Miracle-dz/UniFAR-FTUs huggingface dataset:
Message Transmission and Common Randomness Generation Over MIMO Slow Fading Channels With Arbitrary Channel State Distribution

We investigate the problem of message transmission and the problem of common randomness (CR) generation over single-user multiple-input multiple-output (MIMO) slow fading channels with average input power constraint, additive white Gaussian noise (AWGN), arbitrary state distribution and with complete channel state information available at the receiver side (CSIR). We derive a lower and an upper bound on the outage transmission capacity of MIMO slow fading channels for arbitrary state distribution and show that the bounds coincide except possibly at points of discontinuity of the outage transmission capacity, of which there are, at most, countably many. Such discontinuity issues might occur because the channel state distribution is arbitrary. We also establish the capacity of a specific compound MIMO Gaussian channel in order to prove the lower bound on the outage transmission capacity. Furthermore, we define the outage CR capacity for a two-source model with unidirectional communication over a MIMO slow fading channel with arbitrary state distribution and establish a lower and an upper bound on it using our bounds on the outage transmission capacity of the MIMO slow fading channel.
Integrated Brier Score based Survival Cobra - A regression based approach

Recently Goswami et al. \cite{goswami2022concordance} introduced two novel implementations of combined regression strategy to find the conditional survival function. The paper uses regression-based weak learners and provides an alternative version of the combined regression strategy (COBRA) ensemble using the Integrated Brier Score to predict conditional survival function. We create a novel predictor based on a weighted version of all machine predictions taking weights as a specific function of normalized Integrated Brier Score. We use two different norms (Frobenius and Sup norm) to extract the proximity points in the algorithm. Our implementations consider right-censored data too. We illustrate the proposed algorithms through some real-life data analysis.
Spectral Probing

Linguistic information is encoded at varying timescales (subwords, phrases, etc.) and communicative levels, such as syntax and semantics. Contextualized embeddings have analogously been found to capture these phenomena at distinctive layers and frequencies. Leveraging these findings, we develop a fully learnable frequency filter to identify spectral profiles for any given task. It enables vastly more granular analyses than prior handcrafted filters, and improves on efficiency. After demonstrating the informativeness of spectral probing over manual filters in a monolingual setting, we investigate its multilingual characteristics across seven diverse NLP tasks in six languages. Our analyses identify distinctive spectral profiles which quantify cross-task similarity in a linguistically intuitive manner, while remaining consistent across languages—highlighting their potential as robust, lightweight task descriptors.
Reusing Keywords for Fine-grained Representations and Matchings

Question retrieval aims to find the semantically equivalent questions for a new question, suffering from a key challenge— lexical gap. Previous solutions mainly focus on the translation model, topic model and deep learning techniques. Distinct from the previous solutions, we propose new insights of reusing important keywords to construct fine-grained semantic representations of questions and then fine-grained matchings for estimating the semantic similarity of two questions. Accordingly, we design a fine-grained matching network by reusing the important keywords. In the network, two cascaded units are proposed: (i) fine-grained representation unit, which uses multi-level keyword sets to represent question semantics of different granularity; (ii) fine-grained matching unit, which first generates multiple comparable representation pairs for two questions, i.e., keyword set pairs, and then matches the two questions from multiple granularities and multiple views by using the comparable representation pairs, i.e., from global matching to local matching and from lexical matching to semantic matching. To get the multi-level keyword sets of a question, we propose a cross-task weakly supervised extraction model that applies question-question labeled signals from the training set of question retrieval to super-vise the keyword extraction process. To construct the comparable keyword set pairs, we design a pattern-based assignment method to construct the comparable keyword set pairs from the multi-level keyword sets of two questions. We conduct extensive experiments on three public datasets and the experimental results show that our proposed model outperforms the state-of-the-art solutions.
IEAD: A Novel One-Line Interface to Query Astronomical Science Archives

In this article I present IEAD, a new interface for astronomical science databases. It is based on a powerful, yet simple, syntax designed to completely abstract the user from the structure of the underlying database. The programming language chosen for its implementation, JavaScript, makes it possible to interact directly with the user and to provide real-time information on the parsing process, error messages, and name resolution of targets; additionally, the same parsing engine is used for context-sensitive autocompletion. Ultimately, this product should significantly simplify the use of astronomical archives, inspire more advanced uses of them, and allow the user to focus on what scientific research to perform, instead of on how to instruct the computer to do it.
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning

