Miracle-dz/UniFAR-FTUs huggingface dataset:
Crystal structure of diaqua(bis(3,5-bis(isonicotinamido))- benzoato)nickle(II) tetrahydrate, Ni(H2O)2(C19H13N4O4)2 · 4H2O

C38H38N8NiO14, monoclinic, C12/c1 (no. 15), a = 17.466(2) Å, b = 10.706(1) Å, c = 21.681(2) Å, * = 104.837(1)°, V = 3919.2 Å, Z = 4, Rgt(F) = 0.039, wRref(F ) = 0.063, T = 293 K. Source of material 0.1 mmol 3,5-bis(isonicotinamido)benzoic acid (HL) and 0.05 mmol Ni(NO3)2 were dissolved in 10 ml distilled water. The pH value was adjusted to about 6 with 1 M NaOH solution. Then the mixture was placed in a Teflon-lined stainless vessel (25 ml). The vessel was sealed and heated at 413 K for 72 h under autogenous pressure and then cooled down to room temperature. The resulting solution was filtered and green block-shaped single crystals suitable for analysis were obtained. Experimental details Hydrogen atoms bound to carbon atoms were placed in calculated positions and treated as riding on their parent atoms, with d(C—H) = 0. 93Å with Uiso(H) = 1.2 Ueq(C). Hydrogen atoms attached to oxygen of water molecule were initially located in a difference Fourier map and refined. Discussion In recent years, the design and synthesis of functional coordination polymers are of great interest due to their intriguing structural topologies and versatile chemical properties [1-4]. The Ni1 atom in the title complex sits on the inversion center and is coordinated by two N atoms from two different bis(3,5-bis(isonicotinamido))benzoate ligands (L) and two O atoms from two water molecules, forming a distorted octahedron (figure, top). The coordination bond lengths and angles around the Ni are in the range of 2.057(1) 2.105(2) Å and 86.62(6)° 175.50(9)°, respectively. More interestingly, two adjacent Ni ions linked by the L ligand, which the L ligand in turn uses its carboxylate group and one of the two pyridinyl groups to connect two metal centers, and another pyridinyl group is free of coordination (figure, bottom). There are macrocyclic rings consisted of 24-membered ring in the infinite chain, which similar to the complex [Mn(BBA)2(H2O)2] · 4H2O [5]. It is worthwhile to note that hydrogen bonding interactions are usually important in the synthesis of supramolecular architecture. There exist N–H···O and O–H···O hydrogen bonding interactions between crystal molecules and carboxylate oxygen atoms. These hydrogen bonds in the crystal structure, which further link the 1D complex into a three-dimensional framework structure. Z. Kristallogr. NCS 225 (2010) 249-250 / DOI 10.1524/ncrs.2010. 0108 249 © by Oldenbourg Wissenschaftsverlag, München Crystal: green block, size 0.10 × 0.14 × 0.18 mm Wavelength: Mo K+ radiation (0.71073 Å) . : 5.76 cm−1 Diffractometer, scan mode: Bruker SMART CCD, #/' 2,max: 52° N(hkl)measured, N(hkl)unique: 10559, 3850 Criterion for Iobs, N(hkl)gt: Iobs > 2 )(Iobs), 2359 N(param)refined: 296 Programs: SHELXS-97 [6], SHELXL-97 [7] Table 1. Data collection and handling.
Uncertainty, the Demand for Health Care, and Precautionary Saving

Abstract We analyze the demand for medical care and precautionary saving in a framework with uncertainty surrounding the incidence of illness and the effectiveness of medical treatments and a representation of preferences that disentangles ordinal preferences, risk preferences, and intertemporal smoothing preferences. We consider a pure consumption model with exogenous health capital and a model where young consumers invest in preventive care to increase their future health stock. In both cases, we establish conditions for the different sources for uncertainty to induce precautionary saving and we evaluate how uncertainty affects the demand of curative and preventive care. We also show that given the self-insurance function of preventive care, consumers' welfare may increase with the degree of uncertainty surrounding health care effectiveness.
Analyzing Causal Effects with Observational Studies for Evidence-based Marketing at IBM

