provider (S-CSP). Furthermore, we apply this novel paradigm to existing ABS schemes to reduce the complexity. As a result, we present two concrete OABS schemes: i) in the first OABS scheme, the number of exponentiations involving in signing is reduced from O(d) to O(1) (nearly three), where d is the upper bound of threshold value defined in the predicate; ii) our second scheme is built on Herranz et al.'s construction with constant-size signatures. The number of exponentiations in signing is reduced from O(d2) to O(d) and the communication overhead is O(1). Security analysis demonstrates that both OABS schemes are secure in terms of the unforgeability and attribute-signer privacy definitions specified in the proposed security model. Finally, to allow for high efficiency and flexibility, we discuss extensions of OABS and show how to achieve accountability as well.
MINACE-filter-based facial pose estimation

A facial pose estimation system is presented that functions with illumination variations present. Pose estimation is a useful first stage in a face recognition system. A separate minimum noise and correlation energy (MINACE) filter is synthesized for each pose. To select the MINACE parameter c for the filter for a pose, a training set of illumination differences of several faces at that pose, and a validation set of other poses (illumination differences of several faces at a few other poses) is used in the automated filter-synthesis step. However, the filter for each pose is a combination of faces at only that pose. The pose estimation system is evaluated using images from the CMU Pose, Illumination and Expression (PIE) database. The classification performance (PC) scores are presented for several pose estimation tests. The pose estimate will be used for a subsequent image transformation of a test face to a reference pose for face identification.
A geophysical investigation of the Late Roman underground settlement at Aydintepe, Northeast Turkey

The Aydintepe underground settlement in northeastern Turkey is a series of subsurface galleries carved in Eocene‐aged volcanic tuff. Rescue excavations conducted by the Local Archaeological Museum of Erzurum in 1996 and 1997 uncovered a large settlement with several underground rooms linked by passages. According to the excavators, it may have been built as a hideout and shelter by the first Christians who were banished by the Romans during the 3rd century A.D. Despite its historical and touristic value, parts of the underground settlement at Aydintepe have been destroyed to make room for the construction of new buildings in the modern town. As part of a conservation strategy for this important archaeological resource, a series of ground penetrating radar (GPR) and electrical resistivity measurements were conducted along eight profiles between 2003 and 2005. GPR soundings with a 100‐MHz unshielded antenna yielded reflected hyperbolic signals with high amplitude from tunnel structures. These highamplitude signals corresponded to areas of high electrical resistivity. These anomalies allow us to determine the probable locations of tunnels within the underground settlement and they provide important data that can be used to prevent future damage from modern construction. © 2008 Wiley Periodicals, Inc.
How to Tidy Up a Table: Fusing Visual and Semantic Commonsense Reasoning for Robotic Tasks with Vague Objectives

—Vague objectives in many real-life scenarios pose long-standing challenges for robotics, as deﬁning rules, rewards, or constraints for optimization is difﬁcult. Tasks like tidying a messy table may appear simple for humans, but articulating the criteria for tidiness is complex due to the ambiguity and ﬂexibility in commonsense reasoning. Recent advancement in Large Language Models (LLMs) offers us an opportunity to reason over these vague objectives: learned from extensive human data, LLMs capture meaningful common sense about human behavior. However, as LLMs are trained solely on language input, they may struggle with robotic tasks due to their limited capacity to account for perception and low-level controls. In this work, we propose a simple approach to solve the task of table tidying, an example of robotic tasks with vague objectives. Speciﬁcally, the task of tidying a table involves not just clustering objects by type and functionality for semantic tidiness , but also considering spatial-visual relations of objects for a visually pleasing arrangement, termed as visual tidiness . We propose to learn a lightweight, image-based tidiness score function to ground the semantically tidy policy of LLMs to achieve visual tidiness . We innovatively train the tidiness score using synthetic data gathered using random walks from a few tidy conﬁgurations. Such trajectories naturally encode the order of tidiness, thereby eliminating the need for laborious and expensive human demonstrations. Our empirical results show that our pipeline can be applied to unseen objects and complex 3D arrangements.
Emoji Prediction using Transformer Models

—In recent years, the use of emojis in social media has increased dramatically, making them an important element in understanding online communication. However, predicting the meaning of emojis in a given text is a challenging task due to their ambiguous nature. In this study, we propose a transformer-based approach for emoji prediction using BERT, a widely-used pre-trained language model. We ﬁne-tuned BERT on a large corpus of text(tweets) containing both text (tweets) and emojis to predict the most appropriate emoji for a given text. Our experimental results demonstrate that our approach outperforms several state-of-the-art models in predicting emojis with an accuracy of over 75 percent. This work has potential applications in natural language processing, sentiment analysis, and social media marketing.
Measuring covariation in RNA alignments: physical realism improves information measures

