Miracle-dz/UniFAR-FTUs huggingface dataset:
Dilution Priors for Model Uncertainty

In model uncertainty setups, some models may be very similar to others. For example, this would occur with the uncertainty about which of several highly correlated variables to include in a linear regression. In such setups, it is tempting to characterize ignorance by a uniform prior which assigns equal probability to each model under consideration. Unfortunately, such a prior may not be uniform on “neighborhoods of models,” especially when there are regions of high redundancy in the model space. By skewing the posterior, such priors can be problematic for MCMC sampling of the posterior for model search and for model averaging. Dilution priors rectify this problem by assigning probability more uniformly to model neighborhoods. This talk will focus on the construction of dilution priors for two setups: Bayesian variable selection for the linear model and Bayesian CART model selection. For the linear model, several potential dilution prior contructions will be presented. These include a construction based on a Voronoi tessellation of the space of all models and a construction based on metrics over the space of all models. For Bayesian CART, dilution priors are seen to arise naturally with tree-generating process priors.
The Ellipsis of "Ellipsis". A Reanalysis of "Elided" Noun Phrase Structures in German

This thesis deals with the explanation of so-called “NP-ellipsis” (NPE) in German – a linguistic phenomenon that can be regarded as the omission of the noun in (full) DPs; the remnant (usually a determiner or an adjective) has to show strong morphological agreement by way of an agreement suffix in order to license the elided noun. There are two strategies that seek to explain NPE, as reported in the literature: elision (which can be dependent on the presence of strong inflectional morphology/agreement (on the Dor adjectival remnant), Focus, or the presence of a so-called “classifier phrase”) and pronominalization (used when there is no inflectional morphology on the adjectival remnant). The aim of the thesis is to show that these NPE strategies work well for some languages, but not for German (they thus seem to be too language-specific by nature). Therefore, it is proposed that NPE in German should be captured in another way, i.e., by analyzing NPE constructions as DPs and APs without an NP-complement projection (which, in previous accounts, is incorrectly filled by a null noun (“eN”) or an agreement suffix that acts as a replacement for the “elided” noun). It will also be shown that the new proposal extends to Dutch, English and Frisian as well: an outcome that would point in the direction of a truly cross-linguistic account of the phenomenon in question.
A Case of a 23-Year-Old Male With Leber Hereditary Optic Neuropathy With a Rare Mutation

Mitochondrial DNA (mtDNA) is responsible for encoding 13 subunits of the respiratory chain. These subunits are crucial in providing reducing equivalents for the energy-intensive intracellular processes. Leber hereditary optic neuropathy (LHON) is a mitochondrial illness that causes carcinogenesis due to oxidative stress and painless loss of central vision as a result of selective degradation of retinal ganglion cells as well as their axons. We present a case of a 23-year-old male patient who was diagnosed with subacute LHON. The mutation in our patient was found in a less commonly mutated exon sequence of MT-NDL4, which codes for NADH (nicotinamide adenine dinucleotide hydrogen, reduced) dehydrogenase subunit 4L. The MT-ND4L exon is located immediately upstream of the MTD4 exon on the human mtDNA. The take-home message is to always perform a comprehensive mitochondrial genome analysis for identifying rare mutations when LHON is suspected.
Graph-Based Conversation Analysis in Social Media

Social media platforms offer their audience the possibility to reply to posts through comments and reactions. This allows social media users to express their ideas and opinions on shared content, thus opening virtual discussions. Most studies on social networks have focused only on user relationships or on the shared content, while ignoring the valuable information hidden in the digital conversations, in terms of structure of the discussion and relation between contents, which is essential for understanding online communication behavior. This work proposes a graph-based framework to assess the shape and structure of online conversations. The analysis was composed of two main stages: intent analysis and network generation. Users’ intention was detected using keyword-based classification, followed by the implementation of machine learning-based classification algorithms for uncategorized comments. Afterwards, human-in-the-loop was involved in improving the keyword-based classification. To extract essential information on social media communication patterns among the users, we built conversation graphs using a directed multigraph network and we show our model at work in two real-life experiments. The first experiment used data from a real social media challenge and it was able to categorize 90% of comments with 98% accuracy. The second experiment focused on COVID vaccine-related discussions in online forums and investigated the stance and sentiment to understand how the comments are affected by their parent discussion. Finally, the most popular online discussion patterns were mined and interpreted. We see that the dynamics obtained from conversation graphs are similar to traditional communication activities.
Immunological Insights into Cigarette Smoking-Induced Cardiovascular Disease Risk

