Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracking Problems, Hedge Fund Replication and Alternative Beta

As hedge fund replication based on factor models has encountered growing interest among professionals and academics, and despite the launch of numerous products (indexes and mutual funds) in the past year, it faced many critics. In this paper, we consider three of the main critiques, namely the lack of reactivity of hedge fund replication and its deficiency in capturing tactical allocations; its failure to apprehend non-linear positions of the underlying hedge fund industry and higher moments of hedge fund returns; and, finally, the lack of access to the alpha of hedge funds. To address these problems, we consider hedge fund replication as a general tracking problem which may be solved by means of Bayesian filters. Using the linear Gaussian model as a basis for discussion, we provide the reader with an intuition for the inner tenets of the Kalman filter and illustrate the results' sensitivity to the algorithm specification choices. This part of the paper includes considerations on the type of strategies which can be replicated, as well as the problem of selecting factors. We then apply more advanced Bayesian filters' algorithms, known as particle filters, to capture the non-normality and non-linearities documented on hedge fund returns. Finally, we address the problem of accessing the pure alpha by proposing a core/satellite approach of alternative investments between high-liquid alternative beta and less liquid investments.
Strathprints Institutional Repository

Query performance issues over semi-structured data have led to the emergence of materialised XML views as a means of restricting the data structure processed by a query. However preserving the conventional representation of such views remains a signiﬁcant limiting factor especially in the context of mobile devices where processing power, memory usage and bandwidth are signiﬁcant factors. To explore the concept of a compressed materialised view, we extend our earlier work on structural XML compression to produce a combina-tion of structural summarisation and data compression techniques. These techniques provide a basis for eﬃciently dealing with both structural queries and value-based predicates. We evaluate the eﬀectiveness of such a scheme, presenting results and performance measures that show advantages of using such structures.
A Hybrid Granularity Graph for Improving Adaptive Application Partitioning Efficacy in Mobile Computing Environments

The feasibility of using adaptive object migration to enable the execution of heavy applications in pervasive environments, is determined by the computational efficiency of adaptation algorithms and the efficacy of their decisions. These two factors, which are largely predicated by the resource constraints of devices, are heavily influenced by the granularity at which adaptation decisions are performed. This paper proposes a new type of adaptation granularity which combines the efficiency of coarse level approaches with the efficacy of fine-grained adaptation. A novel approach for achieving this level of granularity through the dynamic decomposition of runtime class graphs is presented and empirically evaluated on a corpus of real world applications. It is shown that the approach improves the efficacy of adaptation decisions by reducing network overheads by a minimum of 17% to as much 99%, while maintaining comparable decision making efficiency to class level adaptation.
ANALYTICAL MODELS FOR CHIP MULTIPROCESSOR MEMORY HIERARCHY DESIGN AND MANAGEMENT

Continued advances in circuit integration technology has ushered in the era of chip multiprocessor (CMP) architectures as further scaling of the performance of conventional wide-issue superscalar processor architectures remains hard and costly. CMP architectures take advantageof Moorei¯s Law by integrating more cores in a given chip area rather than a single fastyet larger core. They achieve higher performance with multithreaded workloads. However,CMP architectures pose many new memory hierarchy design and management problems thatmust be addressed. For example, how many cores and how much cache capacity must weintegrate in a single chip to obtain the best throughput possible? Which is more effective,allocating more cache capacity or memory bandwidth to a program? This thesis research develops simple yet powerful analytical models to study two newmemory hierarchy design and resource management problems for CMPs. First, we considerthe chip area allocation problem to maximize the chip throughput. Our model focuses onthe trade-off between the number of cores, cache capacity, and cache management strategies.We find that different cache management schemes demand different area allocation to coresand cache to achieve their maximum performance. Second, we analyze the effect of cachecapacity partitioning on the bandwidth requirement of a given program. Furthermore, ourmodel considers how bandwidth allocation to different co-scheduled programs will affect theindividual programsi¯ performance. Since the CMP design space is large and simulating only one design point of the designspace under various workloads would be extremely time-consuming, the conventionalsimulation-based research approach quickly becomes ineffective. We anticipate that ouranalytical models will provide practical tools to CMP designers and correctly guide theirdesign efforts at an early design stage. Furthermore, our models will allow them to betterunderstand potentially complex interactions among key design parameters.
Evaluation of the Green Sustainable Development Ability of Chengdu Agriculture Based on Entropy Method

