Miracle-dz/UniFAR-FTUs huggingface dataset:
AlphaPortfolio: Direct Construction Through Deep Reinforcement Learning and Interpretable AI

We propose direct optimization of investors' objectives via reinforcement learning, an alternative to the widely adopted two-step portfolio-management paradigm entailing estimating distributions of asset returns. Building upon recent breakthroughs in AI, we develop neural-network-based multi-sequence models tailored to distinguishing features of economic and financial data. The resulting AlphaPortfolio yields stellar out-of-sample performances (e.g., Sharpe ratio above two) that are robust under various economic and trading restrictions. Moreover, we use polynomial-feature-sensitivity and textual-factor analyses to project the model onto linear regression and natural language spaces uncover key market signals, firms' financials, etc., including their rotation and non-linearity, that drive investment performance. Overall, we highlight the utility of reinforcement deep learning in social sciences, especially finance, and provide novel ``economic distillation'' procedures for interpreting AI and big data models.
Copula Gaussian Graphical Models for Functional Data

Abstract We introduce a statistical graphical model for multivariate functional data, which are common in medical applications such as EEG and fMRI. Recently published functional graphical models rely on the multivariate Gaussian process assumption, but we relax it by introducing the functional copula Gaussian graphical model (FCGGM). This model removes the marginal Gaussian assumption but retains the simplicity of the Gaussian dependence structure, which is particularly attractive for large data. We develop four estimators for the FCGGM and establish the consistency and the convergence rates of one of them. We compare our FCGGM with the existing functional Gaussian graphical model by simulations, and apply our method to an EEG dataset to construct brain networks. Supplementary materials for this article are available online.
Combining Emergent Constraints for Climate Sensitivity

A method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The ±2σ posterior range of ECS for Method C with no overconfidence adjustment is 4.3 ± 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 ± 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean.
Mast cells activation and high blood tryptase levels due to paclitaxel administration. Is Cremophor EL the culprit?

Abstract Rationale: Although the cancer incidence continues to rise, cancer mortality has declined over the past decade, in large part due to more efficacious chemotherapeutic regimens thus, the ability to use first-line chemotherapeutic agents in the treatment of patients with cancer is crucial. Antineoplastic agents can potentially cause toxic and/or hypersensitivity reactions, that can have serious consequences. Anaphylaxis is a big pitfall in oncological patients; the most important aspect in diagnosing anaphylaxis is to precisely identify the offending agent to prevent future events. Paclitaxel (Taxol) is widely used as antitumor medication in the ovarian, breast, non-small-cell lung, and other cancers. Paclitaxel hypersensitivity reactions are frequently described in the literature, but fatalities are rarely reported. Due to the low solubility of paclitaxel, the compound requires dissolution in Cremophor EL, a derivative of castor oil. Patient concerns: A 79-year-old man was affected by high-grade non-papillary urothelial carcinoma and underwent a radical cystectomy and prostatectomy with locoregional lymphadenectomy. Diagnosis: Eight months later, relapse was detected, and penis amputation and left nephrostomy were performed. Multiple metastases to lymph nodes were detected. Interventions: Palliative chemotherapy was started with Paclitaxel (110 mg) infused at a rate of 50 mL/h. Despite premedication with cetirizine dihydrochloride, dexamethasone, ondansetron, ranitidine, 20 min after Paclitaxel infusion starts, the patient developed general distress, followed by cardiac arrest. Outcomes: The mechanism of fatal paclitaxel-associated hypersensitivity reaction is uncertain and its solvent vehicle Cremophor EL may be involved. Several mechanisms have been postulated: an IgE-mediated mast cell degranulation induced by paclitaxel or Cremophor EL, a non-IgE-mediated idiosyncratic mast cell degranulation by paclitaxel or by Cremophor EL, and complement activation. Severe hypersensitivity reactions with fatal outcome are considered rare. Lessons: The unpredictability and often dramatic reactions of Taxol cause substantial anxiety for doctors and caretakers. They also represent a significant logistic and financial burden on hospitals. Despite premedication, skin testing, and desensitization protocols administration of taxane-based, chemotherapy cannot be considered safe and severe to fatal hypersensitivity reactions cannot be prevented.
Measuring the Impact of a New Snow Model Using Surface Energy Budget Process Relationships

