Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative analyses of the two proliferating cell nuclear antigens from the hyperthermophilic archaeon, Thermococcus kodakarensis

The DNA sliding clamp is a multifunctional protein involved in cellular DNA transactions. In Archaea and Eukaryota, proliferating cell nuclear antigen (PCNA) is the sliding clamp. The ring‐shaped PCNA encircles double‐stranded DNA within its central hole and tethers other proteins on DNA. The majority of Crenarchaeota, a subdomain of Archaea, have multiple PCNA homologues, and they are capable of forming heterotrimeric rings for their functions. In contrast, most organisms in Euryarchaeota, the other major subdomain, have a single PCNA forming a homotrimeric ring structure. Among the Euryarchaeota whose genome is sequenced, Thermococcus kodakarensis is the only species with two genes encoding PCNA homologues on its genome. We cloned the two genes from the T. kodakarensis genome, and the gene products, PCNA1 and PCNA2, were characterized. PCNA1 stimulated the DNA synthesis reactions of the two DNA polymerases, PolB and PolD, from T. kodakarensis in vitro. PCNA2, however, only had an effect on PolB. We were able to disrupt the gene for PCNA2, whereas gene disruption for PCNA1 was not possible, suggesting that PCNA1 is essential for DNA replication. The sensitivities of the Δpcna2 mutant strain to ultraviolet irradiation (UV), methyl methanesulfonate (MMS) and mitomycin C (MMC) were indistinguishable from those of the wild‐type strain.
Improved Version of 128 Bit Binary Adder design using QCA with Area Efficiency and High Speed

Among the emerging technologies recently proposed as alternatives to the classic CMOS, Quantum-dot cellular automata (QCA) is one of the most promising solutions to design area efficient and very high speed digital circuits. As transistors decrease in size more and more of them can be accommodated in a single die, thus increasing chip computational capabilities. However, transistors cannot get much smaller than their current size. According to recent analysis the minimum limit for transistor size may be reached. The quantum-dot cellular automata (QCA) approach represents one of the possible solutions in overcoming this physical limit, even though the design of logic modules in QCA is not always straightforward. In this survey a binary adder is taken for analysis and a new adder is designed based upon QCA technology. The aim of this proposed technique is that to reducing number of majority gates used in the design. This will lead to reduce number of QCA cells so that total area of binary adder circuit can be minimized compare to previous designs. The proposed 128 Bit QCA adder is implemented on Xilinx ISE and designed using Verilog HDL.
Analysis of covariance with pre‐treatment measurements in randomized trials under the cases that covariances and post‐treatment variances differ between groups

When primary endpoints of randomized trials are continuous variables, the analysis of covariance (ANCOVA) with pre‐treatment measurements as a covariate is often used to compare two treatment groups. In the ANCOVA, equal slopes (coefficients of pre‐treatment measurements) and equal residual variances are commonly assumed. However, random allocation guarantees only equal variances of pre‐treatment measurements. Unequal covariances and variances of post‐treatment measurements indicate unequal slopes and, usually, unequal residual variances. For non‐normal data with unequal covariances and variances of post‐treatment measurements, it is known that the ANCOVA with equal slopes and equal variances using an ordinary least‐squares method provides an asymptotically normal estimator for the treatment effect. However, the asymptotic variance of the estimator differs from the variance estimated from a standard formula, and its property is unclear. Furthermore, the asymptotic properties of the ANCOVA with equal slopes and unequal variances using a generalized least‐squares method are unclear. In this paper, we consider non‐normal data with unequal covariances and variances of post‐treatment measurements, and examine the asymptotic properties of the ANCOVA with equal slopes using the variance estimated from a standard formula. Analytically, we show that the actual type I error rate, thus the coverage, of the ANCOVA with equal variances is asymptotically at a nominal level under equal sample sizes. That of the ANCOVA with unequal variances using a generalized least‐squares method is asymptotically at a nominal level, even under unequal sample sizes. In conclusion, the ANCOVA with equal slopes can be asymptotically justified under random allocation.
Dynamics of agribusiness decision making in Uganda

