Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPUTATIONAL MODEL FOR PREDICTION OF TRANSMEMBRANE HELICES IN HIV Anubha Dubey *

Genomics and proteomics have added valuable information to our knowledgebase of the human biological system including the discovery of therapeutic targets and disease biomarkers. In order to aid in the identification of membrane proteins, a number of computational methods have been developed. These tools operate by predicting the presence of transmembrane segments. Here we utilize SOSUI prediction method to classify amino acid sequences by two types of transmembrane helices, primary and secondary transmembrane segments. In this work, we have analyzed HIV protein dataset using the SOSUI system which not only predicts transmembrane helix regions but also classifies them whether the protein is soluble or membrane. Further a computational model is being developed by machine learning with fair accuracy.
Modeling simple-cell direction selectivity with normalized, half-squared, linear operators.

1. A longstanding view of simple cells is that they sum their inputs linearly. However, the linear model falls short of a complete account of simple-cell direction selectivity. We have developed a nonlinear model of simple-cell responses (hereafter referred to as the normalization model) to explain a larger body of physiological data. 2. The normalization model consists of an underlying linear stage along with two additional nonlinear stages. The first is a half-squaring nonlinearity; half-squaring is half-wave rectification followed by squaring. The second is a divisive normalization non-linearity in which each model cell is suppressed by the pooled activity of a large number of cells. 3. By comparing responses with counterphase (flickering) gratings and drifting gratings, researchers have demonstrated that there is a nonlinear contribution to simple-cell responses. Specifically they found 1) that the linear prediction from counterphase grating responses underestimates a direction index computed from drifting grating responses, 2) that the linear prediction correctly estimates responses to gratings drifting in the preferred direction, and 3) that the linear prediction overestimates responses to gratings drifting in the nonpreferred direction. 4. We have simulated model cell responses and derived mathematical expressions to demonstrate that the normalization model accounts for this empirical data. Specifically the model behaves as follows. 1) The linear prediction from counterphase data underestimates the direction index computed from drifting grating responses. 2) The linear prediction from counterphase data overestimates the response to gratings drifting in the nonpreferred direction. The discrepancy between the linear prediction and the actual response is greater when using higher contrast stimuli. 3) For an appropriate choice of contrast, the linear prediction from counterphase data correctly estimates the response to gratings drifting in the preferred direction. For higher contrasts the linear prediction overestimates the actual response, and for lower contrasts the linear prediction underestimates the actual response. 5. In addition, the normalization model is qualitatively consistent with data on the dynamics of simple-cell responses. Tolhurst et al. found that simple cells respond with an initial transient burst of activity when a stimulus first appears. The normalization model behaves similarly; it takes some time after a stimulus first appears before the model cells are fully normalized. We derived the dynamics of the model and found that the transient burst of activity in model cells depends in a particular way on stimulus contrast. The burst is short for high-contrast stimuli and longer for low-contrast stimuli. (ABSTRACT TRUNCATED AT 400 WORDS)
COMPETITIVE PRIORITIES: INVESTIGATING THE NEED FOR TRADE‐OFFS IN OPERATIONS STRATEGY

A heated debate continues over the need for trade‐offs in operations strategy. Some researchers call for plants to focus on a single manufacturing capability and devote their limited resources accordingly, while others claim that advanced manufacturing technology (amt) enables concurrent improvements in quality, cost, flexibility, and delivery. Yet there is little empirical evidence for or against the trade‐off model. In response, this study addresses the question: “To what extent do manufacturing plants view competitive priorities as trade‐offs? ” We employ survey data collected from managers and operators in 110 plants that have recently implemented AMT. Our findings suggest that trade‐offs remain. However, perceived differences in competitive priorities are subtle and may vary across levels of the plant hierarchy.
The relativistic Burgers equation on a de Sitter spacetime. Derivation and finite volume approximation

The inviscid Burgers equation is one of the simplest nonlinear hyperbolic conservation law which provides a variety examples for many topics in nonlinear partial differential equations such as wave propagation, shocks and perturbation, and it can easily be derived by the Euler equations of compressible fluids by imposing zero pressure in the given system. Recently, several versions of the relativistic Burgers equations have been derived on different geometries such as Minkowski (flat), Schwarzshild and FLRW spacetimes by LeFloch and his collaborators. In this paper, we consider a family member of the FLRW spacetime so-called the de Sitter background, introduce some important features of this spacetime geometry with its metric and derive the relativistic Burgers equation on it. The Euler system of equations on the de Sitter spacetime can be found by a known process by using the Christoffel symbols and tensors for perfect fluids. We applied the usual techniques used for the Schwarzshild and FLRW spacetimes in order to derive the relativistic Burgers equation from the vanishing pressure Euler system on the de Sitter background. We observed that the model admits static solutions. In the final part, we examined several numerical illustrations of the given model through a finite volume approximation based on the paper by LeFloch et al. The effect of the cosmological constant is also numerically analysed in this part. Furthermore, a comparison of the static solution with the Lax Friedrichs scheme is implemented so that the results demonstrate the efficiency and robustness of the finite volume scheme for the derived model
Understanding the Contribution of Non-source Documents in Improving Missing Link Recovery: An Empirical Study