Energy exchange at the snow‐atmosphere interface in winter is important for the evolution of temperature at the surface and within the snow, preconditioning the snowpack for melt during spring. This study illustrates a set of diagnostic tools that are useful for evaluating the energy exchange at the Earth's surface in an Earth System Model, from a process‐based perspective, using in situ observations. In particular, a new way to measure model improvement using the response of the surface temperature and other surface energy budget (SEB) terms to radiative forcing is presented. These process‐oriented diagnostics also provide a measure of the coupling strength between the incoming radiation and the various terms in the SEB, which can be used to ensure that improvements in predictions of user‐relevant properties, such as 2 m temperature, are happening for the right reasons. Correctly capturing such process relationships is a necessary step toward achieving more skilful weather forecasts and climate projections. These diagnostic techniques are applied to assess the impact of a new multi‐layer snow scheme in the European Centre for Medium‐Range Weather Forecasts'‐Integrated Forecast System at two high‐Arctic sites (Summit, Greenland and Sodankylä, Finland). A previous study showed that it will enhance 2 m temperature forecast skill across the Northern Hemisphere in boreal winter compared to forecasts with the single layer model, reducing a warm bias. In this study we use the diagnostics to show that the bias is improved for the right reasons.
Assessing contributions of natural surface and anthropogenic emissions to atmospheric mercury in a fast-developing region of eastern China from 2015 to 2018

Abstract. Mercury (Hg) is a global toxic pollutant that can be released into the atmosphere through anthropogenic and natural sources. The uncertainties in the estimated emission amounts are much larger from natural than anthropogenic sources. A method was developed in the present study to quantify the contributions of natural surface mercury emissions to ambient gaseous elemental mercury (GEM) concentrations through application of positive matrix factorization (PMF) analysis with temperature, O3, and NH3 as indicators of GEM emissions from natural surfaces. GEM concentrations were continuously monitored at a 2-hourly resolution at a regional background site in the Yangtze River Delta in Eastern China during 2015–2018. Annual average GEM concentrations were in the range of 2.03–3.01 ng/m3, with a strong decreasing trend at a rate of −0.32 ± 0.07 ng m−3 yr−1 from 2015 to 2018, which was mostly caused by reduced anthropogenic emissions since 2013. The estimated contributions from natural surface emissions of mercury to the ambient GEM concentrations were in the range of 0.90–1.01 ng/m3 on annual average with insignificant interannual changes, but the relative contribution increased significantly from 36 % in 2015 to 53 % in 2018, gradually surpassing those from anthropogenic sources.
Drainage Area, Bedrock Fracture Spacing, and Weathering Controls on Landscape‐Scale Patterns in Surface Sediment Grain Size

Sediment grain size links sediment production, weathering, and fining from fractured bedrock on hillslopes to river incision and landscape relief. Yet models of sediment grain size delivery to rivers remain unconstrained due to a scarcity of field data. We analyzed how bedrock fracture spacing and hillslope weathering influence landscape‐scale patterns in surface sediment grain size across gradients of erosion rate and hillslope bedrock exposure in the San Gabriel Mountains (SGM) and northern San Jacinto Mountains (NSJM) of California, USA. Using ground‐based structure‐from‐motion photogrammetry models of 50 bedrock cliffs, we showed that fracture density is ~5 times higher in the SGM than the NSJM. 274 point‐count‐surveys of surface sediment grain size measured in the field and from imagery show a drainage area control on sediment grain size, with systematic downslope coarsening on hillslopes and in headwater‐colluvial channels transitioning to downstream fining in fluvial channels. In contrast to prior work and predictions from a hillslope weathering model, grain size does not increase smoothly with increasing erosion rate. For soil‐mantled landscapes, sediment grain size increases with increasing erosion rates; however, once bare bedrock emerges on hillslopes, sediment grain size in both the NSJM and SGM becomes insensitive to further increases in erosion rate and hillslope bedrock exposure, and instead reflects fracture spacing contrasts between the NSJM and SGM. We interpret this threshold behavior to emerge in steep landscapes due to efficient delivery of coarse sediment from bedrock hillslopes to channels and the relative immobility of coarse sediment in fluvial channels.
Fuzzy time series forecasting in determining inventory policy for a small medium enterprise (SME) company

Fuzzy time series models have been widely used to handle forecasting problems, such as forecasting consumer demand and production volume. It is of greater benefits if we have good forecasting accuracy rates especially in managing inventory in a Small and Medium Enterprise (SME) company. This study focuses on multiple products with single production line. The aims of this study are to propose the appropriate the forecasting method for the products, to develop new inventory policy that minimizes the total inventory cost for the company. Simple forecasting methods like trend line, three month moving average (MA (3) and fuzzy time series forecasting are used in this study. The result shows that fuzzy time series forecasting model is suitable to be used in forecasting future demand for all products. The proposed inventory policy is based on the number of cycle per year and the number of production for each product has helped the company to minimize total inventory cost and schedule the production process accordingly. The proposed inventory policy resulted in lower total inventory cost when compared to current practice.
The health and economic burden of antimicrobial resistance

