Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Inventory‐Pricing Optimization with General Demands: An Alternative Approach for Concavity Preservation

In this study, we provide an alternative approach for proving the preservation of concavity together with submodularity, and apply it to finite‐horizon non‐stationary joint inventory‐pricing models with general demands. The approach characterizes the optimal price as a function of the inventory level. Further, it employs the Cauchy–Schwarz and arithmetic‐geometric mean inequalities to establish a relation between the one‐period profit and the profit‐to‐go function in a dynamic programming setting. With this relation, we demonstrate that the one‐dimensional concavity of the price‐optimized profit function is preserved as a whole, instead of separately determining the (two‐dimensional) joint concavities in price (or mean demand/risk level) and inventory level for the one‐period profit and the profit‐to‐go function in conventional approaches. As a result, we derive the optimality condition for a base‐stock, list‐price (BSLP) policy for joint inventory‐pricing optimization models with general form demand and profit functions. With examples, we extend the optimality of a BSLP policy to cases with non‐concave revenue functions in mean demand. We also propose the notion of price elasticity of the slope (PES) and articulate the condition as that in response to a price change of the commodity, the percentage change in the slope of the expected sales is greater than the percentage change in the slope of the expected one‐period profit. The concavity preservation conditions for the additive, generalized additive, and location‐scale demand models in the literature are unified under this framework. We also obtain the conditions under which a BSLP policy is optimal for the logarithmic and exponential form demand models.
Housing price fluctuations and financial risk transmission: a spatial economic model

ABSTRACT Financial risk derived from housing price fluctuations in China garnered much public concern recently. Based on the theoretical analyses of the transmission of financial risk from housing price fluctuations, this paper establishes panel spatial Durbin models to empirically analyse housing price fluctuations and financial risks transmission from a spatial economic perspective. Employing the panel provincial data from 1999–2015, we conduct an analysis on the 30 provinces in China as well as a comparison among the Eastern, Middle and Western regions of China. The results indicate that: (1) The soaring housing prices driven by bank credit, real estate developers’ heavy investment, local governments’ land revenue and individuals and households demands leads to financial risk in various sectors; (2) due to the ‘substitution effect’, the capital agglomeration in metropolis from bank credits, real estate developers, and individuals and households furthers the amassment of financial risks; (3) housing prices have a significant spatial contagion effect throughout the country, and financial risk could directly transmit across provinces through housing price fluctuations; (4) financial risks could indirectly transmit across provinces via the ‘imitative behaviour’ or ‘driving effect’ of different sectors for different regions of China.
Learning-Based Remote Channel Inference: Feasibility Analysis and Case Study

Channel state information (CSI) plays a vital role in wireless communication systems. However, the CSI acquisition overhead is an enormous obstacle to realize the system performance improvements promised by massive connectivity and massive multiple-input-multiple-output (MIMO). To alleviate this overhead, this paper proposes a remote channel inference framework by probing the channels occupied by a source base station (BS) and inferring the channels of target BSs at geographically separated sites. The work generalizes existing literature which mainly focuses on utilizing the CSI linear correlations of adjacent antennas, by adopting a model-free deep learning framework to investigate non-linear dependence among remote CSI. The existence of such cross-BS CSI dependence is first shown by calculating the mutual information between remote channels, and the Cramér-Rao lower bound of remote CSI inference performance based on a one-ring channel model. Inspired by this finding, modern deep learning approaches are leveraged to perform remote channel inference in heterogeneous networks for both single user and multi-user scenarios. The simulation results based on ray tracing data show evident performance advantages over conventional methods, under both homogeneous and heterogeneous frequency coverage. The proposed framework achieves beamformer inference accuracy within 4.6% of the genie-aided optimum at the cost of sweeping only two beams.
Toxin profiles and antimicrobial resistance patterns among toxigenic clinical isolates of Clostridioides (Clostridium) difficile

Objective(s): Clostridioides (Clostridium) difficile infection as a healthcare-associated infection can cause life-threatening infectious diarrhea in hospitalized patients. The aim of this study was to investigate the toxin profiles and antimicrobial resistance patterns of C. difficile isolates obtained from hospitalized patients in Shiraz, Iran. Materials and Methods: This study was performed on 45 toxigenic C. difficile isolates. Determination of toxin profiles was done using polymerase chain reaction method. Antimicrobial susceptibility to vancomycin, metronidazole, clindamycin, tetracycline, moxifloxacin, and chloramphenicol was determined by the agar dilution method. The genes encoding antibiotic resistance were detected by the standard procedures. Results: The most frequent toxin profile was tcdA+, tcdB+, cdtAˉ, cdtBˉ (82.2%), and only one isolate harboured all toxin associated genes (tcdA+, tcdB+, cdtA+, cdtB+) (2.2%). The genes encoding CDT (binary toxin) were also found in six (13.3%) isolates. Resistance to tetracycline, clindamycin and moxifloxacin was observed in 66.7%, 60% and 42.2% of the isolates, respectively. None of the strains showed resistance to other antibiotics. The distribution of the ermB gene (the gene encoding resistance to clindamycin) was 57.8% and the tetM and tetW genes (the genes encoding resistance to tetracycline) were found in 62.2% and 13.3% of the isolates, respectively. The substitutions Thr82 to Ile in GyrA and Asp426 to Asn in GyrB were seen in moxifloxacin resistant isolates. Conclusion: Our data contributes to the present understanding of virulence and resistance traits amongst the isolates. Infection control strategies should be implemented carefully in order to curb the dissemination of C. difficile strains in hospital.
Relationship Between Suicidality and Disability When Accounting for Depressive Symptomology