Agricultural green sustainable development is a necessary condition to ensure long-term economic and social development in China. Based on the data of agricultural economy and society, resources and environment, this paper uses the entropy method to evaluate the sustainable development of green agriculture in Chengdu from 2011 to 2016. The results show that the overall capability of agricultural green sustainable development in Chengdu shows an upward trend year by year, and the index of development has risen from 0.1866 in 2011 to 0.9353 in 2016. The trend of each subsystem varies from each other. The sustained development of the economic system has the fastest growth rate, with an annual growth rate of 61.81%, followed by the improvement of the environment, whereas there is a greater pressure on the sustainability of resources. Although the impact of resources on sustainable agricultural development is on an upward trend, the resource sustainability index for 2016 is 0.0437, which is still at a low level. In order to comprehensively enhance the level of agricultural green sustainable development in urban areas, the paper puts forward some countermeasures such as establishing a system of agricultural productivity distribution, establishing a standard system of green agriculture, and improving management and control of agricultural resources and environment.
Biofilm streamers cause catastrophic disruption of flow with consequences for environmental and medical systems

Biofilms are antibiotic-resistant, sessile bacterial communities that occupy most moist surfaces on Earth and cause chronic and medical device-associated infections. Despite their importance, basic information about biofilm dynamics in common ecological environments is lacking. Here, we demonstrate that flow through soil-like porous materials, industrial filters, and medical stents dramatically modifies the morphology of Pseudomonas aeruginosa biofilms to form 3D streamers, which, over time, bridge the spaces between obstacles and corners in nonuniform environments. We discovered that accumulation of surface-attached biofilm has little effect on flow through such environments, whereas biofilm streamers cause sudden and rapid clogging. We demonstrate that flow-induced shedding of extracellular matrix from surface-attached biofilms generates a sieve-like network that captures cells and other biomass, which add to the existing network, causing exponentially fast clogging independent of growth. These results suggest that biofilm streamers are ubiquitous in nature and strongly affect flow through porous materials in environmental, industrial, and medical systems.
Using overlap‐extension PCR technique to fusing genes for constructing recombinant plasmids

Overlap‐extension PCR is a method for splice of gene segments to produce focused fragments for constructing recombinant plasmid, but its complexity limits its application. To simplify the protocol and to improve the effectiveness, we employed gradient temperatures to replace the single annealing temperature in the thermo‐cycling program, and optimize the templates ratio. The concentration of each fragment was adjusted to 10 ng µl−1. Fragment concentration ratio was the inverse of the fragment size ratio. The products of fused segments were 2000–5000 bp in length using the revised one‐step method. This method splices effective two or more fragments to fused gene and produce recombinant plasmid.
Postinjury life threatening coagulopathy: is 1:1 fresh frozen plasma:packed red blood cells the answer?

BACKGROUND Recent military experience suggests that immediate 1:1 fresh frozen plasma (FFP); red blood cells (RBC) for casualties requiring >10 units packed red blood cells (RBC) per 24 hours reduces mortality, but no clinical trials exist to address this issue. Consequently, we reviewed our massive transfusion practices during a 5-year period to test the hypothesis that 1:1 FFP:RBC within the first 6 hours reduces life threatening coagulopathy. METHODS We queried our level I trauma center's prospective registry from 2001 to 2006 for patients undergoing massive transfusion. Logistic regression was used to evaluate the independent effect of FFP:RBC in 133 patients who received >10 units RBC in 6 hours on (1) Coagulopathy (international normalized ratio [INR] >1.5 at 6 hours), controlling for our previously described risk factors predictive of coagulopathy, as well as RBC, FFP, and platelet administration (2) Death (controlling for all variables plus age, crystalloids per 24 hours, INR >1.5 at 6 hours). RESULTS Overall mortality was 56%; 50% died from acute blood loss in the operating room. Over 80% of the RBC transfusions were completed in the first 6 hours: (Median RBC: 18 units) Median FFP:RBC survivors, 1:2, nonsurvivors: 1:4. (p < 0.001) INR >1.5 at 6 hours occurred in 30 (23%); 81% died. Regarding mortality, logistic regression showed significant variables (p < 0.05) included: RBC per 6 hours (OR = 1.248, 95%CI: 1.957-53.255), INR at 6 hours >1.5 (OR = 10.208, 95% CI: 1.957-53.255), ED temperature <34 degrees C (OR = 15.491, 95% CI 1.376-174.396), and age >55 years (OR = 40.531, CI 5.315-309.077). The adjusted OR for FFP:RBC ratio including the quadratic term was found to follow a U-shaped association (quadratic term estimate 0.6737 +/- 0.0345, p = 0.0189). CONCLUSION Although our data suggest that 1:1 FFP:RBC reduced coagulopathy, this did not translate into a survival benefit. Our findings indicate that the relationship between coagulopathy and mortality is more complex, and further clinical investigation is necessary before recommending routine 1:1 in the exsanguinating trauma patient.
Online vehicle detection using deep neural networks and lidar based preselected image patches

