Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Interpretability by generalized distillation in Supervised Classification

The ability to interpret decisions taken by Machine Learning (ML) models is fundamental to encourage trust and reliability in different practical applications. Recent interpretation strategies focus on human understanding of the underlying decision mechanisms of the complex ML models. However, these strategies are restricted by the subjective biases of humans. To dissociate from such human biases, we propose an interpretation-by-distillation formulation that is defined relative to other ML models. We generalize the distillation technique for quantifying interpretability, using an information-theoretic perspective, removing the role of ground-truth from the definition of interpretability. Our work defines the entropy of supervised classification models, providing bounds on the entropy of Piece-Wise Linear Neural Networks (PWLNs), along with the first theoretical bounds on the interpretability of PWLNs. We evaluate our proposed framework on the MNIST, Fashion-MNIST and Stanford40 datasets and demonstrate the applicability of the proposed theoretical framework in different supervised classification scenarios.
Structural and Anisotropic Thermal Expansion Correlation of Li2ZrO3 at Different Temperatures

Structural and thermal expansion studies of Li2ZrO3 were carried out by recording X-ray powder diffraction patterns at five temperatures, 82, 198, 300, 423 and 573 K. The Rietveld refinement method was used for the analysis. There were large anisotropic changes in the lattice parameters as a function of temperature, slight changes in the fractional coordinates of the different atoms, and anisotropic changes in the interatomic distances between the different cations. The correlation of these anisotropies with the structure is considered.
Bioconversion of Beetle-Killed Lodgepole Pine Using SPORL: Process Scale-up Design, Lignin Coproduct, and High Solids Fermentation without Detoxification

Mountain pine beetle killed Lodgepole pine (Pinus contorta Douglas ex Loudon) wood chips were pretreated using an acidic sulfite solution of approximately pH = 2.0 at a liquor to wood ratio of 3 and sodium bisulfite loading of 8 wt % on wood. The combined hydrolysis factor (CHF), formulated from reaction kinetics, was used to design a scale-up pretreatment on 2000 g wood chips at a relatively low temperature of 165 °C that reduced furan formation and facilitated high solids saccharification and fermentation. The pretreated solids and liquor were disk milled together to result in a biomass whole slurry of 25% total solids. The whole biomass slurry was directly used to conduct simultaneous enzymatic saccharification and combined fermentation (SSCombF) using a commercial cellulase and Saccharomyces cerevisiae YRH400 without detoxification. A terminal ethanol titer of 47.1 g L–1 with a yield of 306 L (tonne wood)−1, or 72.0% theoretical, was achieved when SSCombF was conducted at an unwashed solids loading of.
Ammonia volatilization from urea as affected by tropical‐based palm oil mill effluent (Pome) and peat

Abstract Ammonia (NH3) volatilization is a major pathway of nitrogen (N) loss which limits the efficiency of urea as a fertilizer when surface‐applied to soils. High pH and low cation exchange capacity in soils have been identified as the principal causes of NH3 volatilization from urea. The several approaches proposed to correct such inefficiency in urea were, thus far, fundamentally based upon delay of urea dissolution and impedance of urea hydrolysis. An attempt was made to establish a preferred environment within the urea‐soil reaction zone (microsite) using palm oil mill effluent (POME) and peat. The organic materials were separately matrixed with urea into pelletized form and evaluated under laboratory regimes for the per cent NH3 volatilization, pH change, and ammonium‐nitrogen (NH4‐N) recovery. Estimation of NH3 volatilization was carried out using a closed‐dynamic air‐flow system. Determination of the chemical and physical attributes of soils and materials, and the measurement of the parameters s.
Texan City magazine health news : a content analysis

City magazines have a powerful role in convincing readers to take proactive health measures, however they rarely take advantage of their capacity to set their communities’ public agendas. This study considered the health content in five city magazines in Texas: Austin Monthly, D Magazine, Fort Worth, Texas Magazine, Houstonia, and San Antonio Magazine. Using agenda building, agenda-setting, and second-level agenda-setting, this research quantitatively analyzed 169 health articles published in 2018 on the five magazines’ websites for how the publications presented health news. The core findings of this research included: the overall agenda was actionable health, the city magazines were more than three times more likely to cite an indirect source than a human, and that salient story elements promoted the historical status quo of city magazines and not the health agenda or health literacy. The most prevalent story topic was nutrition/fitness, occurring in the majority of the articles. The core of my research was to question whether these publications were publishing health content that reflected their cities’ health profiles. While some magazines, such as Houstonia and Fort Worth, Texas Magazine did better than others, all of the city magazines could improve. HEALTH NEWS IN CITY MAGAZINES
ECRIS Operation With Multiple Frequencies