Commonsense generation aims to generate a realistic sentence describing a daily scene under the given concepts, which is very challenging, since it requires models to have relational reasoning and compositional generalization capabilities. Previous work focuses on retrieving prototype sentences for the provided concepts to assist generation. They first use a sparse retriever to retrieve candidate sentences, then re-rank the candidates with a ranker. However, the candidates returned by their ranker may not be the most relevant sentences, since the ranker treats all candidates equally without considering their relevance to the reference sentences of the given concepts. Another problem is that re-ranking is very expensive, but only using retrievers will seriously degrade the performance of their generation models. To solve these problems, we propose the metric distillation rule to distill knowledge from the metric (e.g., BLEU) to the ranker. We further transfer the critical knowledge summarized by the distilled ranker to the retriever. In this way, the relevance scores of candidate sentences predicted by the ranker and retriever will be more consistent with their quality measured by the metric. Experimental results on the CommonGen benchmark verify the effectiveness of our proposed method: (1) Our generation model with the distilled ranker achieves a new state-of-the-art result. (2) Our generation model with the distilled retriever even surpasses the previous SOTA.
Explainability in autonomous pedagogically structured scenarios

We present the notion of explainability for decision-making processes in a pedagogically structured autonomous environment. Multi-agent systems that are structured pedagogically consist of pedagogical teachers and learners that operate in environments in which both are sometimes not fully aware of all the states in the environment and beliefs of other agents thus making it challenging to explain their decisions and actions with one another. This work emphasises the need for robust and iterative explanation-based communication between the pedagogical teacher and the learner. Explaining the rationale behind multi-agent decisions in an interactive, partially observable environment is necessary to build trustworthy and reliable communication between pedagogical teachers and learners. Ongoing research is primarily focused on explanations of the agents' behaviour towards humans, and there is a lack of research on inter-agent explainability.
Overexposure Mask Fusion: Generalizable Reverse ISP Multi-Step Refinement

With the advent of deep learning methods replacing the ISP in transforming sensor RAW readings into RGB images, numerous methodologies solidified into real-life applications. Equally potent is the task of inverting this process which will have applications in enhancing computational photography tasks that are conducted in the RAW domain, addressing lack of available RAW data while reaping from the benefits of performing tasks directly on sensor readings. This paper's proposed methodology is a state-of-the-art solution to the task of RAW reconstruction, and the multi-step refinement process integrating an overexposure mask is novel in three ways: instead of from RGB to bayer, the pipeline trains from RGB to demosaiced RAW allowing use of perceptual loss functions; the multi-step processes has greatly enhanced the performance of the baseline U-Net from start to end; the pipeline is a generalizable process of refinement that can enhance other high performance methodologies that support end-to-end learning.
On the Correspondence between Subshifts of Finite Type and Statistical Mechanics Models

Several classical problems in symbolic dynamics concern the characterization of the simplex of measures of maximal entropy. For subshifts of finite type in higher dimensions, methods of statistical mechanics are ideal for dealing with these problems. R. Burton and J. Steif developed a strategy to construct examples of strongly irreducible subshifts of finite type admitting several measures of maximal entropy. This strategy exploits a correspondence between equilibrium statistical mechanics and symbolic dynamics—a correspondence which was later formalized by O. Häggström. In this paper, we revisit and discuss this correspondence with the aim of presenting a simplified version of it and present some applications of rigorous results concerning the Potts model and the six-vertex model to symbolic dynamics, illustrating in this way the possibilities of this correspondence.
Efficient Shape Retrieval Using Elliptical Shape Coding

This paper presents a novel shape representation, called elliptical shape coding , and uses it to develop an efficient shape retrieval algorithm. The elliptical shape coding transforms an original shape, which is a single, closed contour in our experiments, into a periodic signal. The alignment between two shapes is accomplished efficiently by performing convolution of the corresponding periodic signals. Unlike previous approaches, we taking advantage of a priori knowledge hidden in the shape structure to produce shape matches with a significantly lower computational cost. Experimental results on an MPEG-7 shape database demonstrate the effectiveness of the proposed method.
Evaluation of the Accuracy of the Dark Frame Subtraction Method in CCD Image Processing

Abstract : This document evaluates the reliability of the dark-frame subtraction method for the detection of very dim targets in astronomical images. This method is frequently used for removing the image background gradient (a thermal artefact) in CCD images. This report demonstrates that this method may not be suitable for the detection of objects with very low signal-to-noise ratio. The analysis of two series of 500 dark frames, acquired at two different CCD temperatures, showed that dark frames are not reproducible with enough accuracy. The subtraction of two dark frames, assumed to be acquired at the same temperature, always leaves a residual background comparable or superior to the noise level. It is suspected that the temperature recorded into the image header is the cryo-cooler temperature and not directly the CCD temperature. The temperature oscillates and there is always a small temperature difference between the CCD ship and the cryo-cooler. However, it was found that the image mean intensity is tightly linked to the background gradient amplitude in each dark frame. The subtraction of dark frames with equal mean intensity, instead of equal recorded temperature, gives good results. Unfortunately, it is not obvious to evaluate the mean background intensity when the image contains signals, while the CCD temperature is always available in the image header. In the case where very faint objects have to be detected, the simple dark frame subtraction method should be replaced by more reliable algorithms (but generally longer to compute) able to separate the signal from the image background.
Non-parametric model calibration in finance