Uganda is a developing country in East Africa that faces high poverty rates among a confluence of socio-political issues. In the pursuit of fiscal and political stability, international development organizations are working to create an environment in which business prospers and livelihoods are resilient. Agriculture, in particular, employs 75% of the population and accounts for 85% of Uganda's export earnings. However, many smallholder farmers experience volatile cashflows around crop cycles. Consequently, farmer livelihoods are vulnerable to uncertain growing conditions, market price fluctuations, and financial shocks, such as hospital bills or school fees. Subject to a high degree of uncertainty, it is difficult for farmers to improve their quality of life through agriculture alone. Agribusinesses--commodity traders in particular-are well positioned to help farmers access knowledge, goods, and services to increase yields and improve crop quality In some cases, provision of knowledge, goods, and services aligns with an agribusiness's regular profit-earning strategies. For example, some traders provide farmers with financing to purchase seeds and fertilizer at the beginning of the growing season; the farmer benefits from less cash volatility while the trader ensures crops will be available at harvest. This thesis explores the extent to which trader business strategies might be leveraged to mutually benefit agribusiness growth and farmer livelihoods. Specific goals were to identify common business strategies used by agricultural commodity traders and understand the mechanisms by which certain business strategies also benefit farmers. Semi-structured interviews and subsequent qualitative analysis shed light on the types of business strategies traders adopt. System dynamics modeling was then used to explain why certain strategies can be mutually beneficial. The combination of qualitative and quantitative methods yielded unique insight on ways mutually-beneficial behavior might be encouraged. Thesis Supervisor: Jarrod Goentzel Title: Director, Humanitarian Supply Chain Lab Research Associate, Center for Transportation and Logistics
Aquaporin 1 – a novel player in spinal cord injury

The role of water channel aquaporin 1 (AQP‐1) in uninjured or injured spinal cords is unknown. AQP‐1 is weakly expressed in neurons and gray matter astrocytes, and more so in white matter astrocytes in uninjured spinal cords, a novel finding. As reported before, AQP‐1 is also present in ependymal cells, but most abundantly in small diameter sensory fibers of the dorsal horn. Rat contusion spinal cord injury (SCI) induced persistent and significant four‐ to eightfold increases in AQP‐1 levels at the site of injury (T10) persisting up to 11 months post‐contusion, a novel finding. Delayed AQP‐1 increases were also found in cervical and lumbar segments, suggesting the spreading of AQP‐1 changes over time after SCI. Given that the antioxidant melatonin significantly decreased SCI‐induced AQP‐1 increases and that hypoxia inducible factor‐1α was increased in acutely and chronically injured spinal cords, we propose that chronic hypoxia contributes to persistent AQP‐1 increases after SCI. Interestingly; AQP‐1 levels were not affected by long‐lasting hypertonicity that significantly increased astrocytic AQP‐4, suggesting that the primary role of AQP‐1 is not regulating isotonicity in spinal cords. Based on our results we propose possible novel roles for AQP‐1 in the injured spinal cords: (i) in neuronal and astrocytic swelling, as AQP‐1 was increased in all surviving neurons and reactive astrocytes after SCI and (ii) in the development of the neuropathic pain after SCI. We have shown that decreased AQP‐1 in melatonin‐treated SCI rats correlated with decreased AQP‐1 immunolabeling in the dorsal horns sensory afferents, and with significantly decreased mechanical allodynia, suggesting a possible link between AQP‐1 and chronic neuropathic pain after SCI.
Predicting text quality: Metrics for content, organization and reader interest

When people read articles---news, fiction or technical---most of the time if not always, they form perceptions about its quality. Some articles are well-written and others are poorly written. This thesis explores if such judgements can be automated so that they can be incorporated into applications such as information retrieval and automatic summarization. Text quality does not involve a single aspect but is a combination of numerous and diverse criteria including spelling, grammar, organization, informative nature, creative and beautiful language use, and page layout. In the education domain, comprehensive lists of such properties are outlined in the rubrics used for assessing writing. But computational methods for text quality have addressed only a handful of these aspects, mainly related to spelling, grammar and organization. In addition, some text quality aspects could be more relevant for one genre versus another. But previous work have placed little focus on specialized metrics based on the genre of texts. This thesis proposes new insights and techniques to address the above issues. We introduce metrics that score varied dimensions of quality such as content, organization and reader interest. For content, we present two measures: specificity and verbosity level. Specificity measures the amount of detail present in a text while verbosity captures which details are essential to include. We measure organization quality by quantifying the regularity of the intentional structure in the article and also using the specificity levels of adjacent sentences in the text. Our reader interest metrics aim to identify engaging and interesting articles. The development of these measures is backed by the use of articles from three different genres: academic writing, science journalism and automatically generated summaries. Proper presentation of content is critical during summarization because summaries have a word limit. Our specificity and verbosity metrics are developed with this genre as the focus. The argumentation structure of academic writing lends support to the idea of using intentional structure to model organization quality. Science journalism articles convey research findings in an engaging manner and are ideally suited for the development and evaluation of measures related to reader interest. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Ani Nenkova This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/665 Keywords readability, text quality, writing quality Subject Categories Computer Sciences This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/665 PREDICTING TEXT QUALITY: METRICS FOR CONTENT, ORGANIZATION AND READER INTEREST Annie Priyadarshini Louis A DISSERTATION in Computer and Information Science Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
The Unequal Geographic Burden of Federal Taxes and Its Consequences: A Case for Tax Deductions?

