Miracle-dz/UniFAR-FTUs huggingface dataset:
On Addressing the Impact of ISO Speed Upon PRNU and Forgery Detection

Photo Response Non-Uniformity (PRNU) has been used as a powerful device fingerprint for image forgery detection because image forgeries can be revealed by finding the absence of the PRNU in the manipulated areas. The correlation between an image’s noise residual with the device’s reference PRNU is often compared with a decision threshold to check the existence of the PRNU. A PRNU correlation predictor is usually used to determine this decision threshold assuming the correlation is content-dependent. However, we found that not only the correlation is content-dependent, but it also depends on the camera sensitivity setting. Camera sensitivity, commonly known by the name of ISO speed, is an important attribute in digital photography. In this work, we will show the PRNU correlation’s dependency on ISO speed. Due to such dependency, we postulate that a correlation predictor is ISO speed-specific, i.e. reliable correlation predictions can only be made when a correlation predictor is trained with images of similar ISO speeds to the image in question. We report the experiments we conducted to validate the postulate. It is realized that in the real-world, information about the ISO speed may not be available in the metadata to facilitate the implementation of our postulate in the correlation prediction process. We hence propose a method called Content-based Inference of ISO Speeds (CINFISOS) to infer the ISO speed from the image content.
Time-resolved multiphoton effects in the fluorescence spectra of two-level systems at rest and in motion

We study the time-resolved fluorescence spectrum in two-level systems interacting with an incident coherent field, both in the weak and intermediate coupling regimes. For a single two-level system in the intermediate-coupling case, as time flows, the spectrum develops distinct features that are not captured by a semiclassical treatment of the incident field. Specifically, for a field on resonance with the atomic transition energy, the usual Mollow spectrum is replaced by a four-peak structure, and for a frequency that is half of the atomic transition energy, the time-dependent spectrum develops a second-harmonic peak with a superimposed Mollow triplet. In the long-time limit, our description recovers results previously found in the literature. After analyzing why a different behavior is observed in the quantum and classical dynamics, the reason for the occurrence of a second-harmonic signal in a two-level system is explained via a symmetry analysis of the total (electron and photon) system and in terms of a three-level system operating in limiting regimes. We find an increased second-harmonic signal in an array of two-level systems, suggesting a superradiance-like enhancement for multiple two-level systems in cavity setups. Finally, initial explorative results are presented for two-level model atoms entering and exiting a cavity, which hint at an interesting interplay between cavity-photon screening and atomic dynamics effects.
High-Resolution Air Quality Prediction Using Low-Cost Sensors

The use of low-cost sensors in air quality monitoring networks is still a much-debated topic among practitioners: they are much cheaper than traditional air quality monitoring stations set up by public authorities (a few hundred dollars compared to a few dozens of thousand dollars) at the cost of a lower accuracy and robustness. This paper presents a case study of using low-cost sensors measurements in an air quality prediction engine. The engine predicts jointly PM2.5 and PM10 concentrations in the United States at a very high resolution in the range of a few dozens of meters. It is fed with the measurements provided by official air quality monitoring stations, the measurements provided by a network of more than 4000 low-cost sensors across the country, and traffic estimates. We show that the use of low-cost sensors' measurements improves the engine's accuracy very significantly. In particular, we derive a strong link between the density of low-cost sensors and the predictions' accuracy: the more low-cost sensors are in an area, the more accurate are the predictions. As an illustration, in areas with the highest density of low-cost sensors, the low-cost sensors' measurements bring a 25% and 15% improvement in PM2.5 and PM10 predictions' accuracy respectively. An other strong conclusion is that in some areas with a high density of low-cost sensors, the engine performs better when fed with low-cost sensors' measurements only than when fed with official monitoring stations' measurements only: this suggests that an air quality monitoring network composed of low-cost sensors is effective in monitoring air quality. This is a very important result, as such a monitoring network is much cheaper to set up.
How fair can we go in machine learning? Assessing the boundaries of accuracy and fairness

Fair machine learning has been focusing on the development of equitable algorithms that address discrimination. Yet, many of these fairness‐aware approaches aim to obtain a unique solution to the problem, which leads to a poor understanding of the statistical limits of bias mitigation interventions. In this study, a novel methodology is presented to explore the tradeoff in terms of a Pareto front between accuracy and fairness. To this end, we propose a multiobjective framework that seeks to optimize both measures. The experimental framework is focused on logistiregression and decision tree classifiers since they are well‐known by the machine learning community. We conclude experimentally that our method can optimize classifiers by being fairer with a small cost on the classification accuracy. We believe that our contribution will help stakeholders of sociotechnical systems to assess how far they can go being fair and accurate, thus serving in the support of enhanced decision making where machine learning is used.
Growth, feed efficiency and carcass mineral composition of Heterobranchus longifilis, Oreochromis niloticus and Sarotherodon melanotheron juveniles fed different dietary levels of soybean meal-based diets.