Consistently fitting vanilla option surfaces is an important issue when it comes to modelling in finance. In three different models: local and stochastic volatility, local correlation and hybrid local volatility with stochstic rates, this calibration boils down to the resolution of a nonlinear partial integro-differential equation. In a first part, we give existence results of solutions for the calibration equation. They are based upon fixed point methods in Holder spaces and short-time a priori estimates. We then apply those existence results to the three models previously mentioned and give the calibration obtained when solving the pde numerically. At last, we focus on the algorithm used for the resolution: an ADI predictor/corrector scheme that needs to be modified to take into account the nonlinear term. We also study an instability phenomenon that occurs in certain cases for the local and stochastic volatility model. Using Hadamard’s theory, we try to offer a theoretical explanation to the instability
Effects of 2,5-Dimethoxy-4-Methylamphetamine (DOM) and 2-Piperazin-1-yl-Quinoline (Quipazine) on Fentanyl Versus Food Choice in Rhesus Monkeys

There has been increasing interest in the potential therapeutic effects of drugs with agonist properties at serotonin 2A subtype (5-HT2A) receptors (e.g., psychedelics), including treatment of substance use disorders. Studying interactions between 5-HT2A receptor agonists and other drugs is important for understanding potential therapeutic effects as well as adverse interactions. Direct-acting 5-HT2A receptor agonists such as 2,5-dimethoxy-4-methylamphetamine (DOM) and 2-piperazin-1-yl-quinoline (quipazine) enhance some (e.g., antinociceptive) effects of opioids; however, it is unclear whether they alter the abuse-related effects of opioids. This study examined whether DOM and quipazine alter the reinforcing effects of fentanyl in rhesus monkeys (n = 6) responding under a food versus drug choice procedure. Responding on one lever delivered sucrose pellets and responding on the other lever delivered intravenous (i.v.) infusions. In one set of experiments, fentanyl (0.1–3.2 µg/kg/infusion) versus food choice sessions were preceded by noncontingent i.v. pretreatments with DOM (0032–0.32 mg/kg), quipazine (0.32–1.0 mg/kg), naltrexone (0.032 mg/kg), or heroin (0.1 mg/kg). In another set of experiments, fentanyl was available during choice sessions in combination with DOM (0.32–100 µg/kg/infusion) or quipazine (3.2–320 µg/kg/infusion) in varying dose ratios. Naltrexone decreased and heroin increased fentanyl choice, demonstrating sensitivity of responding to pharmacological manipulation. However, whether given as a pretreatment or made available in combination with fentanyl as a mixture, neither DOM nor quipazine significantly altered fentanyl choice. These results suggest that 5-HT2A receptor agonists do not enhance the reinforcing effects of opioids and, thus, will not likely enhance abuse potential. SIGNIFICANCE STATEMENT Serotonin 2A subtype receptor agonists enhance some (e.g., antinociceptive) effects of opioids, suggesting they could be combined with opioids in some therapeutic contexts such as treating pain. However, it is unclear whether they also enhance adverse effects of opioids, including abuse. Results of this study indicate that serotonin 2A subtype receptor agonists do not reliably enhance opioid self-administration and, thus, are unlikely to enhance the abuse potential of opioids.
Inner disc obscuration in GRS 1915+105 based on relativistic slim disc model

We study the observational signatures of the relativistic slim disc of 10 M_sun black hole, in a wide range of mass accretion rate, mdot, dimensionless spin parameter, a_ast, and viewing angle, i. In general, the innermost temperature, T_in increases with the increase of i for a fixed value of mdot and a_ast, due to the Doppler effect. However, for i > 50 and mdot > mdot_turn, T_in starts to decrease with the increase of mdot. This is a result of self-obscuration -- the radiation from the innermost hot part of the disc is blocked by the surrounding cooler part. The value of mdot_turn and the corresponding luminosities depend on a_ast and i. Such obscuration effects cause an interesting behavior on the disc luminosity (L_disc) -- T_in plane for high inclinations. In addition to the standard-disc branch which appears below mdot_turn and which obeys L_disc propto T_in^4 -relation, another branch above mdot_turn, which is nearly horizontal, may be observed at luminosities close to the Eddington luminosity. We show that these features are likely observed in a Galactic X-ray source, GRS 1915+105. We support a high spin parameter (a_ast > 0.9) for GRS 1915+105 since otherwise the high value of T_in and small size of the emitting region (r_in < 1r_S) cannot be explained.
Variables associated with the success of group cognitive behavioral therapy for insomnia: preliminary results