Smoking is one of the most prominent addictions of the modern world, and one of the leading preventable causes of death worldwide. Although the number of tobacco smokers is believed to be at a historic low, electronic cigarette use has been on a dramatic rise over the past decades. Used as a replacement for cigarette smoking, electronic cigarettes were thought to reduce the negative effects of burning tobacco. Nonetheless, the delivery of nicotine by electronic cigarettes, the most prominent component of cigarette smoke (CS) is still delivering the same negative outcomes, albeit to a lesser extent than CS. Smoking has been shown to affect both the structural and functional aspects of major organs, including the lungs and vasculature. Although the deleterious effects of smoking on these organs individually is well-known, it is likely that the adverse effects of smoking on these organs will have long-lasting effects on the cardiovascular system. In addition, smoking has been shown to play an independent role in the homeostasis of the immune system, leading to major sequela. Both the adaptive and the innate immune system have been explored regarding CS and have been demonstrated to be altered in a way that promotes inflammatory signals, leading to an increase in autoimmune diseases, inflammatory diseases, and cancer. Although the mechanism of action of CS has not been fully understood, disease pathways have been explored in both branches of the immune system. The pathophysiologically altered immune system during smoking and its correlation with cardiovascular diseases is not fully understood. Here we highlight some of the important pathological mechanisms that involve cigarette smoking and its many components on cardiovascular disease and the immune systems in order to have a better understanding of the mechanisms at play.
The Quiver Matrix Model and 2d-4d Conformal Connection

We review the quiver matrix model (the ITEP model) in the light of the recent progress on 2d-4d connection of conformal field theories, in particular, on the relation between Toda field theories and a class of quiver superconformal gauge theories. On the basis of the CFT representation of the beta deformation of the model, a quantum spectral curve is introduced as >=0 at finite N and for beta \neq 1. The planar loop equation in the large N limit follows with the aid of W_n constraints. Residue analysis is provided both for the curve of the matrix model with the "multi-log" potential and for the Seiberg-Witten curve in the case of SU(n) with 2n flavors, leading to the matching of the mass parameters. The isomorphism of the two curves is made manifest.
Resistivity plateau and extremely large magnetoresistance in NbAs 2 and TaAs 2

In topological insulators (TIs), metallic surface conductance saturates the insulating bulk resistance with de- creasing temperature, resulting in resistivity plateau at low temperatures as a transport signature originating from metallic surface modes protected by time reversal symmetry (TRS). Such characteristic has been found in several materials including Bi2Te2Se, SmB6 etc. Recently, similar behavior has been observed in metallic com- pound LaSb, accompanying an extremely large magetoresistance (XMR). Shubnikov-de Hass (SdH) oscillation at low temperatures further confirms the metallic behavior of plateau region under magnetic fields. LaSb[1] has been proposed by the authors as a possible topological semimetal (TSM), while negative magnetoresistance is absent at this moment. Here, high quality single crystals of NbAs2/TaAs2 with inversion symmetry have been grown and the resistivity under magnetic field is systematically investigated. Both of them exhibit metallic behavior under zero magnetic field, and a metal-to-insulator transition occurs when a nonzero magnetic field is applied, resulting in XMR (1.0*105% for NbAs2 and 7.3*105% for TaAs2 at 2.5 K & 14 T). With tempera- ture decreased, a resistivity plateau emerges after the insulator-like regime and SdH oscillation has also been observed in NbAs2 and TaAs2.
On the varieties of representations and characters of a family of one-relator subgroups. Their irreducible components