MOTIVATION The importance of non-coding RNAs is becoming increasingly evident, and often the function of these molecules depends on the structure. It is common to use alignments of related RNA sequences to deduce the consensus secondary structure by detecting patterns of co-evolution. A central part of such an analysis is to measure covariation between two positions in an alignment. Here, we rank various measures ranging from simple mutual information to more advanced covariation measures. RESULTS Mutual information is still used for secondary structure prediction, but the results of this study indicate which measures are useful. Incorporating more structural information by considering e.g. indels and stacking improves accuracy, suggesting that physically realistic measures yield improved predictions. This can be used to improve both current and future programs for secondary structure prediction. The best measure tested is the RNAalifold covariation measure modified to include stacking. AVAILABILITY Scripts, data and supplementary material can be found at http://www.binf. ku.dk/Stinus_covariation
The oceanic CO2 System and Carbon Budget in the Great Barrier Reef, Australia

The Great Barrier Reef (GBR) is the largest reef system in the world. During winter (dry season), a relative increase of CO2 fugacity, together with a depletion in total alkalinity, was evident in the GBR lagoon water. This suggests the release of CO2 due to CaCO3 production in the lagoon. But, in detail, significant contribution from organic matter oxidation was also identified. Despite the dissolved carbon losses as carbonate, total dissolved inorganic carbon (DIC) concentration normalized at constant salinity was relatively uniform throughout the study area. This indicates the presence of external carbon input to the lagoon. The most likely source is river discharge, which carries sizeable amounts of dissolved and particulate organic carbon as well as DIC. The marine CO2 system of the GBR lagoon highlights the nature of tropical carbonate shelves as a strong alkalinity sink and a net oxidation site of land‐derived organic carbon.
Tunable word length architecture for low power wireless OFDM demodulator

This paper proposes a low power architecture that dynamically controls a word length in a wireless OFDM demodulator. Finding the optimum word length for digital systems is a difficult problem because the trade-off between hardware cost and system performance is not conclusive. Actual digital systems prepare a more than enough word length in circuit design level to avoid the worst condition caused by lack of dynamic range. It indicates that they still have room to reduce power during better conditions. We propose tunable word length architecture (TWA) that dynamically changes its own word length according to communication environments. The proposed OFDM demodulator measures error vector magnitude (EVM) from de-modulated signals and tunes a word length to satisfy required quality of communication by monitoring the EVM performance. Our result reports that the demodulator reduced dissipated energy by the maximum 32% and 24% in AWGN and multipath fading channels, respectively, compared to use of a fixed word length
Fundamental limits of reconstruction-based superresolution algorithms under local translation

Superresolution is a technique that can produce images of a higher resolution than that of the originally captured ones. Nevertheless, improvement in resolution using such a technique is very limited in practice. This makes it significant to study the problem: "Do fundamental limits exist for Superresolution? " In this paper, we focus on a major class of superresolution algorithms, called the reconstruction-based algorithms, which compute high-resolution images by simulating the image formation process. Assuming local translation among low-resolution images, this paper is the first attempt to determine the explicit limits of reconstruction-based algorithms, under both real and synthetic conditions. Based on the perturbation theory of linear systems, we obtain the superresolution limits from the conditioning analysis of the coefficient matrix. Moreover, we determine the number of low-resolution images that are sufficient to achieve the limit. Both real and synthetic experiments are carried out to verify our analysis.
Nonlinear Adaptive Control of an Aerial Manipulation System

This paper presents the nonlinear adaptive control of a quadrotor endowed with a 2 degrees of freedom (DOF) manipulator. By considering the quadrotor and the robot arm as a combined system, complete modeling of the aerial manipulation system (AMS) has been presented using the Euler-Lagrange method. A hierarchical nonlinear control scheme which consists of outer and inner control loops has been utilized. Model Reference Adaptive Controller (MRAC) is designed for the outer loop where the required command signals are generated to force the quadrotor to move on a reference trajectory in the presence of uncertainties and reaction forces coming from the manipulator. For the inner loop, the attitude dynamics of the quadrotor and the dynamics of the 2-DOF robotic arm are considered as a fully actuated 5-DOF unified part of the AMS. Nonlinear adaptive control has been utilized for the low-level controller where the changes in inertias and the masses have been tackled along with the reaction forces acting on the attitude part of the AMS. The proposed technique has been validated through simulations in two different scenarios.