In this paper we present a vehicle detection system using convolutional neural networks on 2d image data. Since realtime capabilities are crucial for object detection systems running in real-traffic situations, we will show how the calculation time of our algorithm can be significantly reduced by taking advantage of depth information from lidar sensors. One part of this work focusses on useful network topologies and network parameters to increase the classification precision. We will test the presented algorithm on an autonomous car in different real-traffic scenarios with regards to detection accuracy and calculation time and show experimental results.
Circuit design of a dual-versioning L1 data cache for optimistic concurrency

This paper proposes a novel L1 data cache design with dual-versioning SRAM cells (dvSRAM) for chip multi-processors (CMP) that implement optimistic concurrency proposals. In this new cache architecture, each dvSRAM cell has two cells, a main cell and a secondary cell, which keep two versions of the same data. These values can be accessed, modified, moved back and forth between the main and secondary cells within the access time of the cache. We design and simulate a 32-KB dual-versioning L1 data cache with 45-nm CMOS technology at 2GHz processor frequency and 1V supply voltage, which we describe in detail. We also introduce three well-known use cases that make use of optimistic concurrency execution and that can benefit from our proposed design. Moreover, we evaluate one of the use cases to show the impact of the dual-versioning cell in both performance and energy consumption. Our experiments show that large speedups can be achieved with acceptable overall energy dissipation.
Learning Multi Character Alignment Rules and Classification of Training Data for Transliteration

We address the issues of transliteration between Indian languages and English, especially for named entities. We use an EM algorithm to learn the alignment between the languages. We find that there are lot of ambiguities in the rules mapping the characters in the source language to the corresponding characters in the target language. Some of these ambiguities can be handled by capturing context by learning multi-character based alignments and use of character n-gram models. We observed that a word in the source script may have actually originated from different languages. Instead of learning one model for the language pair, we propose that one may use multiple models and a classifier to decide which model to use. A contribution of this work is that the models and classifiers are learned in a completely unsupervised manner. Using our system we were able to get quite accurate transliteration models.
A Polyadenylation Factor Subunit Implicated in Regulating Oxidative Signaling in Arabidopsis thaliana

Background Plants respond to many unfavorable environmental conditions via signaling mediated by altered levels of various reactive oxygen species (ROS). To gain additional insight into oxidative signaling responses, Arabidopsis mutants that exhibited tolerance to oxidative stress were isolated. We describe herein the isolation and characterization of one such mutant, oxt6. Methodology/Principal Findings The oxt6 mutation is due to the disruption of a complex gene (At1g30460) that encodes the Arabidopsis ortholog of the 30-kD subunit of the cleavage and polyadenylation specificity factor (CPSF30) as well as a larger, related 65-kD protein. Expression of mRNAs encoding Arabidopsis CPSF30 alone was able to restore wild-type growth and stress susceptibility to the oxt6 mutant. Transcriptional profiling and single gene expression studies show elevated constitutive expression of a subset of genes that encode proteins containing thioredoxin- and glutaredoxin- related domains in the oxt6 mutant, suggesting that stress can be ameliorated by these gene classes. Bulk poly(A) tail length was not seemingly affected in the oxt6 mutant, but poly(A) site selection was different, indicating a subtle effect on polyadenylation in the mutant. Conclusions/Significance These results implicate the Arabidopsis CPSF30 protein in the posttranscriptional control of the responses of plants to stress, and in particular to the expression of a set of genes that suffices to confer tolerance to oxidative stress.
eIF3f depletion impedes mouse embryonic development, reduces adult skeletal muscle mass and amplifies muscle loss during disuse

In muscular cells, eukaryotic initiation factor subunit f (eIF3f) activates protein synthesis by allowing physical interaction between mechanistic target of rapamycin complex 1 (MTORC1) and ribosomal protein S6 kinase 1 (S6K1), although its physiological role in animals is unknown. A knockout approach suggests that homozygous mice carrying a null mutation of the eIF3f gene fail to develop and consequently die at early embryonic stage, whereas heterozygous mice associated with a partial depletion of eIF3f gene grow normally and are phenotypically indistinguishable from wild‐type mice. Heterozygous mice express reduced eIF3f mRNA and protein levels in skeletal muscles and show diminished muscle mass associated with a decrease in the protein synthesis rate and an inhibition of the MTORC1 pathway. During hindlimb immobilization, heterozygous eIF3f mice display an exacerbated immobilization‐induced muscle atrophy associated with reduced protein synthesis. These results highlight the essential role of eIF3f during embryonic development and its involvement in muscular homeostasis via protein synthesis regulation.
NP Alignment in Bilingual Corpora