Let us consider the group $G = $ with $m$ and $n$ nonzero integers. In this paper, we study the variety of epresentations $R(G)$ and the character variety $X(G)$ in $SL(2,\C)$ of the group $G$,obtaining by elementary methods an explicit primary decomposition of the ideal corresponding to $X(G)$ in the coordinates $X=t_x$, $Y=t_y$ and $Z=t_{xy}$. As an easy consequence, a formula for computing the number of irreducible components of $X(G)$ as a function of $m$ and $n$ is given. We provide a combinatorial description of $X(G)$ and we prove that in most cases it is possible to recover $(m,n)$ from the combinatorial structure of $X(G)$. Finally we compute the number of irreducible components of $R(G)$ and study the behavior of the projection $t:R(G)\longrightarrow X(G)$.
Single-Cell Analysis to Better Understand the Mechanisms Involved in MS

Multiple sclerosis is a chronic and inflammatory disease of the central nervous system. Although this disease is widely studied, many of the precise mechanisms involved are still not well known. Numerous studies currently focusing on multiple sclerosis highlight the involvement of many major immune cell subsets, such as CD4+ T cells, CD8+ T cells and more recently B cells. However, our vision of its pathology has remained too broad to allow the proper use of targeted therapeutics. This past decade, new technologies have emerged, enabling deeper research into the different cell subsets at the single-cell level both in the periphery and in the central nervous system. These technologies could allow us to identify new cell populations involved in the disease process and new therapeutic targets. In this review, we briefly introduce the major single-cell technologies currently used in studies before diving into the major findings from the multiple sclerosis research from the past 5 years. We focus on results that were obtained using single-cell technologies to study immune cells and cells from the central nervous system.
Modelling and Simulation of Quadrotor Using Model-Based Design

In the recent years, the control of Unmanned Aerial Vehicles (UAV) has become one of the most interesting fields of research, especially for quadrotor aircraft due to the simple structure, superior maneuverability and low cost. This paper presents a Model-Based approach to develop the mathematical model of quadrotor and establish a control method to meet the target of the actual flight. It employs Simulink to design the Proportional Integral Derivative (PID) controller for altitude and attitude loop of quadrotor, and uses Stateflow to implement control logic method for different flight conditions and states.
THE COSMIC RATE, LUMINOSITY FUNCTION, AND INTRINSIC CORRELATIONS OF LONG GAMMA-RAY BURSTS

We calculate durations and spectral parameters for 207 Swift bursts detected by the Burst Alert Telescope from 2007 April to 2009 August, including 67 events with measured redshifts. This is the first supplement to our catalog of 425 Swift gamma-ray bursts (GRBs; 147 with redshifts) starting from GRB 041220. This complete and extensive data set, analyzed with a unified methodology, allows us to conduct an accurate census of intrinsic GRB energetics, hardnesses, durations, and redshifts. The GRB world model we derive reproduces well the observables from both Swift and pre-Swift satellites. Comparing to the cosmic star formation rate, we estimate that only about 0.1% of massive stars explode as bright GRBs. There is strong evidence for evolution in the Swift population at intermediate and high-z, and we can rule out (at the 5σ level) that this is due to evolution in the luminosity function of GRBs. Instead, the Swift sample suggests a modest propensity for low metallicity, evidenced by an increase in the rate density with redshift. Treating the multivariate data and selection effects rigorously, we find a real, intrinsic correlation between Eiso and Epk (and possibly also Tr45,z); however, the correlation is not a narrow log–log relation and its observed appearance is strongly detector-dependent. We also estimate the high-z rate (3%–9% of GRBs at z beyond 5) and discuss the extent of a large missing population of low-Epk,obs X-ray flashes as well as a potentially large missing population of short-duration GRBs that will be probed by EXIST.
Construction of a Vero Cell Line Expressing Human ICAM1 for the Development of Rhinovirus Vaccines