Background: Links between issue reports and their fixing commits play an important role in software maintenance. Such link data are often missing in practice and many approaches have been proposed in order to recover them automatically. Most of existing approaches focus on comparing log messages and source code files in commits with issues reports. Besides the two kinds of data in commits, non-source documents (NSDs) such as change logs usually record the fixing activities and sometimes share similar texts as those in issue reports. However, few discussions have been made on the role of NSDs in designing link recovery approaches. Aims: This paper aims at understanding whether and how NSDs affect the performance of link recovery approaches. Method: An empirical study is conducted to evaluate the role of NSDs in link recovery approaches in 18 open source projects with 6370 issues and 22761 commits. Results: With the inclusion of NSDs, link recovery approaches can get an average increase in F-Measure ranging from 2.76% - 25.63%. Further examinations show NSDs contribute to the performance improvement in 15 projects and have exceptions in 3 projects. The performance improvement in the 15 projects is mainly from the filtering of noisy links. On average, 23.59% - 76.30% false links can be excluded by exploiting NSDs in the link recovery approach. We also analyze the 3 projects in which NSDs cannot improve the performance. Our finding shows sophisticated data selection in NSDs is necessary. Conclusions: Our preliminary findings demonstrate that involving NSDs can improve the performance of link recovery approaches in most cases.
A General Method to Discover Epitopes from Sera

Antigen-antibody complexes are central players in an effective immune response. However, finding those interactions relevant to a particular disease state can be arduous. Nonetheless many paths to discovery have been explored since deciphering these interactions can greatly facilitate the development of new diagnostics, therapeutics, and vaccines. In silico B cell epitope mapping approaches have been widely pursued, though success has not been consistent. Antibody mixtures in immune sera have been used as handles for biologically relevant antigens, but these and other experimental approaches have proven resource intensive and time consuming. In addition, these methods are often tailored to individual diseases or a specific proteome, rather than providing a universal platform. Most of these methods are not able to identify the specific antibody’s epitopes from unknown antigens, such as un-annotated neo antigens in cancer. Alternatively, a peptide library comprised of sequences unrestricted by naturally-found protein space provides for a universal search for mimotopes of an antibody’s epitope. Here we present the utility of such a non-natural random sequence library of 10,000 peptides physically addressed on a microarray for mimotope discovery without sequence information of the specific antigen. The peptide arrays were probed with serum from an antigen-immunized rabbit, or alternatively probed with serum pre-absorbed with the same immunizing antigen. With this positive and negative screening scheme, we identified the library-peptides as the mimotopes of the antigen. The unique library peptides were successfully used to isolate antigen-specific antibodies from complete immune serum. Sequence analysis of these peptides revealed the epitopes in the immunized antigen. We present this method as an inexpensive, efficient method for identifying mimotopes of any antibody’s targets. These mimotopes should be useful in defining both components of the antigen-antibody complex.
Impaired progenitor cell function in HIV-negative infants of HIV-positive mothers results in decreased thymic output and low CD4 counts.

Hematologic and immunologic functions were examined in 19 HIV-negative infants of HIV-positive mothers and 19 control infants of HIV-negative mothers. Control infants were selected to match for gestational age, weight, and mode of delivery. Cord blood was obtained from all infants and used for flow cytometric determination of lymphocyte subsets, including the naive CD4 count. Furthermore, to determine thymic output, cord blood mononuclear cells were used for determination of T-cell receptor excision circles (TRECs). Evaluation of progenitor cell function was done by means of colony-forming cell assay and fetal thymic organ cultures (FTOCs). Lower naive CD4 counts (459.3 +/- 68.9 vs 1128.9 +/- 146.8 cells/microL, P <.001) and reduced thymic output in infants of HIV-positive mothers were found (frequency of CD4(+) cells with TRECs was 3.6% +/- 0.7% compared with 14.3% +/- 2.2% in controls, P <. 001). In combination with lower red blood cell counts in infants of HIV-positive mothers, this finding suggested impairment of progenitor cell function. Indeed, progenitors from infants of HIV-positive mothers had decreased cloning efficiency (15.7% +/- 2.6% vs 55.8% +/- 15.9%, P =.009) and seemed to generate fewer T cells in FTOCs. In conclusion, lower numbers of naive CD4(+) cells and reduced thymic output in HIV-negative infants of HIV-positive mothers may be due to impaired progenitor cell function.
Evaluating the Impact of TLB Misses on Future HPC Systems