We examined suicidality and disability status in 485 U.S. adults. Compared with participants without disabilities, participants with disabilities (n = 92) had significantly higher suicidality scores even when accounting for depressive symptoms. Participants with psychiatric disabilities had significantly higher suicidality scores than participants with other disabilities even when controlling for depressive symptoms.
Using First Principles Density Functional Theory Methods to Model the Seebeck Coefficient of Bulk Silicon

LIBRARIES The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereftcrprcatcd. Abstract: Thermoelectrics are gaining significant amounts of attention considering their relevance today in the areas of sustainable energy generation and energy efficiency. In this thesis, the thermoelectric properties of bulk Silicon were modeled using ab initio density functional theory methods to determine the Si band structure. Specifically, three different models for determining the Seebeck coefficient-Parabolic Bands, Boltzmann's theory, and the 'Pudding Mold' approximation to Boltzmann's theory-were studied in depth and compared with experimental values. Here we show first principles calculations to yield Seebeck coefficients for n-type Silicon to be on the order of 300 gtV/K at-300 K, and-500 gtV/K at 300 K for the Parabolic Bands and Boltzmann approach, respectively. While the 'Pudding Mold' Theory failed in its approximations of the Seebeck coefficients, the calculations using the other two theories were found to agree closely with experimentally determined Seebeck coefficients. Acknowledgements: I would like to thank MIT, the DMSE, Professor Ceder and his group for the direction provided and the access to the necessary resources to perform the work entailed in this project. Specifically, Maria Chan was of great help and I really would not have been able to do this thesis without her guidance and feedback. Without her, this thesis would not have been.
Ambient Systems , Networks and Technologies ( ANT ) April 29-May 2 , 2019 , Leuven , Belgium Path complexity for observed and predicted bicyclist routes

Everyday route choices made by bicyclists are known to be more difficult to explain than vehicle routes, yet prediction of these choices is essential for guiding infrastructural investment in safe cycling. In this paper we study how the concept of route complexity can help generate and analyze plausible choice sets in the demand modeling process. The complexity of a given path in a graph is the minimum number of shortest paths that is required to specify that path. Complexity is a path attribute which is considered to be important for route choice in a similar way as the number of left turns, the number of speed bumps, distance and other. The complexity was determined for a large set of observed routes and for routes in the generated choice sets for the corresponding origin-destination pairs. The respective distributions seem to significantly differ so that the choice sets do not reflect the traveler preferences. This paper looks at how the observed routes compare to routes generated by Breadth First Search Link Elimination and Double Stochastic Generation Function method. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.
Treatment response and outcome of children with T-cell acute lymphoblastic leukemia expressing the gamma-delta T-cell receptor

ABSTRACT T-cell malignancies expressing the γδ T-cell receptor (TCR) are often associated with poor prognosis. Here, we determined the clinical outcome of pediatric patients with T-cell acute lymphoblastic leukemia (T-ALL) expressing the γδ TCR. Of 100 newly diagnosed T-ALL patients, 93 had γδ TCR analysis performed at diagnosis. Repertoire was evaluated by paired sequencing of the rearranged TCR. All patients received intensified chemotherapy and those with minimal residual disease (MRD) ≥ 1% on day 42–46 became candidates for hematopoietic cell transplantation. Of the 93 T-ALL patients, 12 (13%) had γδ T-ALL and 11 (12%) had early T-cell precursor (ETP) ALL. Compared to the remaining 70 T-ALL patients, the γδ T-ALL patients were more likely to have MRD ≥ 1% on day 15–19 (67% vs. 33%, P = 0.03) and day 42–49 (33% vs. 7%; P = 0.007) of remission induction. The 10-year overall survival for γδ T-ALL patients (66.7% ± 22.2%) were lower than that of T-ALL patients (93.3% ± 7.3%, P = 0.001). TCR analysis demonstrated a conserved clonotype. In conclusion, the data suggest that children with γδ T-ALL may have a poor response to remission induction, based on MRD levels and decreased survival than the other T-ALL patients, despite receiving risk-directed therapy.
Cost overruns of public sector construction projects: a developing country perspective