Human rhinoviruses (HRVs) are small non-enveloped RNA viruses that belong to the Enterovirus genus within the Picornaviridae family and are known for causing the common cold. Though symptoms are generally mild in healthy individuals, the economic burden associated with HRV infection is significant. A vaccine could prevent disease. The Vero-cell-based viral vaccine platform technology was considered for such vaccine development. Unfortunately, most HRV strains are unable to propagate on Vero cells due to a lack of the major receptor of HRV group A and B, intercellular adhesion molecule (ICAM1, also known as CD54). Therefore, stable human ICAM1 expressing Vero cell clones were generated by transfecting the ICAM1 gene in Vero cells and selecting clones that overexpressed ICAM1 on the cell surface. Cell banks were made and expression of ICAM1 was stable for at least 30 passages. The Vero_ICAM1 cells and parental Vero cells were infected with four HRV prototypes, B14, A16, B37 and A57. Replication of all four viruses was detected in Vero_ICAM1, but not in the parental Vero cells. Altogether, Vero cells expressing ICAM1 could efficiently propagate the tested HRV strains. Therefore, ICAM1-expressing cells could be a useful tool for the development and future production of polyvalent HRV vaccines or other viruses that use ICAM1 as a receptor.
A large sample of photometric rotation periods for FGK Pleiades stars

Using data from the Hungarian-made Automated Telescope Network (HATNet) survey for transiting exoplanets, we measure photometric rotation periods for 368 Pleiades stars with 0.4 ≲ M ≲ 1.3 M ⊙ . We detect periodic variability for 74 per cent of the cluster members in this mass range that are within our field-of-view, and 93 per cent of the members with 0.7 ≲ M ≲ 1.0 M ⊙ . This increases, by a factor of 5, the number of Pleiades members with measured periods. We compare these data to the rich sample of spectroscopically determined projected equatorial rotation velocities (v sin i) available in the literature for this cluster. Included in our sample are 14 newly identified probable cluster members which have proper motions, photometry and rotation periods consistent with membership. For stars with M ≳ 0.85 M ⊙ the rotation periods, v sin i and radius estimates are consistent with the stars having an isotropic distribution of rotation axes, if a moderate differential rotation law is assumed. For stars with M ≲ 0.85 M ⊙ , the inferred sin i values are systematically larger than 1.0. These observations imply that the combination of measured parameters P(v sin i)/R is too large by ∼24 per cent for low-mass stars in this cluster. By comparing our new mass-period relation for the Pleiades to the slightly older cluster M35, we confirm previous indications that the spin-down stalls at ∼100 Myr for the slowest rotating stars with 0.7 ≲ M ≲ 1.1 M ⊙ ― a fact which may indicate that the internal transport of angular momentum is inefficient in slowly rotating solar-mass stars.
Retrieval of atmospheric and ocean surface parameters from ADEOS‐II Advanced Microwave Scanning Radiometer (AMSR) data: Comparison of errors of global and regional algorithms

Retrieval of the sea surface temperature ts, wind speed W, total atmospheric water vapor content V, and total cloud liquid water content Q over the ocean from the simulated ADEOS‐II AMSR data in the absence of precipitation is considered. The brightness temperatures (TB) with the vertical (V) and horizontal (H) polarizations at the AMSR frequencies ν = 6.9, 10.7, 18.7, 23.8, and 36.5 GHz were computed for the radiosonde and relevant data collected by research vessels. V and Q were retrieved from TB(23.8V) and TB(36.5V) with the physically based “global” (−1.6 < ts ≤ 31°C), “polar” (ts ≤ 15°C), and “tropical” (ts ≥ 24°C) algorithms under the assumption that ts values were derived from the measurements at 6.9 and 10.7 GHz with an error σts. The errors σV and σQ were estimated at several combinations of the difference ΔT36 = TB(36.5V) ‐ TB(36.5H), radiometer noises ΔT and σts. At ΔT36 = 35 K, ΔT = 0.3 K, and σts = 1°C, σV = 1.5 kg/m2 and σQ = 0.029 kg/m2 for a global algorithm. Standard regression techniques were applied to retrieve ts and W from the simulated brightness temperatures for the cases TB(10.7V) ≤ 185 K. For a global data base, three‐channel algorithm (6.9V, 6.9H, 10.7V or 10.7V) yields the ts and W errors equal to 0.40 and 0.58°C and 0.66 and 0.85 m/s as radiometer noises increase from 0.1 to 0.2 K at ν = 6.9 GHz and from 0.13 to 0.27 K at ν = 10.7 GHz.
Convolutional Neural Network Based Two-Layer Transfer Learning for Bearing Fault Diagnosis