TLB misses have been considered an important source of system overhead and one of the causes that limit scalability on large supercomputers. This assumption lead to HPC lightweight kernel designs that usually statically map page table entries to TLB entries and do not take TLB misses. While this approach worked for petascale clusters, programming and debugging exascale applications composed of billions of threads is not a trivial task and users have started to explore novel programming models and tools, which require a richer system software support. In this study we present a quantitative analysis of the effect of TLB misses on current and future parallel applications at scale. To provide a fair evaluation, we compare a noiseless OS (CNK) with a custom version of the same OS capable of handling TLB misses on a BG/P system (up to 4096 cores). Our methodology follows a two-step approach: we first analyze the effects of TLB misses with a low-overhead, range-checking TLB miss handler, and then simulate a more complex TLB management system through TLB noise injection. We analyze the system behavior with different page sizes and increasing number of nodes and perform a sensitivity analysis. Our results show that the overhead introduced by TLB misses on complex HPC applications from the LLNL and ANL benchmarks is below 2% if the TLB pressure is contained and/or the TLB miss handler overhead is low, even with 1MB-pages and under large TLB noise injection. These results open the possibility of implementing richer OS memory management services to satisfy the requirements of future applications and users.
Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study

We use simulated soccer to study multiagent learning. Each team''s players (agents) share action set and policy but may behave differently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare two learning algorithms: TD-Q learning with linear neural networks (TD-Q) and Probabilistic Incremental Program Evolution (PIPE). TD-Q is based on evaluation functions (EFs) mapping input/action pairs to expected reward, while PIPE searches policy space directly. PIPE uses adaptive ``probabilistic prototype trees'''' to synthesize programs that calculate action probabilities from current inputs. Our results show that TD-Q encounters several difficulties in learning appropriate shared EFs. PIPE, however, does not depend on EFs and can find good policies faster and more reliably. This suggests that in multiagent learning scenarios direct search through policy space can offer advantages over EF-based approaches.
The decay of corporations and an analysis of their business fitness landscapes

ABSTRACT Large, established, and once successful corporations and their associated brands continually emerge and disappear in any healthy, competitive business environment. However an important trend is emerging in that the average survival time for large established corporations is getting shorter and shorter. This observation is of critical importance to business analysts, investors, executives and corporate directors, for it is a core parameter in determining the long term valuation of a business. More than ever, effective strategic management needs improved tools to aid in the analysis and forecasting of the corporate life cycle, especially in a business environment undergoing rapid, discontinuous change. This research provides a detailed study of corporate survival trends, and examines and extends the existing landscapes, tools, models and indexes used to monitor the corporate life cycle. The research results in the creation of a new business fitness landscape model, introduces two new business risk parameters, creates a new business diversity fitness index, completes a macro analysis of the extinction rates associated with the 100 largest public corporations in the United States from 1955 to 2008, and applies the new tools and models to selected Fortune 100 corporations. The research finds that the mean lifetime of the United States’ largest corporations has decreased significantly over the last 53 years and that there appears to be a 15 year periodic cycle in peak decay rates. Furthermore the research establishes a new theoretical model for viewing the mean-life and half-life of large corporations and for studying the impact that related and unrelated diversification in industry participation has on a corporation's effective lifespan. In conclusion, the research suggests that established corporations don’t survive just by being the fittest, but that they survive rapid discontinuous change by finding a new optimum position in a new business fitness landscape. In a very dynamic and changeable business fitness landscape, the largest corporations which have multiple, viable, but not necessarily the fittest positions in the fitness landscape, should experience lower rates of decay. The Decay Of Corporations iii Declarations DECLARATIONS This thesis does not incorporate without acknowledgement, any material which has been previously accepted for a degree or diploma by the University or any other institution, and to the best of my knowledge and belief, it contains no material previously published or written by another person, except where due acknowledgement is made in the text of the thesis. Signed: Geoffrey P Forster, Student Number: 21464881
Inclusion Dependencies Reloaded