The effects of substitution dietary fish protein by soybean protein on growth, survival, biochemical composition and mineral composition of juvenile Heterobranchus longifilis, Sarotherodon melanotheron and Oreochromis niloticus were evaluated. Three diets were formulated to be isonitrogenous (35% crude protein) by substituting fish meal for soybean meal at levels of 0 (FM), 25 (SBM25) and 50% (SBM50). Diets were fed to triplicate groups of each species at ratio of 5% body weight. At the end of the study period, final body weight (FBW), final body length (FBL), specific growth rate (SGR) and protein efficiency ratio (PER) decreased with an increasing dietary soybean protein level in H. longifilis. Survival rate (SR) was 1.5 times lower in juvenile catfish fed SBM50 (29.3%) than FM- and SBM25-fed fish (44%). S. melanotheron fed all three diets had no significant difference in FBW, FBL, SGR, SR, FCR, PER or production time. In O. niloticus , SR, FCR and PER were not affected by dietary protein substitution level. However, FBW, FBL and SGR increased with an increasing dietary soybean protein level. The production time generally showed a decline at lower protein substitution levels. The production cost was significantly lower in fish fed SBM50. Proximate composition analysis indicated that the carcass moisture in the three species and carcass protein content in H. longifilis and O. niloticus were not affected by dietary protein source. In S. melanotheron , carcass protein content showed a decline in fish fed SBM50. Carcass lipid, ash and gross energy levels in these species were also significantly affected by dietary protein sources. Carcass calcium and phosphorus concentrations in fish were significantly reduced with a high inclusion of dietary soybean meal. The results of this study indicated that fish meal can be replaced with soybean meal up to 50% level in diets for Tilapia S. melanotheron without adverse effects on growth, nutrient utilization or nitrogen balance. Key words: Fish protein, soya protein, growth, feed efficiency, proximate composition, mineral composition.
Microbial dynamics during aerobic deterioration of silages

Factors affecting microbial dynamics during aerobic deterioration of silages were evaluated in laboratory, pilot-scale and full-scale experiments. Yeasts usually caused the initial deterioration but in some samples with fairly good storage stability (>10 days) the activity was associated with the growth of moulds and bacteria. At a later stage, and especially at higher temperatures (>40°C), the activity was caused by bacilli. The increase in pH as a result of lactate metabolism permitted the growth of Enterobacteria. Evaluation of storage stability in samples from different levels in a bunker silo indicated low stability in surface samples and fairly good stability in samples from 160cm depth. The surface samples contained higher numbers (100–1000 times) of yeasts but the pH levels were similar. The establishment of yeasts during storage is probably due to diffusion of oxygen by leakage through the plastic cover. Compared with a plastic cover (0.1mm polythene), a rubber seal improved storage stability in laboratory silage (pH4.0) inoculated with lactic acid bacteria and infected with Candida albicans. An increase in acid content and a reduction in pH (3.7) by means of an efficient inoculant did not seem to increase the storage potential under the conditions described above. The content of adenosine triphosphate has been used as a measure of microbial activity during aerobic deterioration of silages. The main activity was concentrated at the surface (0–5cm). At 20°C, the activity was about ten times higher at the surface than at a depth of 30cm; above 30°C it was about 100 times higher.
Indirect inference for locally stationary ARMA processes with stable innovations

The class of locally stationary processes assumes that there is a time-varying spectral representation, that is, the existence of finite second moment. We propose the α-stable locally stationary process by modifying the innovations into stable distributions and the indirect inference to estimate this type of model. Due to the infinite variance, some of interesting properties such as time-varying autocorrelation cannot be defined. However, since the α-stable family of distributions is closed under linear combination which includes the possibility of handling asymmetry and thicker tails, the proposed model has the same tail behaviour throughout the time. In this paper, we propose this new model, present theoretical properties of the process and carry out simulations related to the indirect inference in order to estimate the parametric form of the model. Finally, an empirical application is illustrated.
Resource Allocation for Multi-Cell IRS-Aided NOMA Networks

