Miracle-dz/UniFAR-FTUs huggingface dataset:
New bioconjugated rhenium carbonyls by transmetalation reaction with zinc derivatives

The transmetallation reaction between zinc dithiocarbamates and rhenium carbonyls has been used as a new strategy to link biomolecules to transition metals. The zinc(II) dithiocarbamate of isonipecotic acid (1) and the succinimidyl ester derivative (2) were prepared by straight forward procedures and were fully characterized by spectroscopic and X-ray diffraction methods, showing in both cases the presence of dinuclear complexes. Complex 2 reacted with all the primary and secondary amines studied (glycine methyl ester, β-alanine methyl ester, 1-(2-methoxyphenyl)piperazine and D-(+)-glucosamine) through the activated succinimidyl ester group, linking the metallic fragment with the biomolecule by the formation of a peptidic bond, and leading to the respective bioconjugated zinc complexes 3-6. In all cases, these zinc complexes could be isolated from the reaction medium by simple precipitation. These results evidence the potential of complex 2 to be used as a synthon to link the zinc dithiocarbamate fragment to biomolecules that contain an amine group. Complexes 3-6 were characterized by the usual spectroscopic methods and all data agree with the proposed structures, which do not contain significant interactions between the zinc fragment and the functional groups of these biomolecules. The transmetallation reaction between the zinc complexes 3-6 and the rhenium carbonyl [ReBr₃(CO)₃]²⁻ led to the expected rhenium dithiocarbamates 7-10 with no change in the organic dithiocarbamate fragments, confirming the viability of this reaction as a tool for linking biomolecules to transition elements. All complexes were characterized by spectroscopic methods and the crystal structure of 8 was studied by X-ray diffraction analysis. All data demonstrated that the biomolecule is positioned far away from the fac-{Re(CO)₃} fragment and the octahedral coordination around the metal is completed by the functionalized dithiocarbamate and a phosphine ligand. Finally, the analysis by ESI-MS spectrometry of the reaction between the zinc complex 4 and a water solution of [Re(H₂O)₃(CO)₃]+ at a very low concentration (10 ppm) showed that the transmetallation reaction took place even though the solubility of the zinc complex in water medium was as low as 0.66 ppm. This preliminary result supports the viability of this approach for the preparation of rhenium and technetium target specific radiopharmaceuticals since the preparation of these compounds are always performed in water medium.
q-plane zeros of the Potts partition function on diamond hierarchical graphs

We report exact results concerning the zeros of the partition function of the Potts model in the complex $q$ plane, as a function of a temperature-like Boltzmann variable $v$, for the $m$'th iterate graphs $D_m$ of the Diamond Hierarchical Lattice (DHL), including the limit $m \to \infty$. In this limit we denote the continuous accumulation locus of zeros in the $q$ planes at fixed $v = v_0$ as ${\mathcal B}_q(v_0)$. We apply theorems from complex dynamics to establish properties of ${\mathcal B}_q(v_0)$. For $v=-1$ (the zero-temperature Potts antiferromagnet, or equivalently, chromatic polynomial), we prove that ${\mathcal B}_q(-1)$ crosses the real-$q$ axis at (i) a minimal point $q=0$, (ii) a maximal point $q=3$ (iii) $q=32/27$, (iv) a cubic root that we give, with the value $q = q_1 = 1.6388969. $, and (v) an infinite number of points smaller than $q_1$, converging to $32/27$ from above. Similar results hold for ${\mathcal B}_q(v_0)$ for any $-1 0$ (Potts ferromagnet). We also provide computer-generated plots of ${\mathcal B}_q(v_0)$ at various values of $v_0$ in both the antiferromagnetic and ferromagnetic regimes and compare them to numerically computed zeros of $Z(D_4,q,v_0)$.
How Decoding Strategies Affect the Verifiability of Generated Text

Recent progress in pre-trained language models led to systems that are able to generate text of an increasingly high quality. While several works have investigated the fluency and grammatical correctness of such models, it is still unclear to which extent the generated text is consistent with factual world knowledge. Here, we go beyond fluency and also investigate the verifiability of text generated by state-of-the-art pre-trained language models. A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy. In particular, we discover a tradeoff between factuality (i.e., the ability of generating Wikipedia corroborated text) and repetitiveness. While decoding strategies such as top-k and nucleus sampling lead to less repetitive generations, they also produce less verifiable text. Based on these finding, we introduce a simple and effective decoding strategy which, in comparison to previously used decoding strategies, produces less repetitive and more verifiable text.
Mathematical Physics Continuity of the von Neumann Entropy

