Miracle-dz/UniFAR-FTUs huggingface dataset:
The standard model to the Planck scale

The lack of direct evidence for physics beyond the SM at the LHC has led some to reevaluate the need for such physics to solve the hierarchy problem. Instead, the notion that the SM, or something like it, is valid up to the Planck scale and that technical naturalness is sufficient for solving the hierarchy problem has been suggested. This thesis examines minimal extensions of the SM that address its phenomenological and theoretical shortcomings while avoiding new physics between the electroweak and Planck scales that introduces a hierarchy problem. This thesis first studies two issues with the vMSM - an extension of the SM by three right-handed neutrinos - and their possible solutions. The first issue is the tension between dark matter production in the nuMSM and constraints from the Lyman-alpha forest data. To avoid this tension, the vMSM is extended by a Higgs singlet Φ and neutrino dark matter is produced through the decays of Φ rather than through left-right neutrino mixing. It is shown that the hierarchical parameters of this model can arise from symmetries broken at or near the Planck scale for two specific examples: one in which Φ stabilizes the electroweak vacuum and one in which Φ is a light inflaton. The second issue pertains to Higgs ξ-inflation. In the vMSM, a large non-minimal coupling ξ of the Higgs to gravity gives inflation but leads to a possible violation of perturbative unitarity below the inflationary scale. A study of Higgs ξ-inflation with Mh s 125-126 GeV, for which the Higgs self-coupling λ runs to small values near the Planck scale, is carried out. It is shown that small λ can significantly reduce ξ required for inflation, but ξ cannot be small enough to address the possible unitarity issue. For small λ, a new region of Higgs ξ-inflation with a large tensor-to-scalar ratio r that is consistent with BICEP2 is discovered. This thesis then studies the technical naturalness and cosmology of a model that addresses the strong CP problem. It is shown that a classically scale invariant DFSZ invisible aξon model with a Peccei-Quinn scalar S, whose couplings to the SM are ultra-weak, can solve the strong CP problem and generate electroweak symmetry breaking via the Coleman-Weinberg mechanism. The ultra-weak couplings of S are natural due to an underlying approξmate shift symmetry. The model contains a light pseudo-Goldstone dilaton that can be consistent with cosmological bounds while the aξon can be the dark matter of the universe. Finally, a summary of the thesis is presented and future research topics are suggested.
Stability Analysis of Impulsive Functional Differential Equations

This book is devoted to impulsive functional differential equations which are a natural generalization of impulsive ordinary differential equations (without delay) and of functional differential equations (without impulses). At the present time the qualitative theory of such equationsis under rapid development. After a presentation of the fundamental theory of existence, uniqueness and continuability of solutions, a systematic development of stability theory for that class of problems is given which makes the book unique. It addresses to a wide audience such as mathematicians, applied researches and practitioners.
Identifying Counterfactual Queries with the R Package cfid

In the framework of structural causal models, counterfactual queries describe events that concern multiple alternative states of the system under study. Counterfactual queries often take the form of"what if"type questions such as"would an applicant have been hired if they had over 10 years of experience, when in reality they only had 5 years of experience? "Such questions and counterfactual inference in general are crucial, for example when addressing the problem of fairness in decision-making. Because counterfactual events contain contradictory states of the world, it is impossible to conduct a randomized experiment to address them without making several restrictive assumptions. However, it is sometimes possible to identify such queries from observational and experimental data by representing the system under study as a causal model, and the available data as symbolic probability distributions. Shpitser and Pearl (2007) constructed two algorithms, called ID* and IDC*, for identifying counterfactual queries and conditional counterfactual queries, respectively. These two algorithms are analogous to the ID and IDC algorithms by Shpitser and Pearl (2006) for identification of interventional distributions, which were implemented in R by Tikka and Karvanen (2017) in the causaleffect package. We present the R package cfid that implements the ID* and IDC* algorithms. Identification of counterfactual queries and the features of cfid are demonstrated via examples.
Pronunciation Generation for Foreign Language Words in Intra-Sentential Code-Switching Speech Recognition

Code-Switching refers to the phenomenon of switching languages within a sentence or discourse. However, limited code-switching , different language phoneme-sets and high rebuilding costs throw a challenge to make the specialized acoustic model for code-switching speech recognition. In this paper, we make use of limited code-switching data as driving materials and explore a shortcut to quickly develop intra-sentential code-switching recognition skill on the commissioned native language acoustic model, where we propose a data-driven method to make the seed lexicon which is used to train grapheme-to-phoneme model to predict mapping pronunciations for foreign language word in code-switching sentences. The core work of the data-driven technology in this paper consists of a phonetic decoding method and different selection methods. And for imbalanced word-level driving materials problem, we have an internal assistance inspiration that learning the good pronunciation rules in the words that possess sufficient materials using the grapheme-to-phoneme model to help the scarce. Our experiments show that the Mixed Error Rate in intra-sentential Chinese-English code-switching recognition reduced from 29.15\%, acquired on the pure Chinese recognizer, to 12.13\% by adding foreign language words' pronunciation through our data-driven approach, and finally get the best result 11.14\% with the combination of different selection methods and internal assistance tactic.
Causal Analysis of Syntactic Agreement Neurons in Multilingual Language Models