Antimicrobial resistance (AMR) has profound consequences for the treatment of infections. By limiting treatment options, it often makes it necessary to resort to antibiotics with a broader spectrum of action some of which are potentially less effective or safe than narrow-spectrum antibiotics. This limitation in our ability to treat infections effectively has an impact on health care budgets but also broader and potentially disastrous consequences on a variety of economic sectors. This presentation provides an overview of the health and economic burden of AMR. It first presents the current state of knowledge on the epidemiology of AMR and discusses the main analytical challenges in determining the current and long-term effects of resistance on populations in terms of morbidity, mortality, and length of hospital stay. In addition, a summary of the current literature on the economic impact of AMR is provided along with a detailed discussion of the characteristics and limitations of existing economic models. Finally, it identifies the main knowledge gaps and suggests avenues for future research and approaches to address them.
A New Approach for the AFM-Based Mechanical Characterization of Biological Samples

The AFM nanoindentation technique is a powerful tool for the mechanical characterization of biological samples at the nanoscale. The data analysis of the experimentally obtained results is usually performed using the Hertzian contact mechanics. However, the aforementioned theory can be applied only in cases that the sample is homogeneous and isotropic and presents a linear elastic response. However, biological samples often present depth-dependent mechanical properties, and the Hertzian analysis cannot be used. Thus, in this paper, a different approach is presented, based on a new physical quantity used for the determination of the mechanical properties at the nanoscale. The aforementioned physical quantity is the work done by the indenter per unit volume. The advantages of the presented analysis are significant since the abovementioned magnitude can be used to examine if a sample can be approximated to an elastic half-space. If this approximation is valid, then the new proposed method enables the accurate calculation of Young's modulus. Additionally, it can be used to explore the mechanical properties of samples that are characterized by a depth-dependent mechanical behavior. In conclusion, the proposed analysis presents an accurate yet simple technique for the determination of the mechanical properties of biological samples at the nanoscale that can be also used beyond the Hertzian limit.
Spatial Covariance Modeling for Stochastic Subgrid‐Scale Parameterizations Using Dynamic Mode Decomposition

Stochastic parameterizations are increasingly being used in climate modeling to represent subgrid‐scale processes. While different parameterizations are being developed considering different aspects of the physical phenomena, less attention is given to technical and numerical aspects. In particular, empirical orthogonal functions (EOFs) are employed when a spatial structure is required. Here, we provide evidence they might not be the most suitable choice. By applying an energy‐consistent parameterization to the two‐layer quasi‐geostrophic (QG) model, we investigate the model sensitivity to a priori assumptions made on the parameterization. In particular, we consider here two methods to prescribe the spatial covariance of the noise: first, by using climatological variability patterns provided by EOFs, and second, by using time‐varying dynamics‐adapted Koopman modes, approximated by dynamic mode decomposition (DMD). The performance of the two methods are analyzed through numerical simulations of the stochastic system on a coarse spatial resolution and the outcomes compared to a high‐resolution simulation of the original deterministic system. The comparison reveals that the DMD‐based noise covariance scheme outperforms the EOF‐based one. The use of EOFs leads to a significant increase of the ensemble spread and to a meridional misplacement of the bimodal eddy kinetic energy (EKE) distribution. Conversely, using DMDs, the ensemble spread is confined, the meridional propagation of the zonal jet stream is accurately captured, and the total variance of the system is improved. Our results highlight the importance of the systematic design of stochastic parameterizations with dynamically adapted spatial correlations, rather than relying on statistical spatial patterns.
Fiscal Policy in a Networked Economy

Policymakers can choose from a variety of fiscal stimulus levers: conditional transfers, unconditional transfers, or direct purchases from certain industries. This choice is complicated by the rich network structures that connect households and industries via directed consumption and input-output linkages. We study this problem in a model with household heterogeneity in MPCs, directed consumption patterns, and exposure to industry and regional shocks. Theoretically, we express fiscal multipliers in terms of estimable sufficient statistics, and we decompose them in terms of three network effects on top of a standard Keynesian multiplier. Empirically, we find that targeting fiscal policy is important, but simple. First, optimally targeting fiscal stimulus generates twice as much amplification in GDP as untargeted policy. Second, owing to the empirical absence of two of the three network effects, a simple fiscal policy that targets households based on their MPCs is close to maximally expansionary and optimal.
ICO Success and Post-ICO Performance

