Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of CCS in global carbon management: simulation with the DICE model

This study attempts a numerical simulation of potential CCS (carbon dioxide capture and storage) use by using a modified version of the DICE (Dynamic Integrated model on Climate and Economy) model (Nordhaus, 1994; Nordhaus and Boyer, 2000). In DICE, CO2 emissions are controlled to the extent in which a hypothetical optimal carbon tax justifies CO2 reduction by firms: in our analysis, CCS is used when the optimal tax level is higher than the price of CCS. The analysis assesses the economic optimality of CCS use with a range of different assumptions. The simulation particularly focuses on the difference of results originating from two sets of general assumptions on climate change modeling, reflecting the current debate on the economics of climate change (see for example, Heal, 2008): (1) Parameterization of the standard DICE; (2) Alternative assumptions whose hints are drawn from Stern (2007). In the standard DICE cases, the model calculation shows that at the price level of $25/tCO2 ($92/tC), CCS is introduced around in the middle of the twenty-first century. With the alternative assumptions (e.g., near-zero discount rate), CCS begins to be utilized massively earlier in the century. The two sets of results lead to contrasting policy implications on the future CCS use; this is particularly problematic in the CCS context since its benefits are not always clear-cut (e.g., limitedness of secondary benefits besides CO2 reduction, uncertainties about the validity of technology itself). Settlement of the current intellectual debate on the economics of climate change would greatly benefit the debate on the role of CCS as well.
Speaker Recognition Method Based on CPSO Clustering and KMP Algorithm

This paper proposes a chaotic particle swarm optimization and kernel matching pursuit algorithm a combination of speaker recognition methods. First through the CPSO clustering algorithm to transform the MFCC feature parameters of processing, streamlining of the MFCC feature parameters (SMFCC), then use the KMP algorithm on the reduction parameters after the SMFCC feature classification training and recognition. The results show that CPSO-KMP-based speaker recognition method compared to the GMM-UBM method, the relative increase in EER performance of 31%.
A linkage analysis toolkit for studying allosteric networks in ion channels

A thermodynamic approach to studying allosterically regulated ion channels such as the large-conductance voltage- and Ca2+-dependent (BK) channel is presented, drawing from principles originally introduced to describe linkage phenomena in hemoglobin. In this paper, linkage between a principal channel component and secondary elements is derived from a four-state thermodynamic cycle. One set of parallel legs in the cycle describes the “work function,” or the free energy required to activate the principal component. The second are “lever operations” activating linked elements. The experimental embodiment of this linkage cycle is a plot of work function versus secondary force, whose asymptotes are a function of the parameters (displacements and interaction energies) of an allosteric network. Two essential work functions play a role in evaluating data from voltage-clamp experiments. The first is the conductance Hill energy WH[g], which is a “local” work function for pore activation, and is defined as kT times the Hill transform of the conductance (G-V) curve. The second is the electrical capacitance energy WC[q], representing “global” gating charge displacement, and is equal to the product of total gating charge per channel times the first moment (VM) of normalized capacitance (slope of Q-V curve). Plots of WH[g] and WC[q] versus voltage and Ca2+ potential can be used to measure thermodynamic parameters in a model-independent fashion of the core gating constituents (pore, voltage-sensor, and Ca2+-binding domain) of BK channel. The method is easily generalized for use in studying other allosterically regulated ion channels. The feasibility of performing linkage analysis from patch-clamp data were explored by simulating gating and ionic currents of a 17-particle model BK channel in response to a slow voltage ramp, which yielded interaction energies deviating from their given values in the range of 1.3 to 7.2%.
A randomized comparison between rasburicase and allopurinol in children with lymphoma or leukemia at high risk for tumor lysis.

Standard therapy in the United States for malignancy-associated hyperuricemia consists of hydration, alkalinization, and allopurinol. Urate oxidase catalyzes the enzymatic oxidation of uric acid to a 5 times increased urine soluble product, allantoin. Rasburicase is a new recombinant form of urate oxidase available for clinical evaluation. This multicenter randomized trial compared allopurinol to rasburicase in pediatric patients with leukemia or lymphoma at high risk for tumor lysis. Patients received the assigned uric acid-lowering agent for 5 to 7 days during induction chemotherapy. The primary efficacy end point was to compare the area under the serial plasma uric acid concentration curves during the first 96 hours of therapy (AUC(0-96)). Fifty-two patients were randomized at 6 sites. In an intent-to-treat analysis, the mean uric acid AUC(0-96) was 128 +/- 70 mg/dL.hour for the rasburicase group and 329 +/- 129 mg/dL.hour for the allopurinol group (P <.0001). The rasburicase versus allopurinol group experienced a 2.6-fold (95% CI: 2.0-3.4) less exposure to uric acid. Four hours after the first dose, patients randomized to rasburicase compared to allopurinol achieved an 86% versus 12% reduction (P <.0001) of initial plasma uric acid levels. No antirasburicase antibodies were detected at day 14. This randomized study demonstrated more rapid control and lower levels of plasma uric acid in patients at high risk for tumor lysis who received rasburicase compared to allopurinol. For pediatric patients with advanced stage lymphoma or high tumor burden leukemia, rasburicase is a safe and effective alternative to allopurinol during initial chemotherapy.
Random Complexes and l^2-Betti Numbers