Sound marketing decisions often require understanding the cause-and-effect relationships between treatment and outcomes. Market research traditionally approaches such questions by designing randomized experiments that aim to isolate the effects of the specific treatment from other effects. We review an alternate methodology that is well suited to observational studies, where the analyst cannot control how treatment is applied. The methodology uses propensity scoring and matching to emulate the randomization of treatment. It is well established in other fields, but not widely known among marketers in spite of the fact that non-experimental data is common in marketing studies. We present two applications as case studies to illustrate the value of the methodology and to describe how we addressed some of the practical issues, in sufficient detail for readers to be able to use the methodology in similar studies.
A novel NCSTN missense mutation in the signal peptide domain causes hidradenitis suppurativa, which has features characteristic of an autoinflammatory keratinization disease

NCSTN encodes nicastrin, one of the three subunits of γ-secretase, a transmembrane endoprotease complex that catalyzes the cleavage of many transmembrane proteins including Notch receptors.1 In 2010, mutations in genes encoding γ-secretase subunits (NCSTN, PSENEN and PSEN1) were reported in cases of hidradenitis suppurativa (HS). 2 It has been demonstrated that tumor necrosis factor-α and interleukin (IL) -10 levels are elevated in patients with HS with NCSTN mutations,1 although other reports found no elevation3 or found decreases.
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy

PURPOSE We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of deep neural networks. In this study, we trained a neural network for generating Pareto optimal dose distributions, and evaluate the effects of the domain specific loss on the model performance. METHODS In this study, 3 loss functions-mean squared error (MSE) loss, dose volume histogram (DVH) loss, and adversarial (ADV) loss-were used to train and compare 4 instances of the neural network model: 1) MSE, 2) MSE+ADV, 3) MSE+DVH, and 4) MSE+DVH+ADV. The data for 70 prostate patients, including the planning target volume (PTV), and the organs-at-risk (OAR) were acquired as 96 x 96 x 24 dimension arrays at 5 mm3 voxel size. The dose influence arrays were calculated for 70 prostate patients, using a 7 equidistant coplanar beam setup. Using a scalarized multicriteria optimization for intensity modulated radiation therapy, 1200 Pareto surface plans per patient were generated by pseudo-randomizing the PTV and OAR tradeoff weights. With 70 patients, the total number of plans generated was 84,000 plans. We divided the data into 54 training, 6 validation, and 10 testing patients. Each model was trained for a total of 100,000 iterations, with a batch size of 2. All models used the Adam optimizer, with a learning rate of 1×10-3 . RESULTS Training for 100,000 iterations took 1.5 days (MSE), 3.5 days (MSE+ADV), 2.3 days (MSE+DVH), 3.8 days (MSE+DVH+ADV). After training, the prediction time of each model is 0.052 seconds. Quantitatively, the MSE+DVH+ADV model had the lowest prediction error of 0.038 (conformation), 0.026 (homogeneity), 0.298 (R50), 1.65% (D95), 2.14% (D98), 2.43% (D99). The MSE model had the worst prediction error of 0.134 (conformation), 0.041 (homogeneity), 0.520 (R50), 3.91% (D95), 4.33% (D98), 4.60% (D99). For both the mean dose PTV error and the max dose PTV, Body, Bladder and rectum error, the MSE+DVH+ADV outperformed all other models. Regardless of model, all predictions have an average mean and max dose error less than 2.8% and 4.2%, respectively. CONCLUSION The MSE+DVH+ADV model performed the best in these categories, illustrating the importance of both human and learned domain knowledge. Expert human domain specific knowledge can be the largest driver in the performance improvement, and adversarial learning can be used to further capture nuanced attributes in the data. The real-time prediction capabilities allow for a physician to quickly navigate the tradeoff space for a patient, and produce a dose distribution as a tangible endpoint for the dosimetrist to use for planning. This is expected to considerably reduce the treatment planning time, allowing for clinicians to focus their efforts on the difficult and demanding cases.
Projective toric codes over hypersimplices

Let $d\geq 1$ be an integer, and let $\mathcal{P}$ be the convex hull in $\mathbb{R}^s$ of all integral points $\mathbf{e}_{i_1}+\cdots+\mathbf{e}_{i_d}$ such that $1\leq i_1<\cdots< i_d\leq s$, where $\mathbf{e}_i$ is the $i$-th unit vector in $\mathbb{R}^s$. Given a finite field $\mathbb{F}_q$, we determine the minimum distance of the projective toric code $\mathcal{C}_\mathcal{P}(d)$ associated to the hypersimplex $\mathcal{P}$ using the projective footprint function of a graded ideal.
Analysis of the spectral symbol function for spectral approximation of a differential operator