Purpose Cost overrun of construction projects has been a key concern for all stakeholders of projects for many decades now. Many studies have been done in the past and continue to be done currently to understand the underlying causes of construction project cost overruns. However, the empirical evidence of the causes seem not be clear due to the silo approach in understanding the causes of construction project cost overruns. The purpose of this paper is to take the debate a step forward by providing an understanding of the causes of project cost overrun from a system’s perspective, especially from a less researched environment. Design/methodology/approach Data were collected and analysed from 131 respondents who were mainly involved in construction works in public procurement entities in Ghana. A two-staged approach was employed in collecting data from the respondents. The first stage involved an interview session with key informants in the construction industry in Ghana to ascertain the detailed causes of cost overrun of construction projects. The second stage focussed on the validation of these detailed factors by a wider stakeholder group through questionnaires. Factor analysis was employed to consolidate these detailed factors into major causes of construction project cost overruns. Findings The results show that there are primarily four major causes of most public sector construction projects cost overruns. These four major causes of cost overruns are poor contract planning and supervision; change orders; weak institutional and economic environment of projects and lack of effective coordination among the contracting parties. Originality/value The study provides more insights as to the critical and major factors that underpin public sector construction projects cost overruns and more importantly provides a basis for common treatment of the multiple risk factors engendering public sector construction projects cost overruns.
University of Birmingham Structural changes and the role of monetary aggregates in the UK

We investigate whether or not monetary aggregates are important in determining output. In addition to the official Simple Sum measure of money, we employ the sophisticated weighted Divisia aggregate. We also investigate whether or not the influence of money on output is time varying using data-driven procedures to identify breaks in the data and conduct estimations for the different segments defined by these breaks. We find that structural breaks do exist in some of the variables under investigation and these do influence the relationship between monetary aggregates and output. However, the official Simple Sum aggregate appears to be more affected by the breaks than the theoretically superior Divisia aggregate. In particular, our results show that in some segments of our data, the Simple Sum aggregate does not influence output significantly whereas the Divisia aggregate maintains a significant relationship with output in all segments. We conclude that Divisia money is still influencing output in spite of the diminished role played in monetary policy. Our investigation also suggests that the recovery from the financial crisis using quantitative easing would have been faster if money was not being hoarded.
“In-silico studies of facilitated VEGF(s) – VEGFR(s) bindings for assessment of Lysine as an indirect Low-Mol-Wt angiogen: Experimental validation of a potential synthetic Low-Mol-Wt angiogen”

In-vivo angiogenesis process is highly conserved and is mediated through a family of peptides having VEGF-A as the lead member. A respective receptor family comprising of members VEGFR-1, 2, 3 gets expressed on the endothelial cell membrane of the vascular bed in ischemic zone along with parallel expressions of VEGF-A, B, C, D and PlGF. Degree of ischaemia is the main regulator of these coupled expressions of angiogenic peptides/factors (AFs) and respective receptor(s) for a paracrine angiogenic process to take place. Physiological angiogenesis in intrauterine growth phase is the lead process in foetal growth, organogenesis and cellular specialization. Post birth and with aging, this process gets gradually inefficient and slow. In the present in-silico study, all angiogenic factors and receptor species are examined as for their binding stability in basal unaided condition and in presence of a possible Low-Mol-Wt linkage molecule–Lysine. Also a Lysine analogue 1,6-diaminohexanoic acid has been examined for its angiogenic potential both in dry docking experiment and in cell culture assay.
Advances in systematics of bats: towards a consensus on species delimitation and classifications through integrative taxonomy

The order Chiroptera comprises all bat species and is the second-most diverse order of mammals. Organization of this diversity into well-delimited taxa embodying their evolutionary affinities remains challenging, particularly because the relationships among various groups at most taxonomic levels are not yet fully resolved. Historically, systematic questions were approached independently using tools and methodologies within researchers' individual expertise. With technological advances, this trend has shifted to more comprehensive analyses, merging independent datasets from various lines of research. Nevertheless, disagreements among phylogenetic relationships inferred from independent datasets, and derived from the complexity of modes of diversification and speciation, still preclude the achievement of a single, comprehensive, and stable classification framework for bats. In this context, integrative taxonomy uses complementarity among various disciplines to improve the outcome of hypothesis testing, which can be achieved by congruence or cumulation of datasets. Here, we provide a review of bat systematics, with emphasis on the instances where integrative taxonomy has proven to be crucial in delimitation of taxa. We focus on examples of true integration or integration using discrete data to support delimitation at different taxonomic levels. In addition, we discuss the difficulties associated with the integration of incongruent phylogenies, as well as how to incorporate information crucial to phylogenetic decisions from data not easily comparable to most common datasets. Our goal is to provide exemplar guidelines that can be useful to taxonomists for the creation of stable classifications without subjectivity and limitations of single datasets. Accordingly, we present a review of recent issues in chiropteran systematics, including comments on how the use of integrative taxonomy helped to solve them.
INFLUENCE OF TRICEPS SURAE ELECTROMECHANICAL DELAY ON MOVEMENT RESPONSES IN THE SPRINT START EVENT