The usefulness of two-frequency heating for the production of high-charge state high-intensity beams from an ECRIS has been well established. Factors of 2{yields}5 increase in beam currents have been observed accompanied by a shift to higher charge states. The ECRIS at Argonne National Laboratory has been routinely operated utilizing a 14 GHz klystron and a tunable 11-13 GHz traveling wave tube amplifier (TWTA) and the operating characteristics of the source are well known. However, the characteristics of the multi-frequency heated plasma are less well known. Investigations regarding the changes in the source production have been taking place at Argonne National Laboratory. Parameters such as the charge state distribution (CSD), production times and plasma potential have been measured for a multi-frequency heated plasma with emphasis being given to the effect of the frequency gap between the two RF waves. It has been found that the production times decrease in multi-frequency mode with a corresponding increase in the CSD and the overall beam output. At the same time, the plasma potential appears to not change significantly. It has also been found that a larger frequency gap (14.0 and 10.84 GHz), while producing higher charge-state ions, produces less overall beam of the materialmore » of interest but reaches equilibrium more quickly when compared to a smaller gap (14,0 and 12.31 GHz). Possible mechanisms for the observed behavior will be discussed. « less
PSCNet: Pyramidal Scale and Global Context Guided Network for Crowd Counting

Crowd counting, which towards to accurately count the number of the objects in images, has been attracted more and more attention by researchers recently. However, challenges from severely occlusion, large scale variation, complex background interference and non-uniform density distribution, limit the crowd number estimation accuracy. To mitigate above issues, this paper proposes a novel crowd counting approach based on pyramidal scale module (PSM) and global context module (GCM), dubbed PSCNet. Moreover, a reliable supervision manner combined Bayesian and counting loss (BCL) is utilized to learn the density probability and then computes the count exception at each annotation point. Specifically, PSM is used to adaptively capture multi-scale information, which can identify a fine boundary of crowds with different image scales. GCM is devised with low-complexity and lightweight manner, to make the interactive information across the channels of the feature maps more efficient, meanwhile guide the model to select more suitable scales generated from PSM. Furthermore, BL is leveraged to construct a credible density contribution probability supervision manner, which relieves non-uniform density distribution in crowds to a certain extent. Extensive experiments on four crowd counting datasets show the effectiveness and superiority of the proposed model. Additionally, some experiments extended on a remote sensing object counting (RSOC) dataset further validate the generalization ability of the model. Our resource code will be released upon the acceptance of this work.
Metabolite Profiling of Osteoporosis and Atherosclerosis in Postmenopausal Women: A Cross-Sectional Study

Purpose Atherosclerosis (AS) and osteoporosis (OP) are common causes of morbidity and mortality in postmenopausal women and are connected via an unknown mechanistic link. Metabolite profiling of blood samples may allow the identification of new biomarkers and pathways for this enigmatic association. Patients and Methods We studied the difference in 148 metabolite levels from serum samples in postmenopausal women with AS and OP compared with those in healthy participants in this cross-sectional study. Quantitative AS was assessed by carotid artery intima-media thickness (cIMT) and carotid artery calcifications (CACs) by ultrasound, as well as OP by femoral neck (FN) bone mineral density (BMD) and 148 metabolic measures with high-throughput proton (1H) nuclear magnetic resonance (NMR) in serum samples from 280 postmenopausal (PM) women. Subjects were a randomly selected subsample from the population-based Kuopio Osteoporosis Risk Factor and Prevention (OSTPRE) study. The final study population included the following groups: OP with CAC (n=16, group I), non-OP with no CAC (n=59, group II), high cIMT tertile with OP (n=11, group III) and low cIMT tertile without OP (n=48, group IV). Results There were differences in several metabolite levels between groups I and II. The acetate level was lower in group I compared to that in group II (group I mean ± SD: 0.033 ± 0.0070; group II: 0.041 ± 0.014, CI95%: 0.018‒0.15, p=0.014). The result was similar with diacylglycerol (p=0.002), leucine (p=0.031), valine (p=0.022) and several very low-density lipoprotein (VLDL) metabolite levels, which were lower in group I compared to those in group II. However, no associations were found in adjusted analyses with total body (TB) fat mass (FM), age and statin use (p>0.05). Conclusion Our novel study found differences in the metabolite profiling of altered amino acid and lipoprotein metabolism in participants with OP and AS compared with those in healthy women. The causative mechanisms remain unknown and further studies are needed.
Comparison of dexmedetomidine-propofol and ketamine-propofol administration during sedation-guided upper gastrointestinal system endoscopy