Given a differential operator $\mathcal{L}$ along with its own eigenvalue problem $\mathcal{L}u = \lambda u$ and an associated algebraic equation $\mathcal{L}^{(n)} \mathbf{u}_n = \lambda\mathbf{u}_n$ obtained by means of a discretization scheme (like Finite Differences, Finite Elements, Galerkin Isogeometric Analysis, etc.), the theory of Generalized Locally Toeplitz (GLT) sequences serves the purpose to compute the spectral symbol function $\omega$ associated to the discrete operator $\mathcal{L}^{(n)}$ We prove that the spectral symbol $\omega$ provides a necessary condition for a discretization scheme in order to uniformly approximate the spectrum of the original differential operator $\mathcal{L}$. The condition measures how far the method is from a uniform relative approximation of the spectrum of $\mathcal{L}$. Moreover, the condition seems to become sufficient if the discretization method is paired with a suitable (non-uniform) grid and an increasing refinement of the order of approximation of the method. On the other hand, despite the numerical experiments in many recent literature, we disprove that in general a uniform sampling of the spectral symbol $\omega$ can provide an accurate relative approximation of the spectrum, neither of $\mathcal{L}$ nor of the discrete operator $\mathcal{L}^{(n)}$.
Pseudo-task Regularization for ConvNet Transfer Learning

This paper is about regularizing deep convolutional networks (ConvNets) based on an adaptive multi-objective framework for transfer learning with limited training data in the target domain. Recent advances of ConvNets regularization in this context are commonly due to the use of additional regularization objectives. They guide the training away from the target task using some concrete tasks. Unlike those related approaches, we report that an objective without a concrete goal can serve surprisingly well as a regularizer. In particular, we demonstrate Pseudo-task Regularization (PtR) which dynamically regularizes a network by simply attempting to regress image representations to a pseudo-target during fine-tuning. Through numerous experiments, the improvements on classification accuracy by PtR are shown greater or on a par to the recent state-of-the-art methods. These results also indicate a room for rethinking on the requirements for a regularizer, i.e., if specifically designed task for regularization is indeed a key ingredient. The contributions of this paper are: a) PtR provides an effective and efficient alternative for regularization without dependence on concrete tasks or extra data; b) desired strength of regularization effect in PtR is dynamically adjusted and maintained based on the gradient norms of the target objective and the pseudo-task.
Efficient Flicker-Free FEC Codes Using Knuth's Balancing Algorithm for VLC

Visible light communication (VLC) provides a short- range optical wireless communication through light- emitting diode (LED) lighting. Light beam flickering and dimming are among the challenges to be addressed in VLC. Conventional methods for generating flicker-free codes in VLC are based on run-length limited codes that have poor error correction performance, use lookup tables which are memory consuming, and have low transmission rates. In this paper, we propose an efficient construction of flicker-free forward error correction codes to tackle the issue of flickering in VLC. Our simulation results show that by using polar codes and at a dimming ratio of 50%, the proposed system generates flicker-free codes without using lookup tables, while having lower complexity and higher transmission rates than the standard VLC methods. For an information block length of 256, the error correction performance of the proposed scheme is 1.8 dB and 0.9 dB better than that of the regular schemes at the bit error rate of 10^{-6} for a rate of 0.44 and 0.23, respectively.
Kernel Sketching yields Kernel JL