A general method for proving continuity of the von Neumann entropy on subsets of positive trace-class operators is considered. This makes it possible to rederive the known conditions for continuity of the entropy in more general forms and to obtain several new conditions. The method is based on a particular approximation of the von Neumann entropy by an increasing sequence of concave continuous unitary invariant functions defined using decompositions into finite rank operators. The existence of this approximation is a corollary of a general property of the set of quantum states as a convex topological space called the strong stability property. This is considered in the first part of the paper.
Forecasting Return Volatility of the CSI 300 Index Using the Stochastic Volatility Model with Continuous Volatility and Jumps

The logarithmic realized volatility is divided into the logarithmic continuous sample path variation and the logarithmic discontinuous jump variation on the basis of the SV-RV model in this paper, which constructs the stochastic volatility model with continuous volatility (SV-CJ model). Then, we use high-frequency transaction data for five minutes of the CSI 300 stock index as the study sample, which, respectively, make parameter estimation on the SV, SV-RV, and SV-CJ model. We also comparatively analyze these three models' prediction accuracy by using the loss functions and SPA test. The results indicate that the prior logarithmic realized volatility and the logarithmic continuous sample path variation can be used to predict the future return volatility in China's stock market, while the logarithmic discontinuous jump variation is poor at its prediction accuracy. Besides, the SV-CJ model has an obvious advantage over the SV and SV-RV model as to the prediction accuracy of the return volatility, and it is more suitable for the research concerning the problems of financial practice such as the financial risk management.
Atomistic computer simulation of the clay–fluid interface in colloidal laponite

Monte Carlo and molecular dynamics computer simulations have been used to study the structure and dynamics of the interlayer aqueous solution in a colloidal sodium laponite clay at 277 K. The system studied has a clay–clay spacing of 34.06 A, and contains 1200 interlayer water molecules and 24 sodium counterions. The density profiles for interlayer species show two distinct layers of surface water as one moves away from the clay particles. The innermost of these layers is strongly oriented to form hydrogen bonds to the surface oxygen atoms. Radially averaged pair distributions have been calculated as a function of distance from the clay surfaces, and show that throughout our system the water structure is significantly perturbed from the bulk. In particular, we observe an increase in the second nearest-neighbor oxygen–oxygen distance, similar to that reported for low-density water at 268 K [A. K. Soper and M. A. Ricci, Phys. Rev. Lett. 84, 2881 (2000)]. The majority of the sodium counterions are fully hydrated by six water molecules. These hydrated ions have a strong tendency to remain close to the solid surfaces, as so-called "outer-sphere" complexes. However, we also observe cations further from the clay sheets, in the diffuse layer. Diffusion of water and cations in the plane of the clay sheets is comparable to that in the bulk, but is significantly reduced normal to the clay sheets.
Signature of a pair of Majorana zero modes in superconducting gold surface states

Significance In 1937 Ettore Majorana pointed out that unlike the standard solution of the Dirac equation which led to the electron and its antiparticle, the positron, it is possible to have a solution where the fermionic particle is its own antiparticle. While such a particle remains elusive in particle physics, a pair of localized Majorana states has been predicted to reside at the ends of a specially designed superconducting wire. These Majorana zero modes can form building blocks of qubits for fault-tolerant quantum computers. Here we report signatures of Majorana zero modes in a platform consisting of EuS and gold films grown on vanadium. This platform is amenable to standard nanofabrication techniques and holds promise for scalable Majorana qubits. Under certain conditions, a fermion in a superconductor can separate in space into two parts known as Majorana zero modes, which are immune to decoherence from local noise sources and are attractive building blocks for quantum computers. Promising experimental progress has been made to demonstrate Majorana zero modes in materials with strong spin–orbit coupling proximity coupled to superconductors. Here we report signatures of Majorana zero modes in a material platform utilizing the surface states of gold. Using scanning tunneling microscope to probe EuS islands grown on top of gold nanowires, we observe two well-separated zero-bias tunneling conductance peaks aligned along the direction of the applied magnetic field, as expected for a pair of Majorana zero modes. This platform has the advantage of having a robust energy scale and the possibility of realizing complex designs using lithographic methods.
Learning to Order Sub-questions for Complex Question Answering

Answering complex questions involving multiple entities and relations is a challenging task. Logically, the answer to a complex question should be derived by decomposing the complex question into multiple simple sub-questions and then answering those sub-questions. Existing work has followed this strategy but has not attempted to optimize the order of how those sub-questions are answered. As a result, the sub-questions are answered in an arbitrary order, leading to larger search space and a higher risk of missing an answer. In this paper, we propose a novel reinforcement learning(RL) approach to answering complex questions that can learn a policy to dynamically decide which sub-question should be answered at each stage of reasoning. We lever-age the expected value-variance criterion to enable the learned policy to balance between the risk and utility of answering a sub-question. Experiment results show that the RL approach can substantially improve the optimality of ordering the sub-questions, leading to improved accuracy of question answering. The proposed method for learning to order sub-questions is general and can thus be potentially combined with many existing ideas for answering complex questions to enhance their performance.
Development of Milk Chocolate Using Response Surface Methodology (RSM)