Because federal income taxes are based on nominal incomes, workers with the same real incomes pay more taxes in high-cost areas than in low-cost areas, without receiving more in benefits. In the United States, workers in cities offering above-average nominal wages – cities with high productivity, low quality-of-life, or inefficient housing sectors – can expect to pay 30 percent more in federal taxes, on average, than identical workers with the same real incomes in cities offering below-average nominal wages. Federal taxes induce workers to leave high-wage areas, which according to a calibrated model, lower long-run employment levels in these areas by about 15 percent and land and housing prices by 25 and 4 percent; the opposite occurs in low-wage areas. This leads to an inefficient distribution of employment, costing about 0.28 percent of total income a year, or $34 billion in 2005. Workers will locate more efficiently if taxes are appropriately indexed to local wage levels; indexing taxes to local costs may also improve efficiency although it would induce too many workers to live in expensive, high quality-of-life cities. Deductions in the tax code, for housing and property taxes, index taxes partially to local costs, helping workers locate somewhat more efficiently, but creating larger losses by distorting consumption choices. Changes in relative wages and housing prices across cities over the 1980s and 1990s are roughly consistent with predicted changes from federal tax changes, but are too noisy to calibrate the model directly.
Enlarging Context with Low Cost: Efficient Arithmetic Coding with Trimmed Convolution

Arithmetic coding is an essential class of coding techniques. One key issue of arithmetic encoding method is to predict the probability of the current coding symbol from its context, i.e., the preceding encoded symbols, which usually can be executed by building a look-up table (LUT). However, the complexity of LUT increases exponentially with the length of context. Thus, such solutions are limited to modeling large context, which inevitably restricts the compression performance. Several recent deep neural network-based solutions have been developed to account for large context, but are still costly in computation. The inefficiency of the existing methods are mainly attributed to that probability prediction is performed independently for the neighboring symbols, which actually can be efficiently conducted by shared computation. To this end, we propose a trimmed convolutional network for arithmetic encoding (TCAE) to model large context while maintaining computational efficiency. As for trimmed convolution, the convolutional kernels are specially trimmed to respect the compression order and context dependency of the input symbols. Benefited from trimmed convolution, the probability prediction of all symbols can be efficiently performed in one single forward pass via a fully convolutional network. Furthermore, to speed up the decoding process, a slope TCAE model is presented to divide the codes from a 3D code map into several blocks and remove the dependency between the codes inner one block for parallel decoding, which can 60x speed up the decoding process. Experiments show that our TCAE and slope TCAE attain better compression ratio in lossless gray image compression, and can be adopted in CNN-based lossy image compression to achieve state-of-the-art rate-distortion performance with real-time encoding speed.
Multispectral constancy for illuminant invariant representation of multispectral images. (Constance multispectrale pour la représentation invariante à l'illuminant d'images multispectrales)

A conventional color imaging system provides high resolution spatial information and low resolution spectral data. In contrast, a multispectral imaging system is able to provide both the spectral and spatial information of a scene in high resolution. A multispectral imaging system is complex and it is not easy to use it as a hand held device for acquisition of data in uncontrolled conditions. The use of multispectral imaging for computer vision applications has started recently but is not very efficient due to these limitations. Therefore, most of the computer vision systems still rely on traditional color imaging and the potential of multispectral imaging for these applications has yet to be explored. With the advancement in sensor technology, hand held multispectral imaging systems are coming in market. One such example is the snapshot multispectral filter array camera. So far, data acquisition from multispectral imaging systems require specific imaging conditions and their use is limited to a few applications including remote sensing and indoor systems. Knowledge of scene illumination during multispectral image acquisition is one of the important conditions. In color imaging, computational color constancy deals with this condition while the lack of such a framework for multispectral imaging is one of the major limitation in enabling the use of multispectral cameras in uncontrolled imaging environments. In this work, we extend some methods of computational color imaging and apply them to the multispectral imaging systems. A major advantage of color imaging is the ability of providing consistent color of objects and surfaces across varying imaging conditions. In this work, we extend the concept of color constancy and white balancing from color to multispectral images, and introduce the term multispectral constancy. The validity of proposed framework for consistent representation of multispectral images is demonstrated through spectral reconstruction of material surfaces from the acquired images. We have also presented a new hyperspectral reflectance images dataset in this work. The framework of multispectral constancy will make it one step closer for the use of multispectral imaging in computer vision applications, where the spectral information, as well as the spatial information of a surface will be able to provide distinctive useful features for material identification and classification tasks. ​
An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression

