Miracle-dz/UniFAR-FTUs huggingface dataset:
Fordham Intellectual Property, Media and Entertainment Law Journal Freud on the Court: Re-interpreting Sexting & Child Pornography Laws Freud on the Court: Re-interpreting Sexting & Child Pornography Laws * Freud on the Court: Re-interpreting Sexting & Child Pornography Laws

Although many developments in child pornography law are troubling, perhaps the most disconcerting is the growing number of cases in which children are being charged with violating child pornography laws for engaging in " sexting, " or sending sexually explicit photographs via cellular phones or over the Internet. Although the law implicitly considers children the victims of child pornography and the photographer and audience as punishable perpetrators, this logic is challenged by sexting cases. Yet in many instances, children who take and send " lascivious " pictures of themselves have been charged with violating the very law designed to protect them from the harms associated with child pornography. As a result, many scholars have recently de-cried the law as unjust and questioned its confusing motives. Existing scholarship has roundly criticized the situation's ostensible absurdity, but little work has been done to understand the legal motives for charging juveniles in sexting cases. This Article endeavors to better understand the motivation behind the law's perplexing stance on teenage sexting. A close analysis of recent sexting cases reveals a remarkable correlation between Freud's theory of sexuality and sexting jurisprudence. Beginning with the first Supreme Court decision on child pornography, New York v. Ferber, subsequent Supreme Court and District Court decisions on child pornography and sex-ting have been based on a strikingly Freudian logic. Perhaps fittingly, the alignment with Freud is subconscious: no court has acknowledged that its decision rests on a reading of Freud. Yet, as this article shows, Freud offers an extraordinarily accurate theoretical account of what judges have done in recent sexting cases. Understanding sexting cases in light of Freud does more than just explain the bewildering decisions of state and federal courts to uphold convictions against children for violating child pornography laws. In light of this unexpected finding, this article also provides a new basis from which to assess the goals of the law. As prosecutors continue to bring charges against teenagers for sexting, this article offers judges an alternate model for thinking about these difficult cases. Moreover, as more state legislatures draft new rules governing teenage sexting—in 2012 thirteen states considered resolutions aimed at sexting—this article proposes that lawmakers either abandon or correct their subconscious Freudianism in sexting cases, offering suggestions about how better to deal with teenagers who sext. Although many developments in child pornography law are troubling, perhaps the most disconcerting is the growing number of cases …
Supervised Metric Learning with Generalization Guarantees

In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest in optimizing distance and similarity functions using knowledge from training data to make them suitable for the problem at hand. This area of research is known as metric learning. Existing methods typically aim at optimizing the parameters of a given metric with respect to some local constraints over the training sample. The learned metrics are generally used in nearest-neighbor and clustering algorithms. When data consist of feature vectors, a large body of work has focused on learning a Mahalanobis distance, which is parameterized by a positive semi-definite matrix. Recent methods offer good scalability to large datasets. Less work has been devoted to metric learning from structured objects (such as strings or trees), because it often involves complex procedures. Most of the work has focused on optimizing a notion of edit distance, which measures (in terms of number of operations) the cost of turning an object into another. We identify two important limitations of current supervised metric learning approaches. First, they allow to improve the performance of local algorithms such as k-nearest neighbors, but metric learning for global algorithms (such as linear classifiers) has not really been studied so far. Second, and perhaps more importantly, the question of the generalization ability of metric learning methods has been largely ignored. In this thesis, we propose theoretical and algorithmic contributions that address these limitations. Our first contribution is the derivation of a new kernel function built from learned edit probabilities. Unlike other string kernels, it is guaranteed to be valid and parameter-free. Our second contribution is a novel framework for learning string and tree edit similarities inspired by the recent theory of (epsilon,gamma,tau)-good similarity functions and formulated as a convex optimization problem. Using uniform stability arguments, we establish theoretical guarantees for the learned similarity that give a bound on the generalization error of a linear classifier built from that similarity. In our third contribution, we extend the same ideas to metric learning from feature vectors by proposing a bilinear similarity learning method that efficiently optimizes the (epsilon,gamma,tau)-goodness. The similarity is learned based on global constraints that are more appropriate to linear classification. Generalization guarantees are derived for our approach, highlighting that our method minimizes a tighter bound on the generalization error of the classifier. Our last contribution is a framework for establishing generalization bounds for a large class of existing metric learning algorithms. It is based on a simple adaptation of the notion of algorithmic robustness and allows the derivation of bounds for various loss functions and regularizers.
Out of control: Visceral influences on behavior