Uniform spanning trees on finite graphs and their analogues on infinite graphs are a well-studied area. On a Cayley graph of a group, we show that they are related to the first $\ell^2$-Betti number of the group. Our main aim, however, is to present the basic elements of a higher-dimensional analogue on finite and infinite CW-complexes, which relate to the higher $\ell^2$-Betti numbers. One consequence is a uniform isoperimetric inequality extending work of Lyons, Pichot, and Vassout. We also present an enumeration similar to recent work of Duval, Klivans, and Martin.
Keypoint induced distance profiles for visual recognition

We show that histograms of keypoint descriptor distances can make useful features for visual recognition. Descriptor distances are often exhaustively computed between sets of keypoints, but besides finding the k-smallest distances the structure of the distribution of these distances has been largely overlooked. We highlight the potential of such information in the task of particular scene recognition. Discriminative scene signatures in the form of histograms of keypoint descriptor distances are constructed in a supervised manner. The distances are computed between properly selected reference keypoints and the keypoints detected in the input image. The signature is low dimensional, computationally cheap to obtain, and can distinguish a large number of scenes. We introduce a scheme based on multiclass AdaBoost to select the appropriate reference keypoints. The resulting system is capable of handling a large number of scene classes at a fraction of the time required for exhaustively matching sets of keypoints. This supports supports a coarse-to-fine search strategy for approaches reliant on keypoint matching. We test the idea on 3 datasets for particular scene recognition and report the obtained results.
Using lifetime predictors to improve memory allocation performance

Dynamic storage allocation is used heavily in many application areas including interpreters, simulators, optimizers, and translators. We describe research that can improve all aspects of the performance of dynamic storage allocation by predicting the lifetimes of short-lived objects when they are allocated. Using five significant, allocation-intensive C programs, we show that a great fraction of all bytes allocated are short-lived (> 90% in all cases). Furthermore, we describe an algorithm for liftetime prediction that accurately predicts the lifetimes of 42–99% of all objects allocated. We describe and simulate a storage allocator that takes adavantage of lifetime prediction of short-lived objects and show that it can significantly improve a program's memory overhead and reference locality, and even, at times, improve CPU performance as well.
Depression in men in the postnatal period and later child psychopathology: a population cohort study.

OBJECTIVE Postnatal depression in women is associated with adverse effects on both maternal health and children's development. It is unclear whether depression in men at this time poses comparable risks. The present study set out to assess the association between depression in men in the postnatal period and later psychiatric disorders in their children and to investigate predisposing factors for depression in men following childbirth. METHOD A population-based cohort of 10,975 fathers and their children from the Avon Longitudinal Study of Parents and Children (ALSPAC) was recruited in the prenatal period and followed for 7 years. Paternal depressive symptoms were assessed with the Edinburgh Postnatal Depression Scale and later child psychiatric disorder (DSM-IV) with the Development and Well-Being Assessment. RESULTS Depression in fathers in the postnatal period was significantly associated with psychiatric disorder in their children 7 years later (adjusted OR 1.72, 95% CI 1.07-2.77), most notably oppositional defiant/conduct disorders (adjusted OR 1.94, 95% CI 1.04-3.61), after adjusting for maternal depression and paternal educational level. A history of severe depression and high prenatal symptom scores for depression and anxiety were the strongest predictors of paternal depression in the postnatal period. CONCLUSIONS Depression in fathers in the postnatal period is associated with later psychiatric disorders in their children, independently of maternal postnatal depression. Further research into the risks associated with paternal psychopathology is required because this could represent an important opportunity for public health intervention.
The Effects of Sugars on the Biofilm Formation of Escherichia coli 185p on Stainless Steel and Polyethylene Terephthalate Surfaces in a Laboratory Model

Background Bacteria utilize various methods in order to live in protection from adverse environmental conditions. One such method involves biofilm formation; however, this formation is dependent on many factors. The type and concentration of substances such as sugars that are present in an environment can be effective facilitators of biofilm formation. Methods First, the physico-chemical properties of the bacteria and the target surface were studied via the MATS and contact angle measurement methods. Additionally, adhesion to different surfaces in the presence of various concentrations of sugars was compared in order to evaluate the effect of these factors on the biofilm formation of Escherichia coli, which represents a major food contaminant. Results Results showed that the presence of sugars has no effect on the bacterial growth rate; all three concentrations of sugars were hydrophilic and demonstrated a high affinity toward binding to the surfaces. Conclusions The impact of sugars and other factors on biofilm formation can vary depending on the type of bacteria present.
Increased insulin clearance in mice with double deletion of glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide receptors.

