Analysis of a deep neural network for missing transverse momentum reconstruction in ATLAS

The ATLAS detector is a multipurpose particle detector built to record almost all possible decay products of the high energy proton-proton collisions provided by the Large Hadron Collider. The presence and combined kinematics of unobserved particles can be inferred by the observed momentum imbalance in the transverse plane. In this work, a deep neural network was trained using supervised learning to measure this imbalance. The performance of this network was evaluated in MC simulation and in 43 fb⁻¹ of data recorded at ATLAS. The network offered superior resolution and significantly better pileup resistance than all other pre-existing algorithms in every tested topology. The network also provided the best discriminator between events that did and did not contain neutrinos. The potential gain insensitivity to new physics was demonstrated by using this network in a search for the electroweak production of supersymmetric particles. The expected sensitivity to observe the production of said particles was increased by up to 26%

PEACH PRICES IN CALIFORNIA IN THE PRESENCE OF TECHNOLOGICAL CHANGE IN THE AGRICULTURAL PESTICIDE INDUSTRY

The potentially adverse effects of pesticides in wide use are causing concern to grow in the agricultural community. Minimizing the risks to human health and the environment created by agricultural pesticides has become a very important issue. The United States Environmental Protection Agency (EPA) has set a high priority on registering safer pesticides. According to the EPA, more than 1 billion pounds of active pesticide ingredients are used in the United States each year. Americans are exposed to pesticides every day through food consumption, cleaning products, and home and work environments. The agricultural pesticide industry has experienced an influx of changes during the past decade. Two of the primary changes affecting the pesticide industry are the introduction of new technology and EPA regulatory changes. On the regulatory front, the EPA requires manufacturers to register and test pesticides before they appear on the market. By 2006, the EPA will review old pesticides to ensure that they meet new safety requirements. These regulatory initiatives have contributed to the industry drive to develop safer and more "environmentally friendly" products for use in agricultural pest control. Technological changes consist of the introduction of new pesticides that are considered to be safer for both humans and the environment. As new technologies and regulatory initiatives are undertaken to ensure an improvement in both the safety of human health and the environment, one must consider how these changes may affect consumers. Specifically, an analysis should be conducted to determine whether or not the technological and regulatory changes have an effect on consumer prices. The recent developments in the agricultural pesticide industry provide several reasons to believe structural change has been occurring in economic relationships that determine peach prices in California. Therefore, we use a vector autoregressive (VAR) model to forecast peach prices by allowing parameters to vary with time. VAR models differ from standard econometric analyses of structural relationships in that they do not apply the usual exclusion restrictions to specify a priori which variables appear in which equations. Instead, a set of distributed lag equations is used to model each variable as a function of other variables in the structural system (Bessler, 1984). The objective of this paper is to forecast peach prices and evaluate dynamic relationships in the peach industry in the presence of technological and regulatory change. A VAR model that explicitly recognizes structural change will be used to forecast peach prices in California. Changes in dynamic relationships between peach prices and relevant economic variables will be considered.Demand and Price Analysis,

Occasional essay: upper motor neuron syndrome in amyotrophic lateral sclerosis

The diagnosis of amyotrophic lateral sclerosis (ALS) requires recognition of both lower (LMN) and upper motor neuron (UMN) dysfunction.1 However, classical UMN signs are frequently difficult to identify in ALS.2 LMN involvement is sensitively detected by electromyography (EMG)3 but, as yet, there are no generally accepted markers for monitoring UMN abnormalities,4 the neurobiology of ALS itself, and disease spread through the brain and spinal cord,.5 Full clinical assessment is therefore necessary to exclude other diagnoses and to monitor disease progression. In part, this difficulty regarding detection of UMN involvement in ALS derives from the definition of ‘the UMN syndrome’. Abnormalities of motor control in ALS require reformulation within an expanded concept of the UMN, together with the neuropathological, neuro-imaging and neurophysiological abnormalities in ALS. We review these issues here

Patterns of Duality in N=1 SUSY Gauge Theories

We study the patterns in the duality of a wide class of N=1 supersymmetric gauge theories in four dimensions. We present many new generalizations of the classic duality models of Kutasov and Schwimmer, which have themselves been generalized numerous times in works of Intriligator, Leigh and the present authors. All of these models contain one or two fields in a two-index tensor representation, along with fields in the defining representation. The superpotential for the two-index tensor(s) resembles A_k or D_k singularity forms, generalized from numbers to matrices. Looking at the ensemble of these models, classifying them by superpotential, gauge group, and ``level'' -- for terminology we appeal to the architecture of a typical European-style theatre -- we identify emerging patterns and note numerous interesting puzzles.Comment: 34 pages, 4 figures, uses harvmac and table

Thin-film Bulk Acoustic Resonators on Integrated Circuits for Physical Sensing Applications

