Miracle-dz/UniFAR-FTUs huggingface dataset:
Lanthanide-containing Microgels Intended for Bead-Based Bioassays by Mass Cytometry

This thesis describes the synthesis and characterization of functional polyelectrolyte copolymer microgels intended for bioassays based upon mass cytometry. The microgels are based upon copolymers of N-isopropylacrylamide (NIPAm), N-vinylcaprolactam (VCL) and methacrylic acid (MAA), crosslinked with methlene-bis-acrylamide, poly(NIPAm/VCL/MAA). The microgels were loaded with Ln(III) ions, which were then converted in situ to LnF3 or LnPO4 nanoparticles (NPs). Very specific conditions were required for confining the NPs to the core of the microgels. I used mass cytometry to measure the number and the particle-to-particle variation of Ln ions per microgel. By controlling the amount of LnCl3 added to the neutralized microgels, we could vary the content of individual microgels from ca. 10 6 to 10 7 Ln atoms, either in the form of Ln 3+ ions or Ln NPs.
Selective Electrochemical Production of Formate from Carbon Dioxide with Bismuth-Based Catalysts in an Aqueous Electrolyte

For the efficient electroconversion of CO2 to formate, CO and H2 evolution must be suppressed. Herein, carbon-supported BiOx nanoparticles (BiOx/C) were investigated as a potential candidate for CO2 reduction. In bicarbonate solutions, the BiOx/C catalysts exhibited a high Faradaic efficiency of 93.4% for formate from −1.37 to −1.70 V versus Ag/AgCl with a negligible amount of CO and H2. Stable partial current densities and high Faradaic efficiencies were also achieved in 0.5 M NaCl (12.5 mA cm–2 and 96.0%, respectively). The possible reaction pathways and kinetic parameters of formate formation were examined using systematic electrochemical methods, including Tafel, pH dependence, and in situ X-ray absorption near-edge structure analyses. From the results of these mechanistic studies, we propose that dual mechanisms are functional on the BiOx/C catalysts. Specifically, a two-electron and one-proton transfer reaction to adsorbed CO2 or a chemical proton transfer reaction to CO2– anion are the possible rat.
THE IMPACT OF MACROECONOMIC POLICIES ON INCOME DISTRIBUTION: AN EMPIRICAL STUDY OF THE PHILIPPINES

There has been a growing awareness of the income distribution dimension of macroeconomic policies. This paper studies this issue empirically considering the case of the Philippines and using data available from integrated surveys of households. After estimating a reduced form equation it was found that underemployment inflation and government spending worsen income distribution while productivity gains the real interest rate and the real exchange rate were found to improve distribution. A similar pattern emerges when the effects of these variables on the absolute incidence of poverty are estimated. (EXCERPT)
DEVELOPMENT OF POLY(D,L-LACTIC ACID) WITH POLYBENZOXAZINE VIA SOLUTION BLENDING

Polybenzoxazine was synthesized by using bisphenol A, formaldehyde and diamine as precursor via condensation reaction. Triethylenetetramine was used as diamine precursor. Polybenzoxazine was characterized by FTIR technique in order to confirm the obtained polybenzoxazine. The ratio of poly(D,Llactic acid)/polybenzoxazine (Mw = 10,000 and 20,000 g/mol) was 0:100, 1:99, 3:97 and 5:95, respectively. The poly(D,L-lactic acid)/polybenzoxazine films were characterized by using Scanning Electron Microscope (SEM). The results showed homogenous and smooth films. Thermal stability of poly(D,L-lactic acid)/polybenzoxazine films was better than pure polybenzoxazine film. Moreover, 3:97 ratio of polybenzoxazine mixed with poly(D,L-lactic acid) (Mw = 20,000 g/mol) film showed good tensile strength,because the stress at break was 24.06 ± 7.99 when determined by Universal Tenting Machine. It was concluded that poly(D,L-lactic acid) improved the property of the polybenzoxazine film.
The moderating influence of trust on the relationship between institutional image/reputation, perceived value on student loyalty in higher education institution

This survey investigates the moderating influence of trust on the relationships between institutional image/reputation, perceived value on student loyalty. The methodology utilises primary data obtained from questionnaire administered to a sample of 304 postgraduate international students in Universiti Utara Malaysia using simple random probability sampling. Multiple regression technique was employed to analyse data via SPSS statistical package. Results established that institutional image and perceived value have significant positive influence on student loyalty. Institutional image has significant positive influence on perceived value. The influence of image on student loyalty is greater followed by image on student perceived value. Furthermore trust was found to insignificantly moderate between institutional image and perceived value on student loyalty. It is recommended that to successfully compete in a dynamic and complex world of academic excellence universities must be seen to portray favourable image/reputation in terms of practices and actions that invariably transforms to higher perceived value and student loyalty behaviour. Keywords: Institutional Image, Perceived Value, Student Loyalty JEL Classifications: M3
The Value of Big N Target Auditors in Corporate Takeovers