Abstract Understanding discrepancies between behavior and perceived self-interest has been one of the major, but largely untackled, theoretical challenges confronting decision theory from its infancy to the present. People often act against their self-interest in full knowledge that they are doing so; they experience a feeling of being “out of control. ” This paper attributes this phenomenon to the operation of “visceral factors,” which include drive states such as hunger, thirst and sexual desire, moods and emotions, physical pain, and craving for a drug one is addicted to. The defining characteristics of visceral factors are, first, a direct hedonic impact (which is usually negative), and second, an effect on the relative desirability of different goods and actions. The largely aversive experience of hunger, for example, affects the desirability of eating, but also of other activities such as sex. Likewise, fear and pain are both aversive, and both increase the desirability of withdrawal behaviors. The visceral factor perspective has two central premises: First, immediately experienced visceral factors have a disproportionate effect on behavior and tend to “crowd out” virtually all goals other than that of mitigating the visceral factor. Second, people underweigh, or even ignore, visceral factors that they will experience in the future, have experienced in the past, or that are experienced by other people. The paper details these two assumptions, then shows how they can help to explain a wide range of phenomena: impulsivity and self-control, drug addiction, various anomalies concerning sexual behavior, the effect of vividness on decision making, and certain phenomena relating to motivation and action.
Impacts of climate change from 2000 to 2050 on wildfire activity and carbonaceous aerosol concentrations in the western United States

[1] We investigate the impact of climate change on wildfire activity and carbonaceous aerosol concentrations in the western United States. We regress observed area burned onto observed meteorological fields and fire indices from the Canadian Fire Weather Index system and find that May–October mean temperature and fuel moisture explain 24–57% of the variance in annual area burned in this region. Applying meteorological fields calculated by a general circulation model (GCM) to our regression model, we show that increases in temperature cause annual mean area burned in the western United States to increase by 54% by the 2050s relative to the present day. Changes in area burned are ecosystem dependent, with the forests of the Pacific Northwest and Rocky Mountains experiencing the greatest increases of 78 and 175%, respectively. Increased area burned results in near doubling of wildfire carbonaceous aerosol emissions by midcentury. Using a chemical transport model driven by meteorology from the same GCM, we calculate that climate change will increase summertime organic carbon (OC) aerosol concentrations over the western United States by 40% and elemental carbon (EC) concentrations by 20% from 2000 to 2050. Most of this increase (75% for OC and 95% for EC) is caused by larger wildfire emissions with the rest caused by changes in meteorology and for OC by increased monoterpene emissions in a warmer climate. Such an increase in carbonaceous aerosol would have important consequences for western U.S. air quality and visibility.
Confirmation Bias-Aware Fake News Detection with Graph Transformer Networks

This paper proposes a method for fake news detection that is aware of confirmation bias and uses propagation-based techniques. We fine-tune BERT for a stance analysis task in order to obtain user stance embeddings. The structural information is updated by extracting the similarity of stances based on confirmation bias along the news-sharing path using a Graph Transformer Network. Experiments on the Politifact dataset demonstrate that this method achieves superior performance and is effective for real-world fake news detection.
The Basic Public Finance of Public-Private Partnerships

Public-private partnerships (PPPs) cannot be justified because they free public funds. When PPPs are justified on efficiency grounds, the contract that optimally balances demand risk, user-fee distortions and the opportunity cost of public funds, features a minimum revenue guarantee and a revenue cap. However, observed revenue guarantees and revenue sharing arrangements differ from those suggested by the optimal contract. Also, this contract can be implemented via a competitive auction with realistic informational requirements. Finally, the allocation of risk under the optimal contract suggests that PPPs are closer to public provision than to privatization.
The Production of Child Health in Kenya : A Structural Model of Birth Weight

The paper investigates birth weight and its correlates in Kenya using nationally representative data collected by the government in the early 1990s. I find that immunization of the mother against tetanus during pregnancy is strongly associated with improvements in birth weight. Other factors significantly correlated with birth weight include age of the mother at first birth and birth orders of siblings. It is further found that birth weight is positively associated with mother’s age at first birth and with higher birth orders, with the first born child being substantially lighter than subsequent children. Newborn infants are heavier in urban than in rural areas and females are born lighter than males. There is evidence suggesting that a baby born at the clinic is heavier than a newborn baby drawn randomly from the general population.
Observers and observability for uncertain nonlinear systems: A necessary and sufficient condition

In this paper, observers and observability for uncertain nonlinear systems are systematically discussed. It is shown that for the convergence of a large class of observers, featured with the augment state to estimate the uncertainty, it requires not only the observability condition for the augment matrix pair but, more importantly, requires a structural condition first proposed in this paper. Furthermore, it is demonstrated that the combination of this structural condition and the observability of the augment matrix pair is a necessary and sufficient condition for the convergence of the observers and the observability of the original uncertain nonlinear systems. This implies that both the structural condition and the observability condition of the augment matrix pair reveal essential feature of the observing problems for uncertain nonlinear systems. In addition, for unobservable uncertain nonlinear systems, which do not satisfy this necessary and sufficient condition, the biased estimation error is explicitly presented, which can be used to evaluate the estimation performance of this class of observers. The numerical simulations for three typical examples are carried out to validate our theoretical analysis.
Robust visual detection of brake-lights in front for commercialized dashboard camera