Multi-response linear models aggregate a set of vanilla linear models by assuming correlated noise across them, which has an unknown covariance structure. To find the coefficient vector, estimators with a joint approximation of the noise covariance are often preferred than the simple linear regression in view of their superior empirical performance, which can be generally solved by alternating-minimization type procedures. Due to the non-convex nature of such joint estimators, the theoretical justification of their efficiency is typically challenging. The existing analyses fail to fully explain the empirical observations due to the assumption of resampling on the alternating procedures, which requires access to fresh samples in each iteration. In this work, we present a resampling-free analysis for the alternating minimization algorithm applied to the multi-response regression. In particular, we focus on the high-dimensional setting of multi-response linear models with structured coefficient parameter, and the statistical error of the parameter can be expressed by the complexity measure, Gaussian width, which is related to the assumed structure. More importantly, to the best of our knowledge, our result reveals for the first time that the alternating minimization with random initialization can achieve the same performance as the well-initialized one when solving this multi-response regression problem. Experimental results support our theoretical developments.
Enforcing Optimal ACL Policies Using K-Partite Graph in Hybrid SDN

Software Defined Networking (SDN) as an innovative network paradigm that separates the management and control planes from the data plane of forwarding devices by implementing both the management and control planes at a logically centralized entity, called controller. Therefore, it ensures simple network management and control. However, due to several reasons (e.g., deployment cost, fear of downtime) organizations are very reluctant to adopt SDN in practice. Therefore, a viable solution is to replace the legacy devices by SDN devices incrementally. This results in a new network architecture called hybrid SDN. In hybrid SDN, both SDN and legacy devices operate in such a way to achieve the maximum benefit of SDN. The legacy devices are running a traditional protocol and SDN devices are operating using Open-flow protocols. Network policies play an essential role to secure the entire network from several types of attacks like unauthorized access and port/protocol control. In a hybrid SDN, policy implementation is a tedious task that requires extreme care and attention due to the hybrid nature of network traffic. Network policies may be implemented at various positions in hybrid SDN, e.g., near the destination or source node, and at the egress or ingress ports of a router. Each of these schemes has some trade-offs. For example, if policies are implemented near the source nodes then each packet generated from the source must pass through the filter and, thus, requires more processing power, time, resources, etc. Similarly, if policies are installed near the destination nodes, then a lot of unwanted traffic generated causing network congestion. This is an NP-hard problem. To address these challenges, we propose a systematic design approach to implement network policies optimally by using decision tree and K-partite graph. By traversing all the policies, we built up the decision tree that identifies which source nodes can communicate with which destination. Then, we traverse the decision tree and constructs K-partite graph to find possible places (interfaces of the routers) where ACL policies are to be implemented based on the different criteria (i.e., the minimum number of ACL rules and the minimum number of transmissions for unwanted traffic). The edge weight represents the cost per criteria. Then, we traverse the K-partite graph to find the optimal place for ACL rules implementation according to the given criteria. The simulation results indicate that the proposed technique outperforms existing approaches in terms of computation time, traffic optimization and successful packet delivery, etc. The results also indicate that the proposed method improves network performance and efficiency by decreasing network congestion and providing ease of policy implementation.
A novel single-inductor eight-channel light-emitting diode driver for low power display backlight applications

A novel decoder-based single-inductor eight-channel light-emitting diode (LED) driver circuit for low power display backlight applications has been proposed. Uniform brightness in the display provides better picture clarity, which can be achieved by providing uniform DC currents to all channels in the backlight arrangement. Existing systems use individual current regulators for each channel, which fails to provide uniform current to individual channels. Instead, uniform current is provided to all eight channels in the proposed system as the same current is distributed to all channels using time-multiplexing by a 3 × 8 decoder and a 3-bit binary up-counter. A digital pulse width modulator is used in the existing system, which has unwanted switching activities and electromagnetic interference (EMI). Unwanted switching activities and EMI are completely eliminated in the proposed system by applying a novel switching technique. The proposed LED driver is designed and implemented using the 180-nm CMOS process. Each channel is designed to conduct 300 mA of current through it when the supply is 12 V. The proposed LED driver consumes total power of 8.746 W at 27 ◦ C while the peak power efficiency is 96.26%. The minimum current balancing error of the proposed LED driver is 0.019%, whereas it is 0.12% for the existing system. The existing method exhibits an average current balancing error of 2.04% whereas the proposed technique exhibits 1.33% as the average current balancing error, which is 34.82% less than the existing system.