It can be either in the form of a liquid, paste or a block or used as a flavouring ingredient in different foods (Shahkhalili et al., 2000). Chocolate was introduced to Europe exclusively in Spain in the 16th and 17th century. The industrialization of chocolate production began in the beginning of the 20th century but even then it remained an adult luxury product, only for special occasions, celebrations or tender moments between friends (Jyothi, 2003). According to proportion of different ingredients used in preparation of chocolate, three main categories of chocolates are Dark, White and Milk chocolate. Milk powder is the major ingredient of milk chocolate and affects the sensory attributes of the chocolate and the rheological properties of chocolate fluid mass. International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 6 (2017) pp. 2881-2894 Journal homepage: http://www.ijcmas. com
Attentive Student Meets Multi-Task Teacher: Improved Knowledge Distillation for Pretrained Models

In this paper, we explore the knowledge distillation approach under the multi-task learning setting. We distill the BERT model refined by multi-task learning on seven datasets of the GLUE benchmark into a bidirectional LSTM with attention mechanism. Unlike other BERT distillation methods which specifically designed for Transformer-based architectures, we provide a general learning framework. Our approach is model agnostic and can be easily applied on different future teacher models. Compared to a strong, similarly BiLSTM-based approach, we achieve better quality under the same computational constraints. Compared to the present state of the art, we reach comparable results with much faster inference speed.
A constrained minimum criterion for variable selection.

For variable selection in the sparse linear model setting, we propose a new criterion which chooses the subset with the minimum number of variables under the constraint that the likelihood of the subset is above a certain level. When applied to the best subset selection, this criterion gives users direct control on the probability of choosing the correct subset in an asymptotic sense and a consistent estimator for the model parameter vector. It also has an interpretation as a likelihood-ratio test procedure for detecting the cardinality of the true model. When applied to the lasso, this criterion eliminates the need for selecting the lasso tuning parameter as it is automatically determined by the likelihood level in the criterion. Under mild conditions, the lasso estimator under this criterion is consistent. Simulation results show that this criterion gives the lasso better selection accuracy than the cross-validation criterion in many cases. The criterion also automatically gives the chosen model its level of significance which provides users more information for decision making.
Fully Quantizing a Simplified Transformer for End-to-end Speech Recognition

While signiﬁcant improvements have been made in recent years in terms of end-to-end automatic speech recognition (ASR) performance, such improvements were obtained through the use of very large neural networks, unﬁt for embedded use on edge devices. That being said, in this paper, we work on simplifying and compressing Transformer-based encoder-decoder architectures for the end-to-end ASR task. We empirically introduce a more compact Speech-Transformer by investigating the impact of discarding particular modules on the performance of the model. Moreover, we evaluate reducing the numerical precision of our network’s weights and activations while maintaining the performance of the full-precision model. Our experiments show that we can reduce the number of parameters of the full-precision model and then further compress the model 4x by fully quantizing to 8-bit ﬁxed point precision.
Duality between relaxation and first passage in reversible Markov dynamics: rugged energy landscapes disentangled

Relaxation and first passage processes are the pillars of kinetics in condensed matter, polymeric and single-molecule systems. Yet, an explicit connection between relaxation and first passage time-scales so far remained elusive. Here we prove a duality between them in the form of an interlacing of spectra. In the basic form the duality holds for reversible Markov processes to effectively one-dimensional targets. The exploration of a triple-well potential is analyzed to demonstrate how the duality allows for an intuitive understanding of first passage trajectories in terms of relaxational eigenmodes. More generally, we provide a comprehensive explanation of the full statistics of reactive trajectories in rugged potentials, incl. the so-called ‘few-encounter limit’. Our results are required for explaining quantitatively the occurrence of diseases triggered by protein misfolding.
Conditional Response Generation Using Variational Alignment

Generating relevant/conditioned responses in dialog is challenging, and requires not only proper modelling of context in the conversation, but also the ability to generate fluent sentences during inference. In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. Our model first learns meaningful representations of sentences, and then uses a generator to \textit{match} the query with the response distribution. Latent codes from the latter are then used to generate responses. Both quantitative and qualitative evaluations show that our model generates more fluent, relevant and diverse responses than the existing state-of-the-art methods.
The real cycle class map

The classical cycle class map for a smooth complex variety sends cycles in the Chow ring to cycles in the singular cohomology ring. We study two cycle class maps for smooth real varieties: the map from the I-cohomology ring to singular cohomology induced by the signature, and a new cycle class map defined on the Chow-Witt ring. For both maps, we establish basic compatibility results like compatibility with pullbacks, pushforwards and cup products. As a first application of these general results, we show that both cycle class maps are isomorphisms for cellular varieties.
First passage leapovers of Lévy flights and the proper formulation of absorbing boundary conditions