Inclusion dependencies form one of the most fundamental classes of integrity constraints. Their importance in classical data management is reinforced by modern applications such as data cleaning and profiling, entity resolution and schema matching. Surprisingly, the implication problem of inclusion dependencies has not been investigated in the context of SQL, the de-facto industry standard. Codd's relational model of data represents the idealized special case of SQL in which all attributes are declared NOT NULL. Driven by the SQL standard recommendation, we investigate inclusion dependencies and NOT NULL constraints under simple and partial semantics. Partial semantics is not natively supported by any SQL implementation but we show how classical results on the implication problem carry over into this context. Interestingly, simple semantics is natively supported by every SQL implementation, but we show that the implication problem is not finitely axiomatizable in this context. Resolving this conundrum we establish an optimal solution by identifying the desirable class of not-null inclusion dependencies (NNINDs) that subsumes simple and partial semantics as special cases, and whose associated implication problem has the same computational properties as inclusion dependencies in the relational model. That is, NNIND implication is 2-ary axiomatizable and PSPACE-complete to decide. Our proof techniques bring also forward a chase procedure for deciding NNIND implication, the NP-hard subclass of typed acyclic NNINDs, and the tractable subclasses of NNINDs whose arity is bounded.
Collaborative Resilience to Episodic Shocks and Surprises: A Very Long-Term Case Study of Zanjera Irrigation in the Philippines 1979–2010

This thirty-year case study uses surveys, semi-structured interviews, and content analysis to examine the adaptive capacity of Zanjera San Marcelino, an indigenous irrigation management system in the northern Philippines. This common pool resource (CPR) system exists within a turbulent social-ecological system (SES) characterized by episodic shocks such as large typhoons as well as novel surprises, such as national political regime change and the construction of large dams. The Zanjera nimbly responded to these challenges, although sometimes in ways that left its structure and function substantially altered. While a partial integration with the Philippine National Irrigation Agency was critical to the Zanjera’s success, this relationship required on-going improvisation and renegotiation. Over time, the Zanjera showed an increasing capacity to learn and adapt. A core contribution of this analysis is the integration of a CPR study within an SES framework to examine resilience, made possible the occurrence of a wide range of challenges to the Zanjera’s function and survival over the long period of study. Long-term analyses like this one, however rare, are particularly useful for understanding the adaptive and transformative dimensions of resilience.
AN EXPLICIT UPDATE SCHEME FOR INVERSE PARAMETER AND INTERFACE ESTIMATION OF PIECEWISE CONSTANT , DISCONTINUOUS COEFFICIENTS IN LINEAR ELLIPTIC PDES

We introduce a general and efficient method to recover piecewise constant coefficients occurring in elliptic partial differential equations as well as the interface where these coefficients have jump discontinuities. For this purpose, we use an output least squares approach with level set and augmented Lagrangian methods. Our formulation incorporates the inherent nature of the piecewise constant coefficients, which eliminates the need for a complicated non-linear solve at every iteration. Instead, we obtain an explicit update formula and therefore vastly speed up computation. We employ our approach to the example problems of Poisson’s equation and linear elasticity and provide the combination of simultaneously recovering coefficients and interface.
A low-complexity memoryless model for envelope tracking RF power amplifiers

This paper presents a new kind of behavioral model structure for characterizing static nonlinearities in dynamically supplied RF power amplifiers. The proposed architecture is based on the combination of several basic cells and it is focused to simplify the real-time design and implementation of adaptive shaping functions devoted to improve the efficiency in systems controlled by digital signal processors with tight computational capabilities, such is the case of handsets and small-cells.
Automated Discovery of Telic Relations for WordNet

A method is presented for automatically extending WordNet with the telic relationships proposed in Pustejovsky’s lexicon model. The method extracts telic relationships from WordNet glosses by first selecting a telic word through a pattern matcher aided by a part-of-speech tagger and then employing a word disambiguation module to select the specific meaning (synset) of the telic word. The method is shown to be fruitful, inferring a number of useful relationships.
A multi-level search framework for asynchronous cooperation of multiple hyper-heuristics

In this paper, we propose an agent-based multi-level search framework for the asynchronous cooperation of hyper-heuristics. This framework contains a population of different hyper-heuristic agents and a coordinator agent. Each hyper-heuristic agent operates on the same set of low level heuristics, while the coordinator agent operates on top of all the hyper-heuristic agents. Starting from the same initial solution, each hyper-heuristic agent performs a search over the space generated by the low level heuristics. The hyper-heuristic agents cooperate asynchronously through the coordinator agent by exchanging their elite solutions. The coordinator agent maintains a pool of elite solutions and manages the communication between the hyper-heuristics agents. Preliminary computational experiments have been carried out on a set of permutation flow shop benchmark instances. The results illustrated the superior performance of the multi-level framework for asynchronous cooperation of hyper-heuristics.