To establish whether incretin hormones affect insulin clearance, the aim of this study was to assess insulin clearance in mice with genetic deletion of receptors for both glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), so called double incretin receptor knockout mice (DIRKO). DIRKO ( n = 31) and wild-type (WT) C57BL6J mice ( n = 45) were intravenously injected with d-glucose (0.35 g/kg). Blood was sampled for 50 min and assayed for glucose, insulin, and C-peptide. Data were modeled to calculate insulin clearance; C-peptide kinetics was established after human C-peptide injection. Assessment of C-peptide kinetics revealed that C-peptide clearance was 1.66 ± 0.10 10-3 1/min. After intravenous glucose administration, insulin clearance during first phase insulin secretion was markedly higher in DIRKO than in WT mice (0.68 ± 0.06 10-3 l/min in DIRKO mice vs. 0.54 ± 0.03 10-3 1/min in WT mice, P = 0.02). In contrast, there was no difference between the two groups in insulin clearance during second phase insulin secretion ( P = 0.18). In conclusion, this study evaluated C-peptide kinetics in the mouse and exploited a mathematical model to estimate insulin clearance. Results showed that DIRKO mice have higher insulin clearance than WT mice, following intravenous injection of glucose. This suggests that incretin hormones reduce insulin clearance at physiological, nonstimulated levels.
A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems

It has been widely recognized that closed-loop neuroprosthetic systems achieve more favorable outcomes for users then equivalent open-loop devices. Improved performance of tasks, better usability, and greater embodiment have all been reported in systems utilizing some form of feedback. However, the interdisciplinary work on neuroprosthetic systems can lead to miscommunication due to similarities in well-established nomenclature in different fields. Here we present a review of control strategies in existing experimental, investigational and clinical neuroprosthetic systems in order to establish a baseline and promote a common understanding of different feedback modes and closed-loop controllers. The first section provides a brief discussion of feedback control and control theory. The second section reviews the control strategies of recent Brain Machine Interfaces, neuromodulatory implants, neuroprosthetic systems, and assistive neurorobotic devices. The final section examines the different approaches to feedback in current neuroprosthetic and neurorobotic systems.
Dynamic Mechanism Design: Incentive Compatibility, Profit Maximization and Information Disclosure

We examine the design of incentive-compatible screening mechanisms for dynamic environ- ments in which the agents'types follow a (possibly non-Markov) stochastic process, decisions may be made over time and may aect the type process, and payos need not be time-separable. We derive a formula for the derivative of an agent's equilibrium payowith respect to his cur- rent type in an incentive-compatible mechanism, which summarizes all …rst-order conditions for incentive compatibility and generalizes Mirrlees's envelope formula of static mechanism design. We provide conditions on the environment under which this formula must hold in any incentive- compatible mechanism. When specialized to quasi-linear environments, this formula yields a dynamic "revenue-equivalence"result and an expression for dynamic virtual surplus, which is instrumental for the design of optimal mechanisms. We also provide some su¢ cient conditions for incentive compatibility, and for its robustness to an agent's observation of the other agents' past and future types. We apply these results to a number of novel settings, including the de- sign of pro…t-maximizing auctions and durable-good selling mechanisms for buyers whose values
Protection Afforded by Sickle-cell Trait Against Subtertian Malarial Infection

In view of the regional differences in sensitivity found in the earlier experiments one would expect that the units responding, for exampk, to acetone would be found more often in the front part of the bulb and those responding to heavy oil at the back. There is undoubtedly a segregation of this kind, although, in the middle region, unit discharges have been obtained with substances from all the groups. It is true that most of these units can be made to discharge to a wide range of stimuli if the concentration is raised, but there will always be outlying parts of the olfactory organ where it has the threshold value needed to bring out the differential effects.
Intestinal degradation in pigs of rye dietary fibre with different structural characteristics