This article proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service. The problem of joint user association, subchannel assignment, power allocation, phase shifts design, and decoding order determination is formulated for maximizing the achievable sum rate. The challenging mixed-integer non-linear problem is decomposed into an optimization subproblem (P1) with continuous variables and a matching subproblem (P2) with integer variables. In an effort to tackle the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmission power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation, and semidefinite relaxation. In terms of the combinational problem (P2), swap matching-based algorithms are developed for achieving a two-sided exchange-stable state among users, BSs and subchannels. Numerical results demonstrate that: i) the sum rate of multi-cell NOMA networks is capable of being increased by 35% with the aid of the IRS; ii) the proposed algorithms for multi-cell IRS-aided NOMA networks can enjoy 22% higher energy efficiency than conventional NOMA counterparts; iii) the trade-off between spectrum efficiency and coverage area can be tuned by judiciously selecting the location of the IRS.
Evidence of a geographical variation in Mesochaetopterus (Polychaeta: Chaetopteridae) from the Pacific Ocean

Specimens of Mesochaetopterus (Chaetopteridae) from seven geographical sources in the Pacific Ocean are compared on the basis of their hard structures: specialized chaetae, uncinal plates and tubes. There geographical variations are investigated both locally (New South Wales and the Solomon Islands) and over the whole Pacific Ocean from Australia (New South Wales) through the Solomon Islands to Galapagos and Hawaii. The most interesting result is the existence of intra-regional morphological variations with the hard structures differing on specimens sampled in two areas from New South Wales or in two areas from the Solomon Islands out of a total of three areas. These newly described morphologies imply that M. minutus, isolated in a first step from M. sagittarius and confined to the Pacific, is a pseudo-sibling species complex. Each element of this complex is morphologically distinguishable. Consequently the generally accepted role of the long-lived planktonic larvae characteristic of Mesochaetopterus, as a source of geographical homogeneity must be re-examined.
Foreign Direct Investment and Entrepreneurship: Gender Differences Across the World

Using recent cross-national data this paper examines the impact of foreign direct investment (FDI) on entrepreneurship activity. The impact of FDI on entrepreneurship is not clear a priori with the possibilities of both a negative effect (crowding out) and a positive effect (synergy or complementarity). Results find support for the crowding out effect; however, this effect varies across nations with different prevalence of entrepreneurship. Another focus of this work is on gender differences. The crowding out effect is stronger for the full sample, rather than the subsample of female entrepreneurship and this finding stands up to a battery of robustness checks. Policy implications of the findings are discussed.
On the Divergence of Decentralized Non-Convex Optimization

We study a generic class of decentralized algorithms in which $N$ agents jointly optimize the non-convex objective $f(u):=1/N\sum_{i=1}^{N}f_i(u)$, while only communicating with their neighbors. This class of problems has become popular in modeling many signal processing and machine learning applications, and many efficient algorithms have been proposed. However, by constructing some counter-examples, we show that when certain local Lipschitz conditions (LLC) on the local function gradient $\nabla f_i$'s are not satisfied, most of the existing decentralized algorithms diverge, even if the global Lipschitz condition (GLC) is satisfied, where the sum function $f$ has Lipschitz gradient. This observation raises an important open question: How to design decentralized algorithms when the LLC, or even the GLC, is not satisfied? To address the above question, we design a first-order algorithm called Multi-stage gradient tracking algorithm (MAGENTA), which is capable of computing stationary solutions with neither the LLC nor the GLC. In particular, we show that the proposed algorithm converges sublinearly to certain $\epsilon$-stationary solution, where the precise rate depends on various algorithmic and problem parameters. In particular, if the local function $f_i$'s are $Q$th order polynomials, then the rate becomes $\mathcal{O}(1/\epsilon^{Q-1})$. Such a rate is tight for the special case of $Q=2$ where each $f_i$ satisfies LLC. To our knowledge, this is the first attempt that studies decentralized non-convex optimization problems with neither the LLC nor the GLC.
LONG-TERM TRENDS IN SPRING ARRIVAL DATES OF MIGRANT BIRDS AT DELTA MARSH, MANITOBA, IN RELATION TO CLIMATE CHANGE