Merging chemical and biomolecular sensors with silicon integrated circuits has the potential to push complex electronics into a low-cost, portable platform, greatly simplifying system- level instrumentation and extending the reach and functionality of point of use technologies. One such class of sensor, the thin-film bulk acoustic resonator (FBAR), has a micron-scale size and low gigahertz frequency range that is ideally matched with modern complementary metal-oxide-semiconductor (CMOS) technologies. An FBAR sensor can enable label-free detection of analytes in real time, and CMOS integration can overcome the measurement complexity and equipment cost normally required for detection with acoustic resonators. This thesis describes a body of work conducted to integrate an array of FBAR sensors with an active CMOS substrate. A monolithic fabrication method is developed, which allows for FBAR devices to be built directly on the top surface of the CMOS chip through post-processing. A custom substrate is designed and fabricated in 0.18 µm CMOS to support oscillation and frequency measurement for each sensor site in a 6×4 array. The fabrication of 0.8-1.5 GHz FBAR devices is validated for both off-chip and on-chip devices, and the integrated system is characterized for sensitivity and limit of detection. On-chip, parallel measurement of multiple sensors in real time is demonstrated for a quantitative vapor sensing application, and the limit of detection is below 50 ppm. This sensor platform could be used for a broad scope of label-free detection applications in chemistry, biology, and medicine, and it demonstrates potential for enabling a low-cost, point of use instrument

ν2\nu^2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows

In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations of object types and multiplicities in the final state for any desired neutrino multiplicities. In $t\bar{t}$ dilepton events, the momenta of both neutrinos and correlations between them are reconstructed more accurately than when using the most popular standard analytical techniques, and solutions are found for all events. Inference time is significantly faster than competing methods, and can be reduced further by evaluating in parallel on graphics processing units. We apply $\nu^2$-Flows to $t\bar{t}$ dilepton events and show that the per-bin uncertainties in unfolded distributions is much closer to the limit of performance set by perfect neutrino reconstruction than standard techniques. For the chosen double differential observables $\nu^2$-Flows results in improved statistical precision for each bin by a factor of 1.5 to 2 in comparison to the Neutrino Weighting method and up to a factor of four in comparison to the Ellipse approach.Comment: 20 pages, 16 figures, 5 table

\nu-Flows: Conditional Neutrino Regression

We present $\nu$-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks. This method allows the recovery of the full neutrino momentum which is usually left as a free parameter and permits one to sample neutrino values under a learned conditional likelihood given event observations. We demonstrate the success of $\nu$-Flows in a case study by applying it to simulated semileptonic $t\bar{t}$ events and show that it can lead to more accurate momentum reconstruction, particularly of the longitudinal coordinate. We also show that this has direct benefits in a downstream task of jet association, leading to an improvement of up to a factor of 1.41 compared to conventional methods.Comment: 26 pages, 15 figure

Invasion and persistence of a selfish gene in the Cnidaria

Background. Homing endonuclease genes (HEGs) are superfluous, but are capable of invading populations that mix alleles by biasing their inheritance patterns through gene conversion. One model suggests that their long-term persistence is achieved through recurrent invasion. This circumvents evolutionary degeneration, but requires reasonable rates of transfer between species to maintain purifying selection. Although HEGs are found in a variety of microbes, we found the previous discovery of this type of selfish genetic element in the mitochondria of a sea anemone surprising. Methods/Principal Findings. We surveyed 29 species of Cnidaria for the presence of the COXI HEG. Statistical analyses provided evidence for HEG invasion. We also found that 96 individuals of Metridium senile, from five different locations in the UK, had identical HEG sequences. This lack of sequence divergence illustrates the stable nature of Anthozoan mitochondria. Our data suggests this HEG conforms to the recurrent invasion model of evolution. Conclusions. Ordinarily such low rates of HEG transfer would likely be insufficient to enable major invasion. However, the slow rate of Anthozoan mitochondrial change lengthens greatly the time to HEG degeneration: this significantly extends the periodicity of the HEG life-cycle. We suggest that a combination of very low substitution rates and rare transfers facilitated metazoan HEG invasion. © 2006 Goddard et al

Cholera as a 'sanitary test' of British cities, 1831-1866.

The malign contribution of northern industrial cities to the stagnation of national life expectancy over the period 1820-1870 forms part of one of the most long-running debates in English economic history, regarding the impact of early industrialisation on living standards. The deteriorating quality of urban water supplies often features in these arguments as the key driver of worsening mortality in this period. Here we use mortality reported from cholera in the epidemic years 1831-1832 and 1848-1849 as an indicator of the extent of sewage contamination of water in English and Welsh towns in this period. Surprisingly, the geography of reported mortality did not indicate that northern manufacturing and industrial towns were especially deficient in this respect. However, logistic regression analyses identified a number of risk factors for high cholera mortality, including location on coal-bearing strata, which was a feature of many industrial towns. Notably, however, textile-manufacturing towns, although often located in coal-rich districts, were associated with low levels of cholera mortality, and high population growth rates did not influence the risk of cholera. Reductions in cholera mortality after 1849 raise the possibility of widespread improvements in water quality after mid-century, rather earlier than is often assumed. However, in contrast to cholera, infant and diarrhoeal mortality remained high especially in northern towns until at least 1900. Several lines of evidence suggest that infants were relatively protected from waterborne diseases such as cholera and typhoid, and therefore did not benefit greatly from improvements in water quality. We conclude (1) that any worsening of water quality in urban areas c.1800-1850 was not confined to new͛ or rapidly growing industrial or manufacturing towns; and (2) infants probably rarely drank untreated water, so high infant or diarrhoeal mortality rates should not be read as indicators of poor water quality, in the English context