The collision avoidance system (CAS) is an essential system for safe driving that alerts the driver or automatically applies the brakes in an expected situation of a vehicle collision. To realize this, an autonomous system that can quickly and precisely detect brake-lights of preceding vehicle is essential and this should works well in various environments for safety reason. Our proposed vision algorithm solves these objectives focusing on simple color features rather than a learning algorithm with a high computational cost, since our target system is a real-time embedded device, i.e., forward-facing dashboard camera. However, the existing feature-based algorithms are vulnerable to the ambient noise (noise problem), and cannot be flexibly applied to various environments (applicability problem). Therefore, our method is divided into two stages: rear-lights region detection using gamma correction for noise problem, and brake-lights detection using HSV color space for applicability problem, respectively. (i) Rear-lights region detection: we confirm the presence of the vehicle in front and derive the rear-lights region, and used non-linear mapping of gamma correction to make the detected region robust to noise. (ii) Brake-lights detection: from the detected rear-lights region, we extract color features using the HSV color range so that we can classify brake on and off in various conditions. Experimental results show that our algorithm overcomes the noise problem and applicability problem in various environments.
Genotype Distribution of Enterobius vermicularis Isolates from Northwest Provinces of Iran

Background Human infection with Enterobius vermicularis occurs worldwide. The most common clinical manifestation of a pinworm infection is an itchy anal region. This parasite is incidentally found in appendicitis. This study aims to characterize and genotype this parasite from different samples inferred by mt-DNA. Methods Forty appendectomies for acute clinical appendicitis, 40 positive scotch-tape samples, and 10 adult females worm isolated from patients. Genetic differentiation, haplotype differences, and isolates population structure were analyzed based on the cytochrome c oxidase subunit I (cox1) gene. Results It has been demonstrated that all isolations in the appendectomies specimens are similar, and the genetic difference divergence is seen in adult worm specimens. The neutral indices of the samples did not show a significant difference and show that there is no intra-specific and population distribution diversity. Conclusion Our results show different haplotypes in the B type of E. vermicularis population and add new information about genotyping of these parasites in Iran. In comparison with other studies, intra-specific variation of this parasite from Iran was observed.
Space shuttle entry terminal area energy management

A historical account of the development for Shuttle's Terminal Area Energy Management (TAEM) is presented. A derivation and explanation of logic and equations are provided as a supplement to the well documented guidance computation requirements contained within the official Functional Subsystem Software Requirements (FSSR) published by Rockwell for NASA. The FSSR contains the full set of equations and logic, whereas this document addresses just certain areas for amplification.
Impact of implementation of front-of-package nutrition labeling on sugary beverage consumption and consequently on the prevalence of excess body weight and obesity and related direct costs in Brazil: An estimate through a modeling study

Rationale Intake of sugary beverages has been associated with obesity and chronic non-communicable diseases, thereby increasing the direct health costs related to these diseases. Front-of-package nutrition labeling (FoPNL) aims to help consumers understand food composition, thereby improving food choices and preventing the development of such diseases. Objective To estimate, over five years, the impact of implementing FoPNL in Brazil on the prevalence of excess body weight and obesity in adults who consume sugary beverages and the direct costs related to such problems. Methods A simulation study to performed to estimate the effect of FoPNL implementation on the prevalence of excess body weight and obesity. The VIGITEL research database (2019), published in the 2020 report, was used in this study (the final sample consisted of 12,471 data points representing 14,380,032 Brazilians). The scenarios were considered: base (trend in sugary beverage intake); 1 (base scenario associated with the changes in energy content of the purchased beverages observed after the first phase of the Chilean labeling law (−9.9%); and 2 (scenario 1 associated with reformulation of beverages, total energy reduction of −1.6%). Changes in body weight were estimated using the simulation model of Hall et al. (2011) over five years. A linear trend in the prevalence of obesity and excess body weight in the Brazilian population was considered. The impact of the prevalence of obesity and excess body weight on body mass index was estimated. In addition, the direct health costs related to obesity were estimated. Results Energy consumption from sugary beverages after FoPNL implementation is expected to be reduced by approximately 28 kcal/day (95% CI, −30 to −27) considering scenario 1. In scenarios 1 and 2, without FoPNL, the prevalence of obesity and excess body weight over five years was estimated to be 25.3% and 25.2%, and 64.4% and 64.2%, respectively. By extrapolating the results to the entire Brazilian population, it was observed that the implementation of FoPNL may reduce the prevalence of obesity by −0.32 percentage points and −0.35 percentage points (scenario 1 and 2, respectively) and excess body weight by −0.42 percentage points and −0.48 percentage points (scenarios 1 and 2, respectively) in five years. It is estimated that after five years of implementation, it will be possible to save approximately US$ 5,5 millions (95% CI 4,7 to 8,8) in scenario 1, reaching approximately US$ 6,1 millions (95% CI 5,3 to 9,8) in scenario 2. Conclusion The results of this modeling study indicate that FoPNL may reduce prevalence of excess body weight and obesity, representing strategic public policies for obesity prevention.
Quantitative magnetic resonance mapping of the myelin bilayer reflects pathology in multiple sclerosis brain tissue