Introduction. Insomnia Disorder (ID) is defined as the predominant dissatisfaction with the quantity or quality of sleep associated with difficulty in initiating or maintaining sleep or early-morning awakenings with the inability to go back to sleep. Cognitive Behavioral Therapy (CBT) has proven its effectiveness for ID, being established as a frontline treatment. Objective. To identify the variables associated with the success of CBT for insomnia. Method. The sample consisted of 28 patients who attended the HCUV Sleep Unit and met ID diagnostic criteria. All patients underwent a CBT program in group format. Several sociodemographic and clinical characteristics (general psychopathology, anxiety, depression, anger, emotional regulation, and personality) were evaluated in order to determine which discriminated between patients who improve and those who do not improve after CBT, using the Index Insomnia Severity (ISI) as a criterion variable. Results: After the program, almost 60% of the sample improved. Significant differences were found in the level of severity of insomnia after CBT according to sex (p = 0.027), with women improving more. Likewise, lower levels of psychopathology were related to a better response to the intervention (p = 0.007). Moreover, two personality dimensions were significant: low Harm avoidance (p = 0.006) and high Self-directedness (p = 0.026), appearing associated with improvement. Conclusions. The CBT-I group program was globally effective in improving insomnia, detecting that certain clinical and personality profiles benefit the most. More research is needed to determine if they could be patients with different subtypes of insomnia. The temperament dimensions seem to play a relevant role in determining subtypes of insomnia and perhaps we could offer more personalized treatments.
Global drought trends and future projections

Drought is one of the most difficult natural hazards to quantify and is divided into categories (meteorological, agricultural, ecological and hydrological), which makes assessing recent changes and future scenarios extremely difficult. This opinion piece includes a review of the recent scientific literature on the topic and analyses trends in meteorological droughts by using long-term precipitation records and different drought metrics to evaluate the role of global warming processes in trends of agricultural, hydrological and ecological drought severity over the last four decades, during which a sharp increase in atmospheric evaporative demand (AED) has been recorded. Meteorological droughts do not show any substantial changes at the global scale in at least the last 120 years, but an increase in the severity of agricultural and ecological droughts seems to emerge as a consequence of the increase in the severity of AED. Lastly, this study evaluates drought projections from earth system models and focuses on the most important aspects that need to be considered when evaluating drought processes in a changing climate, such as the use of different metrics and the uncertainty of modelling approaches. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.
Using LISA-like gravitational wave detectors to search for primordial black holes

Primordial black hole (PBH), which can be naturally produced in the early universe, remains a promising dark matter candidate . It can merge with a supermassive black hole (SMBH) in the center of a galaxy and generate gravitational wave (GW) signals in the favored frequency region of LISA-like experiments. In this work, we initiate the study on the event rate calculation for such extreme mass ratio inspirals (EMRI). Including the sensitivities of various proposed GW detectors, we find that such experiments offer a novel and outstanding tool to test the scenario where PBH constitutes (fraction of) dark matter. The PBH energy density fraction of DM ($f_\text{PBH}$) could potentially be explored as small as $10^{-3} \sim 10^{-4}$. Further, LISA has the capability to search for PBH mass upto $10^{-2} \sim 10^{-1} M_\odot$. Other proposed GW experiments can probe lower PBH mass regime.
Coupling independent walkers and the inclusion process

AbstractWe show propagation of local equilibrium for the symmetric inclu-sion process (SIP) after di usive rescaling of space and time, as well asthe local equilibrium property of the non-equilibrium steady state inthe boundary driven SIP. The main tool is self-duality and a couplingbetween nSIP particles and nindependent random walkers. 1 Introduction The symmetric inclusion process (SIP) was introduced in [GKRV] and stud-ied in more detail in [GRV]. It is a process where particles move on thelattice Z d according to symmetric nearest neighbor random walk jumps atrate m=2, and on top there is an attractive interaction governed by \in-clusion jumps" where particles at nearest neighbor positions invite eachother (invitations which are always followed up) at rate 1. The attractiveinteraction as opposed to exclusion in the well-known exclusion process isresponsible for interesting phenomena. In the limit of small di usion, con-densation phenomena occur [GRVc], and there is an analogue of Liggett’scomparison inequality which leads to \propagation of positive correlations"for appropriately chosen initial product measures [GRV]. The fundamental property which makes this model tractable, despitethe interaction is self-duality. Self-duality, combined with a good couplingof exclusion walkers and independent random walkers has lead in the con-text of the exclusion process to the proof of the hydrodynamic limit, in thesense of propagation of local equilibrium; see e.g. [MP], [MIPP] for twogood reference on this approach based on v-functions. The construction ofa good coupling between exclusion walkers and independent random walk-ers relies quite strongly on particular properties of the exclusion process,1