This study examined the delay in sprint start performance related to electromechanical delay (EMD) in the triceps surae muscle and aimed to determine whether sprinters may gain an advantage in sprint start response time (SSRT). SSRT’s of nineteen sprinters were measured using an International Association of Athletics Federations approved SSRT detection system. EMD times were also obtained from the triceps surae muscle during a simple heel-lift experiment. Using Brosnan et al. (2016) response time limits, the results demonstrated that EMD produced a significant moderate correlation with SSRT (r = 0.572, P = 0.01). Initial results suggest EMD influences SSRT. However greater specificity in the EMD measurement to the sprint start action is required to determine the true effects of EMD on SSRT.
Treat-to-target concept implementation for evaluating rheumatoid arthritis patients in daily practice.

OBJECTIVE We aimed to assess the implementation of the treat-to-target (T2T) concept in rheumatoid arthritis (RA) patients in daily practice. METHODS All RA patients visiting one of the 7 academic medical centers in Israel in June 2015 with at least 3 previous clinic visits were included in this study. A common questionnaire was used to collect data from patients' medical records, and two independent rheumatologists evaluated the collected data for the implementation of the T2T concept. The associations between T2T implementation and the categorical and continuous variables were assessed. RESULTS The study included 724 patients with a mean (standard deviation) age of 62.6 (13.97) years and 575 (80.4%) of them were women. Four centers used more than one scoring method, with Disease Activity Score-28 and Clinical Disease Activity Index) being most commonly used. Only 276 (38.1%) patients had disease score results in ≥3 visits, and the T2T recommendations were implemented for 245 (33.8%) of the 724 patients. The rate of implementation was higher in younger (p=0.028) rheumatoid factor-positive patients (p=0.011) and varied between centers (11.1%-87% p<0.0001). T2T implementation did not correlate to gender, place of residence, education, tobacco use, treatment regimens, and presence of erosions or comorbidities. CONCLUSION The T2T concept was implemented on only 33.8% of patients and was not affected by RA disease severity. Further studies are needed to determine the reasons for this deviation from the T2T standard of care for RA as well as its consequences.
Research on Risk of Stock Index Futures Market Based on EGARCH Model

Volatility is an important indicator to measure the risk of financial market. The EGARCH model is established by using the logarithmic yield of the China Securities Index (CSI) 300 stock index futures from January 2014 to August 2017, then the volatility and yield of the data in the last three months are back tested to confirm the reliability of the model. Furthermore, based on the established EGARCH model, the future volatility and yield are forecasted, and the value at risk of stock index futures contract is calculated. At last, the error of our EGARCH model is determined by compare the predicted yield with actual value.
Convolutional neural network based heterogeneous transfer learning for remote-sensing scene classification

ABSTRACT Deep convolutional neural network (CNN) transfer has recently shown strong performance in scene classification of high-resolution remote-sensing images. However, the majority of transfer learning solutions are categorized as homogeneous transfer learning, which ignores differences between target and source domains. In this paper, we propose a heterogeneous model to transfer CNNs to remote-sensing scene classification to correct input feature differences between target and source datasets. First, we extract filters from source images using the principal component analysis (PCA) method. Next, we convolute the target images with the extracted PCA filters to obtain an adopted target dataset. Then, a pretrained CNN is transferred to the adopted target dataset as a feature extractor. Finally, a classifier is used to accomplish remote-sensing scene classification. We conducted extensive experiments on the UC Merced dataset, the Brazilian coffee scene dataset and the Aerial Images Dataset to verify the effectiveness of the proposed heterogeneous model. The experimental results show that the proposed heterogeneous model outperforms the homogeneous model that uses pretrained CNNs as feature extractors by a wide margin and gains similar accuracies by fine-tuning a homogeneous transfer learning model with few training iterations.
Flow-Centric Painterly Rendering

We present a painterly rendering technique that emphasizes smooth line flows to describe important shapes in the image, as well as contributing in an essential part to the final stylistic artwork. Our method uses the smoothed edge tangent flow (ETF) [20] for stroke pathing, and provides an artist-friendly way to control the general look and feel of the image using familiar brush stroke properties and colour theme palettes. We achieve a highly stylized expressionistic look that can be applied to still photographs and portraits.