The main contribution of the paper is to show that Gaussian sketching of a kernel-Gram matrix $\mathbf{K}$ yields an operator whose counterpart in an RKHS $\mathcal{H}$, is a random projection operator---in the spirit of Johnson-Lindenstrauss (JL) lemma. To be precise, given a random matrix $Z$ with i.i. d. Gaussian entries, we show that a sketch $Z\mathbf{K}$ corresponds to a particular random operator in (infinite-dimensional) Hilbert space $\mathcal{H}$ that maps functions $f \in \mathcal{H}$ to a low-dimensional space $\mathbb{R}^d$, while preserving a weighted RKHS inner-product of the form $\langle f, g \rangle_{\Sigma} \doteq \langle f, \Sigma^3 g \rangle_{\mathcal{H}}$, where $\Sigma$ is the \emph{covariance} operator induced by the data distribution. In particular, under similar assumptions as in kernel PCA (KPCA), or kernel $k$-means (K-$k$-means), well-separated subsets of feature-space $\{K(\cdot, x): x \in \cal X\}$ remain well-separated after such operation, which suggests similar benefits as in KPCA and/or K-$k$-means, albeit at the much cheaper cost of a random projection. In particular, our convergence rates suggest that, given a large dataset $\{X_i\}_{i=1}^N$ of size $N$, we can build the Gram matrix $\mathbf{K}$ on a much smaller subsample of size $n\ll N$, so that the sketch $Z\mathbf{K}$ is very cheap to obtain and subsequently apply as a projection operator on the original data $\{X_i\}_{i=1}^N$. We verify these insights empirically on synthetic data, and on real-world clustering applications.
Bayesian kernel machine regression‐causal mediation analysis

Greater understanding of the pathways through which an environmental mixture operates is important to design effective interventions. We present new methodology to estimate natural direct and indirect effects and controlled direct effects of a complex mixture exposure on an outcome through a mediator variable. We implement Bayesian Kernel Machine Regression (BKMR) to allow for all possible interactions and nonlinear effects of (1) the co‐exposures on the mediator, (2) the co‐exposures and mediator on the outcome, and (3) selected covariates on the mediator and/or outcome. From the posterior predictive distributions of the mediator and outcome, we simulate counterfactuals to obtain posterior samples, estimates, and credible intervals of the mediation effects. Our simulation study demonstrates that when the exposure‐mediator and exposure‐mediator‐outcome relationships are complex, BKMR‐Causal Mediation Analysis performs better than current mediation methods. We applied our methodology to quantify the contribution of birth length as a mediator between in utero co‐exposure to arsenic, manganese, and lead, and children's neurodevelopmental scores, in a prospective birth cohort in Bangladesh. Among younger children, we found a negative (adverse) association between the metal mixture and neurodevelopment. We also found evidence that birth length mediates the effect of exposure to the metal mixture on neurodevelopment for younger children. If birth length were fixed to its 75th percentile value, the harmful effect of the metal mixture on neurodevelopment is attenuated, suggesting nutritional interventions to help increase fetal growth, and thus birth length, could potentially block the harmful effect of the metal mixture on neurodevelopment.
The impact of the observed baryon distribution in haloes on the total matter power spectrum

The interpretation of upcoming weak gravitational lensing surveys depends critically on our understanding of the matter power spectrum on scales $k \lt 10\, {h\, {\rm Mpc}^{-1}}$, where baryonic processes are important. We study the impact of galaxy formation processes on the matter power spectrum using a halo model that treats the stars and gas separately from the dark matter distribution. We use empirical constraints from X-ray observations (hot gas) and halo occupation distribution modelling (stars) for the baryons. Since X-ray observations cannot generally measure the hot gas content outside r500c, we vary the gas density profiles beyond this radius. Compared with dark matter only models, we find a total power suppression of $1\, {\mathrm{per\ cent}}$ ($5\, {\mathrm{per\ cent}}$) on scales $0.2\! -\! 1\, {h\, {\rm Mpc}^{-1}}$ ($0.5\! -\! 2\, {h\, {\rm Mpc}^{-1}}$), where lower baryon fractions result in stronger suppression. We show that groups of galaxies ($10^{13} \lt m_{\mathrm{500c}} / (h^{-1}\, \mathrm{M}_{\odot }) \lt 10^{14}$) dominate the total power at all scales $k \lesssim 10\, {h\, {\rm Mpc}^{-1}}$. We find that a halo mass bias of $30\, {\mathrm{per\ cent}}$ (similar to what is expected from the hydrostatic equilibrium assumption) results in an underestimation of the power suppression of up to $4\, {\mathrm{per\ cent}}$ at $k=1\, {h\, {\rm Mpc}^{-1}}$, illustrating the importance of measuring accurate halo masses. Contrary to work based on hydrodynamical simulations, our conclusion that baryonic effects can no longer be neglected is not subject to uncertainties associated with our poor understanding of feedback processes. Observationally, probing the outskirts of groups and clusters will provide the tightest constraints on the power suppression for $k \lesssim 1\, {h\, {\rm Mpc}^{-1}}$.
Two-Stream Action Recognition-Oriented Video Super-Resolution