Using a large sample between 1987 and 2006, we document that firms with Big N auditors are; 1) more likely to become M&A targets, and 2) more likely to be ultimately acquired in M&A deals. Cross-sectional analyses further suggest that the effects of Big N target auditors on the likelihood of becoming targets and on deal completion rates are more pronounced for firms with low accruals quality (our proxy for high information risk). Overall, the results emphasize the important role that Big N target auditors play in facilitating M&A transactions.
Temperature dependence of the interfacial magnetic anisotropy in W/CoFeB/MgO

The interfacial perpendicular magnetic anisotropy in W/CoFeB (1.2 ∼ 3 nm)/MgO thin film structures is strongly dependent on temperature, and is significantly reduced at high temperature. The interfacial magnetic anisotropy is generally proportional to the third power of magnetization, but an additional factor due to thermal expansion is required to explain the temperature dependence of the magnetic anisotropy of ultrathin CoFeB films. The reduction of the magnetic anisotropy is more prominent for the thinner films; as the temperature increases from 300 K to 400 K, the anisotropy is reduced ∼50% for the 1.2-nm-thick CoFeB, whereas the anisotropy is reduced ∼30% for the 1.7-nm-thick CoFeB. Such a substantial reduction of magnetic anisotropy at high temperature is problematic for data retention when incorporating W/CoFeB/MgO thin film structures into magneto-resistive random access memory devices. Alternative magnetic materials and structures are required to maintain large magnetic anisotropy at elevated tem.
Azopolyimides – influence of chemical structure on azochromophore photo-orientation efficiency

This work is a summary of our previous investigations of polyimides containing derivatives of azobenzene or azopyridine. Here we discussed the relationship between the microstructure of azopolyimides and azochromophore photo-orientation efficiency determined in measurements of photoinduced birefringence as well as the relationship between the microstructure of azopolyimides and their selected physical properties. The designed architecture of azopolymers allowed to trace the effect of such structural elements as the structure of the polymer main chain and chromophore, its content, location including the method of dye assembling with the polymer matrix on thermal, optical and photoinduced properties. We also discussed the possibility of the intermolecular hydrogen bonds formation in functionalized azopolyimides, which hinder isomerization of the azobenzene molecules results the photoinduced birefringence is not observed in the material. Additionally, the possibility of potential applications of azopolyimides as layers for the liquid crystal alignment was presented.
What Do Outside CEOs Really Do? Evidence from Plant-Level Data

Using rich plant-level data, we analyze the relative performance of inside and outside CEOs and provide the first empirical evidence on what CEOs actually do to improve performance. Contrary to conventional wisdom, we show that, relative to insiders, outsiders achieve greater productivity improvements in both low- and high-performing firms. Efficiency gains emerge from divesting low-performing, non-core, and low-tech plants. Additionally, outsiders streamline continuing plants by cutting costs, consolidating products, adopting newer technology, and shifting to more capital-intensive production that improves labor productivity. Outsiders’ advantage in rectifying pre-turnover inefficiencies stems from their ability to stimulate change, rather than their broader experiences.
The Unequal Economic Consequences of Carbon Pricing

This paper studies how carbon pricing affects emissions, economic aggregates and inequality. Exploiting institutional features of the European carbon market and high-frequency data, I identify a carbon policy shock. I find that a tighter carbon pricing regime leads to a significant increase in energy prices, a persistent fall in emissions and an uptick in green innovation. This comes at the cost of a temporary fall in economic activity, which is not borne equally across society: poorer households lower their consumption significantly while richer households are barely affected. Not only are the poor more exposed because of their higher energy share, they also experience a larger fall in their income. These indirect effects play a crucial role in the transmission, accounting for over 80 percent of the aggregate effect on consumption. My results suggest that targeted fiscal policy can reduce the economic costs of carbon pricing without compromising emission reductions.
Rapid nondestructive evaluation of duck meat pH and color using visible and near-infrared reflectance spectroscopy