Abstract We examined a 63-year data set of dates of first spring sightings for 96 species of migrant birds at Delta Marsh, Manitoba, and considered the influence of local climate change on those arrival dates. Mean monthly spring temperatures increased (0.6–3.8°C) for all four months considered; however, trends for February and March were stronger than those for April and May. Over the 63-year period, 27 species significantly altered their arrival dates. Most of those species arrived significantly earlier; whereas only two species, Greater Yellowlegs (Tringa melanoleuca) and Lesser Yellowlegs (T. flavipes), arrived significantly later over time. About half of the migrants showed significant relationships between arrival dates and mean temperature for their month of arrival. Fifteen species showed significantly earlier arrivals over time and a significant relationship between arrival date and temperature. We also characterized migrants by taxon, breeding status, and wintering location to determine whether there were any trends for altered arrivals within certain groups. Waterfowl, species that breed at Delta Marsh, and short-distance migrants showed slightly higher incidences of advancing arrival dates compared with other groups. Our results provide evidence that climate warming has influenced spring migration arrival dates of several species in Manitoba. Tendances à Long-terme des Dates d'Arrivée Printanières des Oiseaux Migrateurs dans le Delta Marsh, Manitoba, en Relation avec les Changements Climatiques
A survey of face recognition techniques under occlusion

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed.
The Effects of Two Drying Rates on the Desiccation Tolerance of Embryonic Axes of Recalcitrant Jackfruit (Artocarpus heterophyllus Lamk.) Seeds

This study compared the survival, electrolyte leakage and ultrastructural characteristics of embryonic axes of recalcitrant jackfruit dried rapidly (⩽90 min) or slowly (2–3 d). Axes dried slowly showed decreased viability at higher water content than those dried rapidly; this was mirrored by an increase in electrolyte leakage at approx. 0.8 and 0.4 g H2O g−1dry mass, respectively. Rapid drying conferred relatively greater tolerance to dehydration, as attested by the 100% survival attained at approx. 0.4 g g−1in contrast to the total mortality of axes dried slowly to this water content. Partially hydrated axes were processed for microscopy using freeze-substitution to prevent rehydration artefacts. Radicles of axes dehydrated to 0.7 g g−1were examined microscopically to assess their cellular morphology and appearance of membranes following rapid or slow drying. Measurements showed that drying rate affected the distribution of water within axes, which could be relevant to the extent of stress experienced by germinative cells. Membrane breakdown was not observed in cells dehydrated either rapidly or slowly to 0.7 g g−1. Endoplasmic reticulum profiles were prominent in dehydrated cells. Autophagy was observed in axes of both treatments. The area occupied by vacuoles increased significantly only following rehydration, and was similar in axes dried rapidly or slowly. Desiccation damage became evident on rehydration, and was reversible following rapid drying but was more severe in axes dried slowly. Prolonged exposure to partial hydration may contribute to the greater sensitivity of vacuoles to damage during rehydration.
Oxygen-independent photocleavage of DNA, and uptake of chlorophyll derivatives by cellular nuclei and mitochondria

ABSTRACT Plasmid DNA has been efficiently photocleaved by sodium pheophorbides (Na-Phdes) a and b in the absence of oxygen as well as in the presence of oxygen. Singlet oxygen production sensitized by Na-Phdes a or b cannot be observed in the aqueous media, but with remarkable efficiency in organic solvents. Fluorescence microscopic observation has shown a rapid incorporation of Na-Phde a into nuclei, mitochondria, and lysosome of human oral mucosa cells. In contrast, Na-Phde b has been incorporated only into the plasma membrane. Fluorescence intensity of Na-Phde a has rapidly decreased during microscopic observation, indicating that some photochemical reactions take place in the organelle.
Deployment of Robotic Swarms via Density Feedback Control

With the rapid development of AI and robotics, deploying a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their overall performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for the deployment problem of a robotic swarm to achieve a desired global configuration. Specifically, we use partial differential equations (PDEs) to model the swarm and control its probability density over a bounded spatial domain using density feedback. The presented density feedback control laws use the current density as feedback signals and generate corresponding velocity fields that, by acting locally on individual robots, guide their global configuration to a target profile. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the disturbed closed-loop system (which is a nonlinear PDE) is locally ISS with respect to estimation errors of the current density. The effectiveness of the proposed control laws is verified using agent-based simulations.