Structural probing work has found evidence for latent syntactic information in pre-trained language models. However, much of this analysis has focused on monolingual models, and analyses of multilingual models have employed correlational methods that are confounded by the choice of probing tasks. In this study, we causally probe multilingual language models (XGLM and multilingual BERT) as well as monolingual BERT-based models across various languages; we do this by performing counterfactual perturbations on neuron activations and observing the effect on models’ subject-verb agreement probabilities. We observe where in the model and to what extent syntactic agreement is encoded in each language. We find significant neuron overlap across languages in autoregressive multilingual language models, but not masked language models. We also find two distinct layer-wise effect patterns and two distinct sets of neurons used for syntactic agreement, depending on whether the subject and verb are separated by other tokens. Finally, we find that behavioral analyses of language models are likely underestimating how sensitive masked language models are to syntactic information.
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference

We introduce two synthetic likelihood methods for Simulation-Based Inference (SBI), to conduct either amortized or targeted inference from experimental observations when a high-ﬁdelity simulator is available. Both methods learn a conditional energy-based model (EBM) of the likelihood using synthetic data generated by the simulator, conditioned on parameters drawn from a proposal distribution. The learned likelihood can then be combined with any prior to obtain a posterior estimate, from which samples can be drawn using MCMC. Our methods uniquely combine a ﬂexible Energy-Based Model and the minimization of a KL loss: this is in contrast to other synthetic likelihood methods, which either rely on normalizing ﬂows, or minimize score-based objectives; choices that come with known pitfalls. Our ﬁrst method, Amortized Unnormalized Neural Likelihood Estimation (AUNLE), introduces a tilting trick during training that allows to signiﬁcantly lower the computational cost of inference by enabling the use of eﬃcient MCMC techniques. Our second method, Sequential UNLE (SUNLE), employs a robust doubly intractable approach in order to re-use simulation data and improve posterior accuracy on a speciﬁc dataset. We demonstrate the properties of both methods on a range of synthetic datasets, and apply them to a neuroscience model of the pyloric network in the crab Cancer Borealis , matching the performance of other synthetic likelihood methods at a fraction of the simulation budget.
A New Task: Deriving Semantic Class Targets for the Physical Sciences

We deﬁne deriving semantic class targets as a novel multi-modal task. By doing so, we aim to improve classiﬁcation schemes in the physical sciences which can be severely abstracted and obfuscating. We address this task for upcoming radio astronomy surveys and present the derived semantic radio galaxy morphology class targets.
Learning to predict arbitrary quantum processes

We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process $\mathcal{E}$ over $n$ qubits. For a wide range of distributions $\mathcal{D}$ on arbitrary $n$-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process~$\mathcal{E}$, with a small average error over input states drawn from $\mathcal{D}$. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Numerical experiments on predicting quantum dynamics with evolution time up to $10^6$ and system size up to $50$ qubits corroborate our proof. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.
Position Tracking of a Varying Number of Sound Sources with Sliding Permutation Invariant Training

Machine-learning-based sound source localization (SSL) methods have shown strong performance in challenging acoustic scenarios. However, little work has been done on adapting such methods to track consistently multiple sources appearing and disappearing, as would occur in reality. In this paper, we present a new training strategy for deep learning SSL models with a straightforward implementation based on the mean squared error of the optimal association between estimated and reference positions in the preceding time frames. It optimizes the desired properties of a tracking system: handling a time-varying number of sources and ordering localization estimates according to their trajectories, minimizing identity switches (IDSs). Evaluation on simulated data of multiple reverberant moving sources and on two model architectures proves its effectiveness in reducing identity switches without compromising frame-wise localization accuracy.
Perverse homotopy heart and MW-modules

We compute the perverse δ-homotopy heart of the motivic stable homotopy category over a base scheme with a dimension function δ, rationally or after inverting the exponential characteristic in the equicharacteristic case. In order to do that, we define the notion of homological Milnor-Witt cycle modules and construct a homotopy-invariant RostSchmid cycle complex. Moreover, we define the category of cohomological Milnor-Witt cycle modules and show a duality result in the smooth case.
InterSim: Interactive Traffic Simulation via Explicit Relation Modeling

Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for planners in a more scalable and safe way compared to real-world road testing. Existing approaches learn an agent model from large-scale driving data to simulate realistic traffic scenarios, yet it remains an open question to produce consistent and diverse multi-agent interactive behaviors in crowded scenes. In this work, we present InterSim, an interactive traffic simulator for testing autonomous driving planners. Given a test plan trajectory from the ego agent, InterSim reasons about the interaction relations between the agents in the scene and generates realistic trajectories for each environment agent that are consistent with the relations. We train and validate our model on a large-scale interactive driving dataset. Experiment results show that InterSim achieves better simulation realism and reactivity in two simulation tasks compared to a state-of-the-art learning-based traffic simulator.
Quantifying the Loss of Acyclic Join Dependencies

Acyclic schemes posses known benefits for database design, speeding up queries, and reducing space requirements. An acyclic join dependency (AJD) is lossless with respect to a universal relation if joining the projections associated with the schema results in the original universal relation. An intuitive and standard measure of loss entailed by an AJD is the number of redundant tuples generated by the acyclic join. Recent work has shown that the loss of an AJD can also be characterized by an information-theoretic measure. Motivated by the problem of automatically fitting an acyclic schema to a universal relation, we investigate the connection between these two characterizations of loss. We first show that the loss of an AJD is captured using the notion of KL-Divergence. We then show that the KL-divergence can be used to bound the number of redundant tuples. We prove a deterministic lower bound on the percentage of redundant tuples. For an upper bound, we propose a random database model, and establish a high probability bound on the percentage of redundant tuples, which coincides with the lower bound for large databases.
Geographic Citation Gaps in NLP Research

In a fair world, people have equitable opportunities to education, to conduct scientific research, to publish, and to get credit for their work, regardless of where they live. However, it is common knowledge among researchers that a vast number of papers accepted at top NLP venues come from a handful of western countries and (lately) China; whereas, very few papers from Africa and South America get published. Similar disparities are also believed to exist for paper citation counts. In the spirit of “what we do not measure, we cannot improve”, this work asks a series of questions on the relationship between geographical location and publication success (acceptance in top NLP venues and citation impact). We first created a dataset of 70,000 papers from the ACL Anthology, extracted their meta-information, andgenerated their citation network. We then show that not only are there substantial geographical disparities in paper acceptance and citation but also that these disparities persist even when controlling for a number of variables such as venue of publication and sub-field of NLP. Further, despite some steps taken by the NLP community to improve geographical diversity, we show that the disparity in publication metrics across locations is still on an increasing trend since the early 2000s. We release our code and dataset here: https://github.com/iamjanvijay/acl-cite-net
FairCLIP: Social Bias Elimination based on Attribute Prototype Learning and Representation Neutralization

The Vision-Language Pre-training (VLP) models like CLIP have gained popularity in recent years. However, many works found that the social biases hidden in CLIP easily manifest in downstream tasks, especially in image retrieval, which can have harmful effects on human society. In this work, we propose FairCLIP to eliminate the social bias in CLIP-based image retrieval without damaging the retrieval performance achieving the compatibility between the debiasing effect and the retrieval performance. FairCLIP is divided into two steps: Attribute Prototype Learning (APL) and Representation Neutralization (RN). In the first step, we extract the concepts needed for debiasing in CLIP. We use the query with learnable word vector prefixes as the extraction structure. In the second step, we first divide the attributes into target and bias attributes. By analysis, we find that both attributes have an impact on the bias. Therefore, we try to eliminate the bias by using Re-Representation Matrix (RRM) to achieve the neutralization of the representation. We compare the debiasing effect and retrieval performance with other methods, and experiments demonstrate that FairCLIP can achieve the best compatibility. Although FairCLIP is used to eliminate bias in image retrieval, it achieves the neutralization of the representation which is common to all CLIP downstream tasks. This means that FairCLIP can be applied as a general debiasing method for other fairness issues related to CLIP.
Deterministic and stochastic sampling of two coupled Kerr parametric oscillators

The vision of building computational hardware for problem optimization has spurred large efforts in the physics community. In particular, networks of Kerr parametric oscillators (KPOs) are envisioned as simulators for finding the ground states of Ising Hamiltonians. It was shown, however, that KPO networks can feature large numbers of unexpected solutions that are difficult to sample with the existing deterministic (i.e., adiabatic) protocols. In this work, we experimentally realize a system of two classical coupled KPOs, and we find good agreement with the predicted mapping to Ising states. We then introduce a protocol based on stochastic sampling of the system, and we show how the resulting probability distribution can be used to identify the ground state of the corresponding Ising Hamiltonian. This method is akin to a Monte Carlo sampling of multiple out-of-equilibrium stationary states and is less prone to become trapped in local minima than deterministic protocols.
Callisto's Atmosphere: First Evidence for H2 and Constraints on H2O