Multiple sclerosis (MS) is a neuroinflammatory disease characterized by loss of myelin (demyelination) and, to a certain extent, subsequent myelin repair (remyelination). To better understand the pathomechanisms underlying de- and remyelination and to monitor the efficacy of treatments aimed at regenerating myelin, techniques offering noninvasive visualizations of myelin are warranted. Magnetic resonance (MR) imaging has long been at the forefront of efforts to visualize myelin, but it has only recently become feasible to access the rapidly decaying resonance signals stemming from the myelin lipid-protein bilayer itself. Here, we show that direct MR mapping of the bilayer yields highly specific myelin maps in brain tissue from patients with MS. Furthermore, examination of the bilayer signal behavior is found to reveal pathological alterations in normal-appearing white and gray matter. These results indicate promise for in vivo implementations of the myelin bilayer mapping technique, with prospective applications in basic research, diagnostics, disease monitoring, and drug development.
Glacial geology and stratigraphy of Fort Richardson, Alaska : a review of available data on the hydrogeology

Abstract : The surficial geology and glacial stratigraphy of Fort Richardson are extremely complex. Recent mapping by the USGS shows the general distribution of surficial deposits, but details on the underlying stratigraphy remain poorly known, leaving a critical gap in the understanding of ground water conditions below Fort Richardson. A conceptual model of the subsurface stratigraphy was developed on the basis of results of recent surficial mapping, current knowledge of the glacial history studies of modern glaciers, and limited subsurface data. A confining layer below the southern half of the cantonment is likely the northern extension of an "older" ground moraine that crops out further to the south. Below the cantonment, this moraine is buried below about 15 m of outwash and fan deposits, but it appears to be absent to the north, where the confined and unconfined aquifers are hydraulically connected. The northern limit of the "continuous" ground moraine is roughly below the cantonment and parts of Operable Unit D. Buried silt horizons in the fan probably create the locally perched aquifers; however, erosional remnants of the ground moraine and interfingering of debris flow deposits along the Elmendorf Moraine are plausible alternatives. These deposits are composed of finer-grained materials that slow ground water infiltration and cause water to accumulate.
Molecular detection of Coxiella burnetii in raw meat samples collected from different abattoirs in districts Kasur and Lahore of Punjab, Pakistan

Coxiella burnetii is the zoonotic pathogen that causes Q fever; it is widespread globally. Livestock animals are its main reservoir, and infected animals shed C. burnetii in their birth products, feces, vaginal mucus, urine, tissues, and food obtained from them, i.e., milk and meat. There were previously very few reports on the prevalence of C. burnetii in raw meat. This study aimed to determine the prevalence of C.burnetii and its molecular characterization in raw ruminant meat from the Kasur and Lahore districts in Punjab, Pakistan, as this has not been reported so far. In this study, 200 meat samples, 50 from each species of cattle, buffalo, goat, and sheep, were collected from the slaughterhouses in each district, Kasur and Lahore in 2021 and 2022. PCR was used for the detection of the IS1111 element of C. burnetii. The data were recorded and univariate analysis was performed to determine the frequency of C. burnetii DNA in raw meat samples obtained from different ruminant species using the SAS 9.4 statistical package. Of the total of 200 raw meat samples, C. burnetii DNA was present in 40 (20%) of them, tested by PCR using the IS1111 sequence. The prevalence of C.burnetii differed among the studied species of ruminants. When species were compared pairwise, the prevalence in cattle was statistically significantly lower than in sheep (P = 0.005). The sequence alignment based on origin implied that the strains are genetically diverse in different districts of Punjab, Pakistan. The findings demonstrated that the prevalence of C. burnetii, especially in raw meat samples, deserves more attention from the health care system and professionals from Punjab, Pakistan, i.e., abattoir workers and veterinarians.