Abstract Background: Dexmedetomidine and ketamine popular sedative agents that result in minimal respiratory depression and the presence of analgesic activity. We aimed to compare the effectiveness and safety of a dexmedetomidine-propofol combination and a ketamine-propofol combination during upper gastrointestinal system endoscopy. Methods: The study commenced after receiving approval from the local ethics committee. Patients between 18 and 60 years in the American Society of Anesthesiologists (ASA) I and II groups were included. Patients who had severe organ disease, who had allergies to the study drugs, and who refused to participate were excluded. Cases were randomized into a dexmedetomidine-propofol group (Group D, n = 30) and a ketamine-propofol group (Group K, n = 30). Cardiac monitoring, peripheral oxygen saturation, and bispectral index (BIS) monitoring were performed. Group D received 1 mg/kg dexmedetomidine + 0.5 mg/kg propofol intravenous (IV) bolus, 0.5 μg/kg/h dexmedetomidine + 0.5 mg/kg/h propfol infusion. Group K received 1 mg/kg ketamine + 0.125 mL/kg propofol iv bolus, 0.25 mg/kg/h ketamine + 0.125 mL/kg/h propfol infusion. Patients were followed up with a Ramsay Sedation Scale (RSS) of ≥4. Means, standard deviations, lowest and highest frequency values, and ratio values were used for descriptive statistics, and the SPSS 22.0 program was used for statistical analyses. Results: In Group K, recovery time and mean blood pressure (MBP) values were significantly shorter. Furthermore, coughing rate, pulse, and BIS values were higher than in Group D (P < .05). Although there were no significant differences between the groups in terms of endoscopic tolerance and endoscopist satisfaction, we observed that the dexmedetomidine group experienced more comfortable levels of sedation. Conclusion: Dexmedetomidine-propofol and ketamine-propofol combinations may be suitable and safe for endoscopy sedation due to their different properties. It was observed that the dexmedetomidine-propfol combination was superior in terms of sedation depth and that the ketamine-propofol combination was superior in terms of early recovery. As a result, we suggest the dexmedetomidine-propofol combination for upper gastrointestinal system endoscopy sedation due to hemodynamic stability and minimal adverse effects.
Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection

Although deep neural networks have shown promising performances on various tasks, they are susceptible to incorrect predictions induced by imperceptibly small perturbations in inputs. A large number of previous works proposed to detect adversarial attacks. Yet, most of them cannot effectively detect them against adaptive whitebox attacks where an adversary has the knowledge of the model and the defense method. In this paper, we propose a new probabilistic adversarial detector motivated by a recently introduced non-robust feature. We consider the non-robust features as a common property of adversarial examples, and we deduce it is possible to find a cluster in representation space corresponding to the property. This idea leads us to probability estimate distribution of adversarial representations in a separate cluster, and leverage the distribution for a likelihood based adversarial detector.
Space charge profiles in low density polyethylene samples containing a permittivity/conductivity gradient

Laser induced pressure pulse space charge measurements were made on 1.5 mm thick plaques of high purity low density polyethylene equipped with vacuum-evaporated aluminium electrodes. Temperature differences up to 20 °C were maintained across the samples, which were subjected to dc fields up to 1.5×107 V m-1. Current density was also measured as a function of temperature and field. Space charge due exclusively to the temperature gradient was detected, with density of order 0.01 C m-3. The activation energy associated with the transport of electrons through the bulk was calculated as 0.09 eV. However, measurements of the temperature dependence of the current in isothermal samples yielded activation energies in the range 0.9-1.3 eV. It is deduced that most of this larger activation energy is associated with charge injection at the electrodes. Electron mobilities not less than 2×10-12 m2 V-1 s-1 were inferred by combining the space charge and current density measurements.
Comparison of Iron Availability in Leaves of Barley and Rice

Iron (Fe) is an essential trace element in all eukaryotes. In higher plants, Fe deficiency causes interveinal chlorosis in young leaves. However, in barley and rice, both of which are “Strategy II” plants, the degree and the pattern of Fe-deficiency symptoms differ. In the present study, barley and rice plants were grown in the same container, i.e., by “coculturing,” to compensate for the amount of mugineic acids in rice in the nutrient solution. We examined the differential availability of Fe for distribution and retranslocation in shoots between barley and rice without considering the difference in the iron acquisition ability, which is affected by the differential mugineic acid secretion between barley and rice. Although the Fe concentration of young barley leaves had decreased under the coculture conditions, the SPAD value was similar to that in monocultured barley. In contrast, although there was an increase in the Fe concentration of the young leaves of cocultured rice, the SPAD value decreased, as in the case of monocultured rice. Rice accumulated Fe in old leaves, whereas in barley Fe was efficiently distributed to young leaves. Therefore, the SPAD value of the second leaf in rice remained constantly high. The Fe concentration of the second leaf in barley decreased under Fe-deficient coculture conditions, the SPAD value decreased and the senescence of the second leaf become accelerated. 59Fe pulse-labeling experiments suggested that in barley Fe was more efficiently retranslocated from old leaves to young leaves than that in rice. As a result, the level of Fe present in the fraction with a molecular weight lower than the 10,000/water-soluble Fe ratio was higher in the old leaves of barley than in the old leaves of rice under Fe-deficient conditions. Based on the results obtained, we suggest that the distribution and retranslocation characteristics of internal Fe in barley may be well adapted to Fe deficiency.
Global regularity and time decay for the SQG equation with anisotropic fractional dissipation