We study the video super-resolution (SR) problem for facilitating video analytics tasks, e.g. action recognition, instead of for visual quality. The popular action recognition methods based on convolutional networks, exemplified by two-stream networks, are not directly applicable on video of low spatial resolution. This can be remedied by performing video SR prior to recognition, which motivates us to improve the SR procedure for recognition accuracy. Tailored for two-stream action recognition networks, we propose two video SR methods for the spatial and temporal streams respectively. On the one hand, we observe that regions with action are more important to recognition, and we propose an optical-flow guided weighted mean-squared-error loss for our spatial-oriented SR (SoSR) network to emphasize the reconstruction of moving objects. On the other hand, we observe that existing video SR methods incur temporal discontinuity between frames, which also worsens the recognition accuracy, and we propose a siamese network for our temporal-oriented SR (ToSR) training that emphasizes the temporal continuity between consecutive frames. We perform experiments using two state-of-the-art action recognition networks and two well-known datasets--UCF101 and HMDB51. Results demonstrate the effectiveness of our proposed SoSR and ToSR in improving recognition accuracy.
Reasoning Over Paragraph Effects in Situations

A key component of successfully reading a passage of text is the ability to apply knowledge gained from the passage to a new situation. In order to facilitate progress on this kind of reading, we present ROPES, a challenging benchmark for reading comprehension targeting Reasoning Over Paragraph Effects in Situations. We target expository language describing causes and effects (e.g., “animal pollinators increase efficiency of fertilization in flowers”), as they have clear implications for new situations. A system is presented a background passage containing at least one of these relations, a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the background passage in the context of the situation. We collect background passages from science textbooks and Wikipedia that contain such phenomena, and ask crowd workers to author situations, questions, and answers, resulting in a 14,322 question dataset. We analyze the challenges of this task and evaluate the performance of state-of-the-art reading comprehension models. The best model performs only slightly better than randomly guessing an answer of the correct type, at 61.6% F1, well below the human performance of 89.0%.
Polyakov linear-
σ
 model in mean-field approximation and optimized perturbation theory

We compare results from the Polyakov linear-sigma model (PLSM) in optimized perturbation theory (OPT) with the mean-field approximation (MFA). At finite temperatures and chemical potentials, the chiral condensates and the decofinement order parameters, the thermodynamic pressure, the pseudo-critical temperatures, the subtracted condensates, the second- and high-order moments of various conserved charges (cumulants) obtained in MFA are compared with OPT and also confronted to available lattice QCD simulations. We conclude that when moving from lower- to higher-order moments of various quantum charges, OPT becomes more closer to QCD.
Do Co-purchases Reveal Preferences? Explainable Recommendation with Attribute Networks

With the prosperity of business intelligence, recommender systems have evolved into a new stage that we not only care about what to recommend, but why it is recommended. Explainability of recommendations thus emerges as a focal point of research and becomes extremely desired in e-commerce. Existent studies along this line often exploit item attributes and correlations from different perspectives, but they yet lack an effective way to combine both types of information for deep learning of personalized interests. In light of this, we propose a novel graph structure, \emph{attribute network}, based on both items' co-purchase network and important attributes. A novel neural model called \emph{eRAN} is then proposed to generate recommendations from attribute networks with explainability and cold-start capability. Specifically, eRAN first maps items connected in attribute networks to low-dimensional embedding vectors through a deep autoencoder, and then an attention mechanism is applied to model the attractions of attributes to users, from which personalized item representation can be derived. Moreover, a pairwise ranking loss is constructed into eRAN to improve recommendations, with the assumption that item pairs co-purchased by a user should be more similar than those non-paired with negative sampling in personalized view. Experiments on real-world datasets demonstrate the effectiveness of our method compared with some state-of-the-art competitors. In particular, eRAN shows its unique abilities in recommending cold-start items with higher accuracy, as well as in understanding user preferences underlying complicated co-purchasing behaviors.