ABSTRACT In the present study, visible and near-infrared reflectance spectroscopy were applied to predict quality attributes of duck breast meat. The real color (expressed as lightness, redness, and yellowness) and pH of duck samples were recorded using traditional contact methods and then modeled with their corresponding spectral data by partial least square regression. Before the establishment of prediction models, three different spectral preprocessing methods including first derivative, standard normalized variate, and Savitzky–Golay smoothing were used. Compared to the models obtained from original spectral data set, the predictive capabilities of models based on the spectra after preprocessing were improved effectively. As a result, the determination coefficient of calibration set and prediction set of the best models for lightness, redness, yellowness, and pH parameters were 0.96 and 0.85, 0.94 and 0.94, 0.96 and 0.94, 0.81 and 0.76, respectively. Results demonstrated that visible and near-infrared spectroscopy can become a useful tool for rapid prediction of duck color and pH quality attributes.
Investigation of Dataset Features for Just-in-Time Defect Prediction

Just-in-time (JIT) defect prediction refers to the technique of predicting whether a code change is defective. Many contributions have been made in this area through the excellent dataset by Kamei. In this paper, we revisit the dataset and highlight preprocessing difficulties with the dataset and the limitations of the dataset on unsupervised learning. Secondly, we propose certain features in the Kamei dataset that can be used for training models. Lastly, we discuss the limitations of the dataset's features.
Millimeter Wave and Terahertz Urban Microcell Propagation Measurements and Models

Comparisons of outdoor Urban Microcell (UMi) large-scale path loss models, root mean square (RMS) delay spreads (DS), angular spreads (AS), and the number of spatial beams for extensive measurements performed at 28, 38, 73, and 142 GHz are presented in this letter. Measurement campaigns were conducted from 2011–2020 in downtown Austin, Texas, Manhattan (New York City), and Brooklyn, New York with communication ranges up to 930 m. Key similarities and differences in outdoor wireless channels are observed when comparing the channel statistics across a wide range of frequencies from millimeter-wave to sub-THz bands. Path loss exponents (PLEs) are remarkably similar over all measured frequencies, when referenced to the first meter free space path loss, and the RMS DS and AS decrease as frequency increases. The similar PLEs from millimeter-wave to THz frequencies imply that spacing between cellular base stations will not have to change as carrier frequencies increase towards THz, since wider bandwidth channels at sub-THz or THz carrier frequencies will cover similar distances because antenna gains increase quadratically with increasing frequency when the physical antenna area remain constant.
SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases

Knowledge Base Question Answering (KBQA) tasks that in-volve complex reasoning are emerging as an important re-search direction. However, most KBQA systems struggle withgeneralizability, particularly on two dimensions: (a) acrossmultiple reasoning types where both datasets and systems haveprimarily focused on multi-hop reasoning, and (b) across mul-tiple knowledge bases, where KBQA approaches are specif-ically tuned to a single knowledge base. In this paper, wepresent SYGMA, a modular approach facilitating general-izability across multiple knowledge bases and multiple rea-soning types. Specifically, SYGMA contains three high levelmodules: 1) KB-agnostic question understanding module thatis common across KBs 2) Rules to support additional reason-ing types and 3) KB-specific question mapping and answeringmodule to address the KB-specific aspects of the answer ex-traction. We demonstrate effectiveness of our system by evalu-ating on datasets belonging to two distinct knowledge bases,DBpedia and Wikidata. In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper. We show that our generalizable approach has bettercompetetive performance on multiple datasets on DBpediaand Wikidata that requires both multi-hop and temporal rea-soning
Central Limit Theorem for $C\beta E$ Pair Dependent Statistics in Mesoscopic Regime

We extend our results on the fluctuation of the pair counting statistic of the Circular Beta Ensemble $\sum_{i\neq j}f(L_N(\theta_i-\theta_j))$ for arbitrary $\beta>0$ in the mesoscopic regime $L_N=O(N^{2/3-\epsilon})$.
Simultaneous X-ray and radio observations of the transitional millisecond pulsar candidate CXOU J110926.4–650224

We present the results of simultaneous observations of the transitional millisecond pulsar (tMSP) candidate CXOU J110926.4–650224 with the XMM-Newton satellite and the MeerKAT telescope. The source was found at an average X-ray luminosity of LX ≃ 7 × 1033 erg s−1 over the 0.3−10 keV band (assuming a distance of 4 kpc) and displayed a peculiar variability pattern in the X-ray emission, switching between high, low and flaring modes on timescales of tens of seconds. A radio counterpart was detected at a significance of 7. 9σ with an average flux density of ≃33 μJy at 1.28 GHz. It showed variability over the course of hours and emitted a ≃10-min long flare just a few minutes after a brief sequence of multiple X-ray flares. No clear evidence for a significant correlated or anticorrelated variability pattern was found between the X-ray and radio emissions over timescales of tens of minutes and longer. CXOU J110926.4–650224 was undetected at higher radio frequencies in subsequent observations performed with the Australia Telescope Compact Array, when the source was still in the same X-ray sub-luminous state observed before, down to a flux density upper limit of 15 μJy at 7.25 GHz (at 3σ). We compare the radio emission properties of CXOU J110926.4–650224 with those observed in known and candidate tMSPs and discuss physical scenarios that may account for its persistent and flaring radio emissions.
DeepPSL: End-to-end perception and reasoning with applications to zero shot learning