We explore the parameter space for the contribution to Callisto's H corona observed by the Hubble Space Telescope from sublimated H2O and radiolytically produced H2 using the Direct Simulation Monte Carlo method. The spatial morphology of this corona produced via photoelectron and magnetospheric electron‐impact‐induced dissociation is described by tracking the motion of and simulating collisions between the hot H atoms and thermal molecules including a near‐surface O2 component. Our results indicate that sublimated H2O produced from the surface ice, whether assumed to be intimately mixed with or distinctly segregated from the dark nonice or ice‐poor regolith, cannot explain the observed structure of the H corona. On the other hand, a global H2 component can reproduce the observation, and is also capable of producing the enhanced electron densities observed at high altitudes by Galileo's plasma‐wave instrument, providing the first evidence of H2 in Callisto's atmosphere. The range of H2 surface densities explored, under a variety of conditions, that are consistent with these observations is ∼(0.4–1) × 108 cm−3. The simulated H2 escape rates and estimated lifetimes suggest that Callisto has a neutral H2 torus. We also place a rough upper limit on the peak H2O number density (≲108 cm−3), column density (≲1015 cm−2), and sublimation flux (≲1012 cm−2 s−1), all of which are 1–2 orders of magnitude less than that assumed in previous models. Finally, we discuss the implications of these results, as well as how they compare to Europa and Ganymede.
T-Optimality: A Stopping Rule for a First Order Algorithm

In the optimal design theory, the T-optimality criterion is useful for the discrimination between two competitive models. This criterion has an interesting statistical interpretation as the power of a test for the fit of a second model when the first one is true. Usually there is not a closed form for the T-optimum design and it must be computed through an iterative procedure. In this short note a stopping rule for a first order algorithm is provided. The proposed stopping rule has the interesting feature that the algorithm will always stop at a design which reaches a minimum fixed efficiency. In other words, the algorithm stops when it reaches a design efficiency (with respect to the unknown T-optimum design) as good as wanted.
REASONS FOR NUCLEAR FORCES IN LIGHT OF THE CONSTITUTION OF THE REAL SPACE

The concept of microstructure of the real space, con sidered as a mathematical lattice of cells (the tes sellattice), and notions of canonical particles and fields, which ar e generated by the space, are analyzed. Submicrosco pic mechanics based on this concept is discussed and employed for in-depth study of the nucleon-nucleon in teraction. It is argued that a deformation coat is developed i n the real space around the nucleon (as is the case with any other canonical particle such as electron, muon, etc.) an d that they are the deformation coats that are resp onsible for the appearance of nuclear forces. One more source of nu clear forces is associated with inerton clouds, exc itations of the space tessellattice (the excitations are a subs tructure of nucleons’ matter waves), which accompany moving nucleons as in the case of any other canonical part icles. Two nuclear systems are under consideration: the deuteron and a weight nucleus. It is shown that a weight nuc leus is a cluster of interacting protons and neutro ns. The condition of the cluster stability is obtained in t he framework of the statistical mechanical approach . On the whole, the paper proposes a radically new approach to a ve ry old unsolved problem, the origin of the nuclear forces, allowing the understanding mechanism(s) occurring at low energy nuclear transmutations.
ON THE MERGING CLUSTER ABELL 578 AND ITS CENTRAL RADIO GALAXY 4C+67.13

Here we analyze radio, optical, and X-ray data for the peculiar cluster Abell 578. This cluster is not fully relaxed and consists of two merging sub-systems. The brightest cluster galaxy (BCG), CGPG 0719.8+6704, is a pair of interacting ellipticals with projected separation ∼10 kpc, the brighter of which hosts the radio source 4C+67.13. The Fanaroff–Riley type-II radio morphology of 4C+67.13 is unusual for central radio galaxies in local Abell clusters. Our new optical spectroscopy revealed that both nuclei of the CGPG 0719.8+6704 pair are active, albeit at low accretion rates corresponding to the Eddington ratio ∼ 10 − 4 ? > (for the estimated black hole masses of ∼ 3 × 10 8 M ⊙ ? > and ∼ 10 9 M ⊙ ? > ). The gathered X-ray (Chandra) data allowed us to confirm and to quantify robustly the previously noted elongation of the gaseous atmosphere in the dominant sub-cluster, as well as a large spatial offset (∼60 kpc projected) between the position of the BCG and the cluster center inferred from the modeling of the X-ray surface brightness distribution. Detailed analysis of the brightness profiles and temperature revealed also that the cluster gas in the vicinity of 4C+67.13 is compressed (by a factor of about ∼1.4) and heated (from ≃ 2.0 ? > keV up to 2.7 keV), consistent with the presence of a weak shock (Mach number ∼1.3) driven by the expanding jet cocoon. This would then require the jet kinetic power of the order of ∼ 10 45 ? > erg s−1, implying either a very high efficiency of the jet production for the current accretion rate, or a highly modulated jet/accretion activity in the system.