In order to investigate the effects of dietary fibre (DF) characteristics on carbohydrate degradation and the metabolism in the large intestine, pigs were fed on four rye-bread diets (based on whole rye, pericarp/testa, aleurone or endosperm) with differences in characteristics and amount of DF. The degradability of DF in the large intestine varied greatly between diets. The pericarp/testa DF was hardly degraded in the large intestine, whereas endosperm DF was extensively and rapidly degraded in the caecum. Caecal degradation of aleurone DF was also limited, leaving more material to be degraded in the colon. The undegradable pericarp/testa DF was characterized by high contents of lignin, cellulose, ferulic acids and highly substituted arabinoxylans (the major DF component in rye). Ingestion of this diet resulted in increased faecal bulk and reduced transit time, but with low colonic pH and the lowest concentrations of short-chain fatty acids (SCFA). The aleurone diet, on the other hand, led to a fermentation pattern which may be considered more optimal, with lower colonic pH and higher concentrations of SCFA, in particular butyric acid. Despite the large differences in carbohydrate fermentation only minor significant effects on the presence of protein degradation products and on histological measurements (height and diameter of colonic crypts and thickness of the muscularis externa) were observed.
Developmental Cognitive Neuroscience Developmental Changes in the Reward Positivity: an Electrophysiological Trajectory of Reward Processing

Keywords: Cognitive control Reinforcement learning Development Reward positivity Anterior cingulate cortex Dopamine a b s t r a c t Children and adolescents learn to regulate their behavior by utilizing feedback from the environment but exactly how this ability develops remains unclear. To investigate this question, we recorded the event-related brain potential (ERP) from children (8–13 years), adolescents (14–17 years) and young adults (18–23 years) while they navigated a " virtual maze " in pursuit of monetary rewards. The amplitude of the reward positivity, an ERP component elicited by feedback stimuli, was evaluated for each age group. A current theory suggests the reward positivity is produced by the impact of reinforcement learning signals carried by the midbrain dopamine system on anterior cingulate cortex, which utilizes the signals to learn and execute extended behaviors. We found that the three groups produced a reward positivity of comparable size despite relatively longer ERP component latencies for the children, suggesting that the reward processing system reaches maturity early in development. We propose that early development of the midbrain dopamine system facilitates the development of extended goal-directed behaviors in anterior cingulate cortex.
Retrieval in Long-Surveillance Videos Using User-Described Motion and Object Attributes

We present a content-based retrieval method for long-surveillance videos in wide-area (airborne) and near-field [closed-circuit television (CCTV)] imagery. Our goal is to retrieve video segments, with a focus on detecting objects moving on routes, that match user-defined events of interest. The sheer size and remote locations where surveillance videos are acquired necessitates highly compressed representations that are also meaningful for supporting user-defined queries. To address these challenges, we archive long-surveillance video through lightweight processing based on low-level local spatiotemporal extraction of motion and object 2. These are then hashed into an inverted index using locality-sensitive hashing. This local approach allows for query flexibility and leads to significant gains in compression. Our second task is to extract partial matches to user-created queries and assemble them into full matches using dynamic programming (DP). DP assembles the indexed low-level features into a video segment that matches the query route by exploiting causality. We examine CCTV and airborne footage, whose low contrast makes motion extraction more difficult. We generate robust motion estimates for airborne data using a tracklets generation algorithm, while we use the Horn and Schunck approach to generate motion estimates for CCTV. Our approach handles long routes, low contrasts, and occlusion. We derive bounds on the rate of false positives and demonstrate the effectiveness of the approach for counting, motion pattern recognition, and abandoned object applications.
A case for neuromorphic ISAs

The desire to create novel computing systems, paired with recent advances in neuroscientific understanding of the brain, has led researchers to develop neuromorphic architectures that emulate the brain. To date, such models are developed, trained, and deployed on the same substrate. However, excessive co-dependence between the substrate and the algorithm prevents portability, or at the very least requires reconstructing and retraining the model whenever the substrate changes. This paper proposes a well-defined abstraction layer -- the Neuromorphic instruction set architecture, or NISA -- that separates a neural application's algorithmic specification from the underlying execution substrate, and describes the Aivo framework, which demonstrates the concrete advantages of such an abstraction layer. Aivo consists of a NISA implementation for a rate-encoded neuromorphic system based on the cortical column abstraction, a state-of-the-art integrated development and runtime environment (IDE), and various profile-based optimization tools. Aivo's IDE generates code for emulating cortical networks on the host CPU, multiple GPGPUs, or as boolean functions. Its runtime system can deploy and adaptively optimize cortical networks in a manner similar to conventional just-in-time compilers in managed runtime systems (e.g. Java, C#). We demonstrate the abilities of the NISA abstraction by constructing a cortical network model of the mammalian visual cortex, deploying on multiple execution substrates, and utilizing the various optimization tools we have created. For this hierarchical configuration, Aivo's profiling based network optimization tools reduce the memory footprint by 50% and improve the execution time by a factor of 3x on the host CPU. Deploying the same network on a single GPGPU results in a 30x speedup. We further demonstrate that a speedup of 480x can be achieved by deploying a massively scaled cortical network across three GPGPUs. Finally, converting a trained hierarchical network to C/C++ boolean constructs on the host CPU results in 44x speedup.