We introduce DeepPSL a variant of Probabilistic Soft Logic (PSL) to produce an end-to-end trainable system that integrates reasoning and perception. PSL represents ﬁrst-order logic in terms of a convex graphical model – Hinge-Loss Markov random ﬁelds (HL-MRFs). PSL stands out among probabilistic logic frameworks due to its tractability having been applied to systems of more than 1 billion ground rules. The key to our approach is to represent predicates in ﬁrst-order logic using deep neural networks and then to approximately back-propagate through the HL-MRF and thus train every aspect of the ﬁrst-order system being represented. We believe that this approach represents an interesting direction for the integration of deep learning and reasoning techniques with applications to knowledge base learning, multi-task learning, and explainability. We evaluate DeepPSL on a zero shot learning problem in image classiﬁcation. State of the art results demonstrate the utility and ﬂexibility of our approach.
“How Robust R U?”: Evaluating Task-Oriented Dialogue Systems on Spoken Conversations

Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets. However, written dialogues are not sufficient to fully capture the nature of spoken conversations as well as the potential speech recognition errors in practical spoken dialogue systems. This work presents a new benchmark on spoken task-oriented conversations, which is intended to study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. We report that the existing state-of-the-art models trained on written conversations are not performing well on our spoken data, as expected. Furthermore, we observe improvements in task performances when leveraging $n$-best speech recognition hypotheses such as by combining predictions based on individual hypotheses. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
To Which Out-Of-Distribution Object Orientations Are DNNs Capable of Generalizing?

The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data, ie. , out-of-distribution (OoD) orientations, is not well understood. For humans, behavioral studies showed that recognition accuracy varies across OoD orientations, where generalization is much better for some orientations than for others. In contrast, for DNNs, it remains unknown how generalization abilities are distributed among OoD orientations. In this paper, we investigate the limitations of DNNs’ generalization capacities by systematically inspecting patterns of success and failure of DNNs across OoD orientations. We use an intuitive and controlled, yet challenging learning paradigm, in which some instances of an object category are seen at only a few geometrically restricted orientations, while other instances are seen at all orientations. The effect of data diversity is also investigated by increasing the number of instances seen at all orientations in the training set. We present a comprehensive analysis of DNNs’ generalization abilities and limitations for representative architectures (ResNet, Inception, DenseNet and CORnet). Our results reveal an intriguing pattern—DNNs are only capable of generalizing to instances of objects that appear like 2D, ie. , in-plane, rotations of in-distribution orientations.
Making Curiosity Explicit in Vision-based RL

Vision-based reinforcement learning (RL) is a promising technique to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in terms of sample efficiency, especially when using image observations. This has led to an increased attention on integrating state representation learning (SRL) techniques into the RL pipeline. Work in this field demonstrates a substantial improvement in sample efficiency among other benefits. However, to take full advantage of this paradigm, the quality of samples used for training plays a crucial role. More importantly, the diversity of these samples could affect the sample efficiency of vision-based RL, but also its generalization capability. In this work, we present an approach to improve the sample diversity. Our method enhances the exploration capability of the RL algorithms by taking advantage of the SRL setup. Our experiments show that the presented approach outperforms the baseline for all tested environments. These results are most apparent for environments where the baseline method struggles. Even in simple environments, our method stabilizes the training, reduces the reward variance and boosts sample efficiency.
Optimization of Photocatalytic Decolourization of Methyl Green Dye Using Commercial Zinc Oxide as catalyst

This study describes the results for series of experiments on photocatalytic decolourization of methyl green dye from aqueous solution in the presence of commercial ZnO suspension. The photocatalytic decolourization has been studied as a function of initial dye concentration, best dose of ZnO, initial pH of aqueous solution, and temperature by regular intervals from experiments. The decolourization of methyl green dye process was found to follow pseudo-first order kinetics with the use of artificial UV (A) light sources. The optimum dye and catalyst concentration was measured and found equal to be 50 ppm and 700 mg/ 200 mL -1 respectively. The best value of pH for the photocatalytic decolourization of methyl green dye in the presence of ZnO suspension is pH 5.4. The Photocatalytic decolourization reaction of dye was endothermic and found the rate of reaction increased with increasing temperature. Arrhenius plot obtains that the activation energy for this reaction is equal to 24. 914 kJ mol -1 , this value is small that beyond to be associated with the transport of photoelectrons through the catalyst to the adsorbed oxygen on the surface.