Rolling bearing fault diagnosis is one of crucial tasks in mechanical equipment fault diagnosis. Currently, artificial intelligence and machine learning-driven fault diagnosis methods are extensively utilized for rolling bearing. When compared to traditional techniques, the diagnostic accuracy has significantly improved. These methods, however, need a substantial amount of labelled training data, which is difficult to obtain in actual failures. In order to resolve this problem, Transfer Learning (TL) was created to learn in the target domain by accessing knowledge from the pertinent labelled source domain. Inspired by Maximum Mean Discrepancy, this paper puts forward a Convolutional Neural Network (CNN) based Two-layer Transfer Learning (CTTL) method for fault diagnosis. In the first layer, the fault features are automatically extracted by CNN and a term called Feature Weighted Maximum Mean Discrepancy (WMMD) is considered to minimise the difference between source and target domains. In the second layer, the Third Dataset, which is based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method was designed on the basis of the predicted labels from the first layer. The Calinski-Harabaz (CH) index of the Target Dataset controls the iteration times of CTTL. CTTL change the process of Transfer Learning method from learning the distribution of domains to learning the distribution of fault types in more detail, which will get higher accuracy. Proposed CTTL is tested by the bearing datasets of Case Western Reserve University (CWRU) and XJTU-SY. The experimental findings reveal that CTTL is capable of achieving a high diagnosis accuracy across different load domains. In the majority of experiments, CTTL outperformed other algorithms, including Deep Neural Network, Support Vector Machine, and several other methods.
Physical Layer Anonymous Precoding Design: From the Perspective of Anonymity Entropy

In the era of e-Health, privacy protection has become imperative in applications that carry personal and sensitive data. Departing from the data-perturbation based privacy-preserving techniques that reduce the fidelity of the disclosed data, in this paper we investigate anonymous communications, which mask the identity of the data sender while providing high data reliability. Focusing on the physical (PHY) layer, we first explore the break of privacy through a statistical attribute based sender detection (SD) from the receiver. Compared to the existing literature, this enables a much enhanced SD performance, especially when the users are equipped with different numbers of antennas. To counteract the advanced SD approach above, we formulate explicit anonymity constraints for the design of the anonymous precoder, which mask the sender’s PHY attributes that can be exploited by SD, while at the same time preserving the reliability of the data. Then, anonymity entropy-oriented precoders are proposed for different antenna configurations at the users, which adaptively construct a maximum number of aliases while obeying users’ signal-to-noise-ratio requirements for data accuracy. Simulation results demonstrate that the proposed anonymous precoders provide the highest level of anonymity entropy over the benchmarks, while achieving reasonable symbol error rate for the communication signal.
Bayesian Variable Selection for Fractional Factorial Experiments with Multilevel Categorical Factors

Fractional factorial experiments are an exceedingly important tool to the scientist or engineer working in virtually any sector of industry. As was first demonstrated by Box and Meyer (1993), what has become known as the Bayesian variable-selection approach is a useful addition to the traditional analysis techniques, due to its ability to identify “difficult-to-spot” interaction effects in experiments with complex aliasing. However, the published experiments on which this approach has been applied consist either of two-level factors or factors with quantitative levels. A multilevel categorical factor differs in that its effect must be represented by more than one parameter in the model, these being the regression coefficients associated with a set of dummy variables. Regardless of how the dummy variables are selected, it is not reasonable to assume that these parameters are exchangeable either among themselves or between parameters representing the effect of other factors. Hence, the priors proposed to date cannot be used. We present a simple multivariate normal prior for this effect that is a natural extension of the prior used by previous authors for two-level factors and demonstrate its effectiveness by reanalyzing two multilevel factorial experiments with very complex aliasing.