In this paper, we focus on the two-dimensional surface quasi-geostrophic equation with fractional horizontal dissipation and fractional vertical thermal diffusion. On the one hand, when the dissipation powers are restricted to a suitable range, the global regularity of the surface quasi-geostrophic equation is obtained by some anisotropic embedding and interpolation inequalities involving fractional derivatives. One the one hand, we obtain the optimal large time decay estimates for global weak solutions by an anisotropic interpolation inequality. Moreover, based on the argument adopted in establishing the global $\dot{H}^1$-norm of the solution, we obtain the optimal large time decay estimates for the above obtained global smooth solutions. Finally, the decay estimates for the difference between the full solution and the solution to the corresponding linear part are also derived.
On the Negative Hydration. A Nuclear Magnetic Relaxation Study

The paper is opened by a structural and thermodynamic analysis of the phenomenon of negative hydration (or structure breaking effect). This treatment shows that the state of negative hydration is energetically higher than the state of hydration which is caused by an inert solute particle. Furthermore, in the state of negative hydration the molecular configurations of the hydration sphere are spread over a comparatively wide range.Proton relaxation rates in aqueous solutions of an extended number of ions of different types have been measured in order to investigate which ions show negative hydration. The reorientation time of the water molecule is taken as an indicator for the nature of the hydration. Among others some doubly charged anions were found to be structure breakers. In non‐aqueous solvents generally no negative solvation seems to be present, there are, however, at least two exceptions: ethylene glycol and glycerol. Furthermore, it is demonstrated that the negative hydration is a cooperative effect between many water molecules since the addition of organic molecules to the solution generally destroys this effect. In the concluding discussion it is shown that the structure breaking effect is caused by a weak electrostatic field to be predicted from the rough geometry of the ion considered.
High-Order Steady-State Diffusion Approximations

Much like higher-order Taylor expansions allow one to approximate functions to a higher degree of accuracy, we demonstrate that, by accounting for higher-order terms in the Taylor expansion of a Markov process generator, one can derive novel diffusion approximations that achieve a higher degree of accuracy compared with the classical ones used in the literature over the last 50 years.
Finite presentations for stated skein algebras and lattice gauge field theory

We provide finite presentations for stated skein algebras and deduce that those algebras are Koszul and that they are isomorphic to the quantum moduli algebras appearing in lattice gauge field theory, generalizing previous results of Bullock, Frohman, Kania-Bartoszynska and Faitg.
Silver staining of DNA in polyacrylamide gels: Linearity and effect of fragment size

A simplified, inexpensive silver stain for proteins in polyacrylamide gels also stains DNA with a sensitivity of 1 ng (< 0.03 ng/mm2). Sensitivity of detection is superior to ethidium bromide and, for 11 double‐stranded fragments of ϕX174 DNA, staining is linear with respect to concentration. Surprisingly, the slope of staining is similar for pieces of DNA larger than 300 base pairs, but is greater for fragments of smaller size. Staining of DNA with silver is a practical alternative to visualization with ethidium bromide and may serve as a tool for elucidating the basis of silver staining.
Physics Guided Machine Learning Methods for Hydrology

Streamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generation. While physically-based models are rooted in rich understanding of the physical processes, a significant performance gap still remains which can be potentially addressed by leveraging the recent advances in machine learning. The goal of this work is to incorporate our understanding of physical processes and constraints in hydrology into machine learning algorithms, and thus bridge the performance gap while reducing the need for large amounts of data compared to traditional data-driven approaches. In particular, we propose an LSTM based deep learning architecture that is coupled with SWAT (Soil and Water Assessment Tool), an hydrology model that is in wide use today. The key idea of the approach is to model auxiliary intermediate processes that connect weather drivers to streamflow, rather than directly mapping runoff from weather variables which is what a deep learning architecture without physical insight will do. The efficacy of the approach is being analyzed on several small catchments located in the South Branch of the Root River Watershed in southeast Minnesota. Apart from observation data on runoff, the approach also leverages a 200-year synthetic dataset generated by SWAT to improve the performance while reducing convergence time. In the early phases of this study, simpler versions of the physics guided deep learning architectures are being used to achieve a system understanding of the coupling of physics and machine learning. As more complexity is introduced into the present implementation, the framework will be able to generalize to more sophisticated cases where spatial heterogeneity is present.