An important open problem in the theory of Lévy flights concerns the analytically tractable formulation of absorbing boundary conditions. Although the nonlocal approach, where the dynamical operators are given restrictions on their domain properties, has yielded substantial insights regarding the statistics of first passage, the resultant modifications to the dynamical equations hinder the detailed analysis possible in the absence of these nonlocal restrictions. In this study it is demonstrated that using the first-hit distribution, related to the first passage leapover, as the absorbing sink preserves the tractability of the dynamical equations for a particle undergoing Lévy flight. In particular, knowledge of the first-hit distribution is sufficient to fully determine the first passage time and position density of the particle, without requiring operator domain restrictions or numerical simulations. In addition, we report on the first-hit and leapover properties of first passages and arrivals for Lévy flights of arbitrary skew parameter, and extend these results to Lévy flights in a certain ubiquitous class of potentials satisfying an integral condition.
Relativistic Jets in Gamma-Ray-Emitting Narrow-Line Seyfert 1 Galaxies

Before the launch of the Fermi Gamma-ray Space Telescope satellite only two classes of active galactic nuclei (AGN) were known to generate relativistic jets and thus to emit up to the γ -ray energy range: blazars and radio galaxies, both hosted in giant elliptical galaxies. The discovery by the Large Area Telescope (LAT) on-board the Fermi satellite of variable γ -ray emission from a few radio-loud narrow-line Seyfert 1 galaxies (NLSy1) revealed the presence of an emerging third class of AGN with powerful relativistic jets. Considering that NLSy1 are usually hosted in late-type galaxies with relatively small black hole masses, this finding opened new challenging questions about the nature of these objects, the disc/jet connection, the emission mechanisms at high energies, and the formation of relativistic jets. In this review, I will discuss the broad-band properties of the γ -ray-emitting NLSy1 included in the Fourth Fermi LAT source catalog, highlighting major findings and open questions regarding jet physics, black hole mass estimation, host galaxy and accretion process of these sources in the Fermi era.
The effect of target language proficiency on deception detection among undergraduate students

I examined the impact of targets’ language proficiency on observers’ lie detection accuracy, discrimination, bias, and confidence. Observers (N = 132) were randomly assigned to make deception judgments about targets (N = 56) from four proficiency groups (i.e., native, advanced, intermediate, and beginner English speakers). Overall, observers’ accuracy differed based on targets’ level of proficiency. Specifically, accuracy and discrimination were poorest when observers judged beginner English speakers compared to targets from any other proficiency group. Moreover, observers exhibited a truth bias only when they judged native English speakers. They were also more confident when detecting targets who were lie-tellers than truth-tellers. Implications and directions for future research were discussed in light of these results.
Guidelines for measurement of thermal emission

Thermal emission is the radiation of electromagnetic waves from hot objects. The promise of thermal-emission engineering for applications in energy harvesting, radiative cooling, and thermal camouflage has recently led to renewed research interest in this topic. There is a substantial need for accurate and precise measurement of thermal emission in a laboratory setting, which can be challenging in part due to the presence of background emission from the surrounding environment and the measurement instrument itself. This is especially true for measurements of emitters at temperatures close to that of the environment, where the impact of background emission is relatively large. In this paper, we describe, recommend, and demonstrate general procedures for thermal-emission measurements that are applicable to most experimental conditions, including less-common and more-challenging cases that include thermal emitters with temperature-dependent emissivity and emitters that are not in thermal equilibrium.
On Architectures for Including Visual Information in Neural Language Models for Image Description

A neural language model can be conditioned into generating descriptions for images by providing visual information apart from the sentence prefix. This visual information can be included into the language model through different points of entry resulting in different neural architectures. We identify four main architectures which we call init-inject, pre-inject, par-inject, and merge. We analyse these four architectures and conclude that the best performing one is init-inject, which is when the visual information is injected into the initial state of the recurrent neural network. We confirm this using both automatic evaluation measures and human annotation. We then analyse how much influence the images have on each architecture. This is done by measuring how different the output probabilities of a model are when a partial sentence is combined with a completely different image from the one it is meant to be combined with. We find that init-inject tends to quickly become less influenced by the image as more words are generated. A different architecture called merge, which is when the visual information is merged with the recurrent neural network's hidden state vector prior to output, loses visual influence much more slowly, suggesting that it would work better for generating longer sentences. We also observe that the merge architecture can have its recurrent neural network pre-trained in a text-only language model (transfer learning) rather than be initialised randomly as usual. This results in even better performance than the other architectures, provided that the source language model is not too good at language modelling or it will overspecialise and be less effective at image description generation. Our work opens up new avenues of research in neural architectures, explainable AI, and transfer learning.