We compile a comprehensive dataset of initial coin offerings (ICOs) from 19 data sources including 11 ICO aggregators. We alleviate severe limitations of available ICO data by performing the first systematic analysis of ICO data quality and use our dataset to study determinants of ICO funding success as well as post-ICO operating and financial performance. We highlight determinants of ICO success that are new to the literature and overturn some findings in existing studies. In addition, we provide evidence on some determinants of initial and longer-term ICO success on which existing literature has not reached a consensus. We also show that entrepreneurs' skin in the game is an important determinant of venture's post-ICO operating performance. Finally, we demonstrate that post-ICO operating success translates into financial one.
“God Spoke to Me”: Subjective Paranormal Experience and the Homeostatic Response to Early Trauma

The prevalence of reported subjective paranormal experience (SPE) is at high levels in all populations investigated to date. This article presents a new integrative theory of SPE in light of the brain’s homeostatic response to early trauma. I hold that developmental factors in the brain’s responses to trauma predispose victimized individuals towards SPE and paranormal beliefs. I examine the reported associations between childhood abuse, dissociation, depersonalization, compartmentalization, fantasy generation, homeostasis and SPE. A new integrative theory of psychological homeostasis draws upon the mechanisms of dissociation and fantasy generation to explain the origins of SPE. Twelve hypotheses from the Homeostasis Theory are found to be consistent with the findings of multiple studies and falsifying evidence has yet to be identified. Freezing and associated releases of fantasy, which may take the form of SPE, serve as a survival strategy in the homeostatic regaining of safety and control following childhood abuse. Prospective research is necessary to deepen our understanding of the brain mechanisms required by the system described here.
Credit Ratings and the Hold-Up Problem in the Loan Market

This paper studies whether credit ratings can alleviate the hold-up problem in the loan market. We exploit a refinement of the rating information produced by a certifier that rates private bank-dependent firms in France on a vast scale. The refinement causes some firms to receive a positive rating surprise that is not due to an improvement in firm fundamentals. We show that affected firms become less reliant on lenders that have greater ability to extract informational rents. These firms receive greater and less expensive bank credit from new and less informed lenders and invest more. We deduce that credit ratings reduce the monopoly power of informed banks, helping firms to expand their access to bank credit. This paper was accepted by David Sraer, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023. 4779 .
Characterization of Pseudomonas fluorescens polyhydroxyalkanoate produced from molasses as a carbon source

Polyhydroxyalkanoate (PHA) is a biopolymer produced by various types of bacteria under conditions of unbalanced growth. One of the bacteria generating PHA is Pseudomonas fluorescens with a carbon source in the form of molasses. This study aim ed to determine the effect of molasses concentration and incubation period on biomass and PHA characteristics. This study utili s ed two factor s , such as molasses concentration of 40, 60, 80 g/L, and incubation period for 3, 5, 7 days. The research result indicated the highest biomass yield was obtained from the treatment of 40 g/L molasses concentration and in 7-day incubation period , with dry cell weight values of 1 , 955 g/L, PHA weight of 0.756 g/L, dry cell yield of 2.036%, and PHA yield of 39.06% , respectively . The visual characteristics of PHA samples were indicated with brownish and granular state , while the melting point and functional group s were identical to pure polyhydroxybutyrate (PHB). The characteristics of the PHB structure obtained from Fourier Transform Infrared Spectroscopy (FTIR) analysis, depicting: C = O esters, CH 3 , CH 2 , -C-O-, -C-O-C Polymers, and C-C. The melting point of PHA samples , using Differential Scanning Calorimetry (DSC) analysis, was 166.4 o C and an enthalpy of -13.885 J/g with 9.5% degree of crystalli s ation.
Exploring Food Waste at a Residential Youth Summer Camp: A Mixed-Methods Study

ABSTRACT This study explored the feasibility of a food waste management and reduction program (FWMRP) at a youth summer camp. An experiential FWMRP educational curriculum was developed and delivered to 864 campers over four weeks. Food waste was collected for three meals before and after the program each week. Stakeholder interviews (n = 6) explored staff views about food waste and their acceptability of the FWMRP curriculum. While absolute quantity of food waste decreased, the reduction was not statistically significant (p > .05). Stakeholders identified the need for adequate resources to ensure feasibility to implement and sustain the FWMRP curriculum at this youth summer camp.