Aligning the NPs of parallel corpora is logically halfway between the sentence- and word-alignment tasks that occupy much of the MT literature, but has received far less attention. NP alignment is a challenging problem, capable of rapidly exposing flaws both in the word-alignment and in the NP chunking algorithms one may bring to bear. It is also a very rewarding problem in that NPs are semantically natural translation units, which means that (i) word alignments will cross NP boundaries only exceptionally, and (ii) within sentences already aligned, the proportion of 1-1 alignments will be higher for NPs than words. We created a simple gold standard for English-Hungarian, Orwells 1984, (since this already exists in manually verified POS-tagged format in many languages thanks to the Multex and MultexEast project) by manually verifying the automaticaly generated NP chunking (we used the yamcha, mallet and hunchunk taggers) and manually aligning the maximal NPs and PPs. The maximum NP chunking problem is much harder than base NP chunking, with F-measure in the . 7 range (as opposed to over .94 for base NPs). Since the results are highly impacted by the quality of the NP chunking, we tested our alignment algorithms both with real world (machine obtained) chunkings, where results are in the . 35 range for the baseline algorithm which propagates GIZA++ word alignments to the NP level, and on idealized (manually obtained) chunkings, where the baseline reaches . 4 and our current system reaches . 64.
A Simple Relational Classifier

Abstract : We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model the predicts only based on class labels of related neighbors, using no learning and no inherent attributes. We show that it performs surprisingly well by comparing it to more complex models such as Probabilistic Relational Models and Relational Probability Trees on three data sets from published work. We argue that a simple model such as this should be used as a baseline to assess the performance of relational learners.
Versatile Virus-Like Particle Carrier for Epitope Based Vaccines

Background Recombinant proteins and in particular single domains or peptides are often poorly immunogenic unless conjugated to a carrier protein. Virus-like-particles are a very efficient means to confer high immunogenicity to antigens. We report here the development of virus-like-particles (VLPs) derived from the RNA bacteriophage AP205 for epitope-based vaccines. Methodology/Principal Findings Peptides of angiotensin II, S.typhi outer membrane protein (D2), CXCR4 receptor, HIV1 Nef, gonadotropin releasing hormone (GnRH), Influenza A M2-protein were fused to either N- or C-terminus of AP205 coat protein. The A205-peptide fusions assembled into VLPs, and peptides displayed on the VLP were highly immunogenic in mice. GnRH fused to the C-terminus of AP205 induced a strong antibody response that inhibited GnRH function in vivo. Exposure of the M2-protein peptide at the N-terminus of AP205 resulted in a strong M2-specific antibody response upon immunization, protecting 100% of mice from a lethal influenza infection. Conclusions/Significance AP205 VLPs are therefore a very efficient and new vaccine system, suitable for complex and long epitopes, of up to at least 55 amino acid residues in length. AP205 VLPs confer a high immunogenicity to displayed epitopes, as shown by inhibition of endogenous GnRH and protective immunity against influenza infection.
Randomized data structures for the dynamic closest-pair problem

We describe a new randomized data structure, the sparse partition, for solving the dynamic closest-pair problem. Using this data structure the closest pair of a set of n points in D-dimensional space, for any fixed D, can be found in constant time. If a frame containing all the points is known in advance, and if the floor function is available at unit cost, then the data structure supports insertions into and deletions from the set in expected O(log n) time and requires expected O(n) space. This method is more efficient than any deterministic algorithm for solving the problem in dimension D > 1. The data structure can be modified to run in O(log2 n) expected time per update in the algebraic computation tree model. Even this version is more efficient than the best currently known deterministic algorithm for D > 2. Both results assume that the sequence of updates is not determined in any way by the random choices made by the algorithm.
Thermosensitive liposomal drug delivery systems: state of the art review

Thermosensitive liposomes are a promising tool for external targeting of drugs to solid tumors when used in combination with local hyperthermia or high intensity focused ultrasound. In vivo results have demonstrated strong evidence that external targeting is superior over passive targeting achieved by highly stable long-circulating drug formulations like PEGylated liposomal doxorubicin. Up to March 2014, the Web of Science listed 371 original papers in this field, with 45 in 2013 alone. Several formulations have been developed since 1978, with lysolipid-containing, low temperature-sensitive liposomes currently under clinical investigation. This review summarizes the historical development and effects of particular phospholipids and surfactants on the biophysical properties and in vivo efficacy of thermosensitive liposome formulations. Further, treatment strategies for solid tumors are discussed. Here we focus on temperature-triggered intravascular and interstitial drug release. Drug delivery guided by magnetic resonance imaging further adds the possibility of performing online monitoring of a heating focus to calculate locally released drug concentrations and to externally control drug release by steering the heating volume and power. The combination of external targeting with thermosensitive liposomes and magnetic resonance-guided drug delivery will be the unique characteristic of this nanotechnology approach in medicine.