Miracle-dz/UniFAR-FTUs huggingface dataset:
Near BMN dynamics of the AdS 3 × S 3 × S 3 × S 1 superstring

We investigate the type IIA AdS3 × S ×M4 superstring with M4 = S × S or M4 = T . String theory in this background is interesting because of AdS3/CFT2 and its newly discovered integrable structures. We derive the kappa symmetry gauge-fixed Green-Schwarz string action to quadratic order in fermions and quartic order in fields utilizing a near BMN expansion. As a first consistency check of our results we show that the two point functions are one-loop finite in dimensional regularization. We then perform a Hamiltonian analysis where we compare the energy of string states with the predictions of a set of conjectured Bethe equations. While we find perfect agreement for single rank one sectors, we find that the product SU(2)×SU(2) sector does not match unless the Bethe equations decouple completely. We then calculate 2→ 2 bosonic tree-level scattering processes on the string worldsheet and show that the two-dimensional S-matrix is reflectionless. This might be important due to the presence of massless worldsheet excitations which are generally not described by the Bethe equations. ar X iv :1 20 4. 47 42 v3 [ he pth ] 1 8 O ct 2 01 2
Effects of estuary characteristics and tsunami waveforms on transported sand sheets by

The details of the 1700 Cascadia Subdction Zone earthquake and tsunami can be better constrained using data from tsunami sand sheets, provided knowledge of the waveform, estuary geometry and bed roughness controls on the deposition pattern of the sand sheet. In this study, we use the hydrodynamic and sediment transport model, Delft3D, to investigate the effects of each of these parameters on tsunami sand sheet inland extent and deposition pattern. Different waves produce different sand sheet patterns, as do different estuary geometries and bed roughness. The key finding is that estuary geometry has the greatest impact on deposition and erosion. With accurate reconstruction of the estuary parameters at the time of the event, a relatively limited range of waveforms can be inferred from the sand sheet, therefore informing details of the associated rupture. Inversion of rupture characteristics from tsunami deposits can be applied to estuaries along the coast of Oregon. With this information, new constrains of the details of the 1700 event and older events allows for improved estimates of a future Cascadia Subduction Zone event. INTRODUCTION Sand sheets in coastal estuaries in the Pacific Northwest provide evidence of the earthquake and tsunami that occurred in 1700 along the Cascadia Subduction Zone [Atwater, 1987]. However, geoscientists have no measurements to directly constrain the geometry of the 1700 fault rupture and resulting tsunami wave characteristics, because instrumentation did not exist. Current knowledge of the 1700 event is based on tsunami
Study on Statistical Characteristics of Nonpoint Pollutants Deposited in Urban Areas

The objective of this study is to clarify the statistical characteristics of pollutants deposited in urban areas, taking account of stages (road surfaces, street inlets and streams) and land uses where they accumulated, from surveys over a period of 16 years in Kyoto City. The result of correlation analysis showed that there are intimate relationship between grain size and such pollutants as organic matter and nutrients. It showed the dependence of pollutants on grain size. After principal component analysis, the distribution of principal factor loading showed that the first and the second components meant the type of pollution and the physical property of pollutants. The distribution of the principal factor score showed that each land use had different pollutant characteristics. On the other hand, the characteristics of the pollutants that accumulated on a road surface were shown to be affected by storms, and nutrients were more likely to run off in a storm than organic matter. The content rate of pollutants accumulated in a stream was shown to decrease in proportion to the progress of sewer construction, but it was also shown that the rate of some pollutants has not changed in the area of a separate sewer system. As a result, some valuable information for the control of nonpoint pollutants in urban area was obtained.
Simultaneous Determination of Chlorogenic Acid and Galuteolin in Honeysuckle

Objective:To establish a wavelength conversion method for determination of chlorogenic acid and galuteolin in honeysuckle simultaneously.Method:The separation was performed on a Hypersil gold C18 column (4.6 mm × 250 mm,5 μm). Acetonitril-0.05% H3P04 was used as the mobile phase with gradient elution.The flow rate was 0.5 mL·min-1.The detection wavelength was at 327 nm and 350 nm.Result:The linear range(n=5)was 0.06 × 103-0.42 × 103 mg·L-1(r=0.999 9) and 0.716-5.728 g·L-1(r=0.999 95) respectively.The average recoveries(n=6) of chlorogenic acid and galuteolin were 101.15% and 99.80% respectively.Conclusion:This method is sensitive,reliable and can be applied to determine chlorogenic acid and galuteolin in the raw material of honeysuckle.
Bounds on dark matter annihilation cross-sections from inert doublet model in the context of 21-cm cosmology of dark ages

We study the fluctuations in the brightness temperature of 21-cm signal [Formula: see text] at the dark ages ([Formula: see text]) with a dark matter (DM) candidate in Inert Doublet Model (IDM). We then explore the effects of different fractions of IDM DM on [Formula: see text] signal. The IDM DM masses are chosen in few tens of GeV region as well as in the high mass region beyond 500 GeV. It has been observed that the [Formula: see text] signal is more sensitive in the DM mass range of 70–80 GeV. A lower bound on annihilation cross-section for this DM is also obtained by analyzing the [Formula: see text] signal. This is found to lie within the range [Formula: see text] cm3/s for the IDM DM mass range 10 GeV[Formula: see text] GeV.
Machine learning in the social and health sciences

The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented. This may be due to the separate development of research in the computational/data versus social and health sciences as well as a lack of accessible overviews and adequate training in ML techniques for non data science researchers. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches, by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, and causal inference to common research goals, such as estimating prevalence of adverse health or social outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes. This meta-mapping aims at overcoming disciplinary barriers and starting a fluid dialogue between researchers from the social and health sciences and methodologically trained researchers. Such mapping may also help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences, and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research.
UniTTS: Residual Learning of Unified Embedding Space for Speech Style Control

We propose a novel high-fidelity expressive speech synthesis model, UniTTS, that learns and controls overlapping style attributes avoiding interference. UniTTS represents multiple style attributes in a single unified embedding space by the residuals between the phoneme embeddings before and after applying the attributes. The proposed method is especially effective in controlling multiple attributes that are difficult to separate cleanly, such as speaker ID and emotion, because it minimizes redundancy when adding variance in speaker ID and emotion, and additionally, predicts duration, pitch, and energy based on the speaker ID and emotion. In experiments, the visualization results exhibit that the proposed methods learned multiple attributes harmoniously in a manner that can be easily separated again. As well, UniTTS synthesized high-fidelity speech signals controlling multiple style attributes. The synthesized speech samples are presented at https://anonymous-authors2022.github. io/paper_works/UniTTS/demos/.
More than Encoder: Introducing Transformer Decoder to Upsample

Medical image segmentation methods downsample images for feature extraction and then upsample them to restore resolution for pixel-level predictions. In such schema, upsample technique is vital in restoring information for better performance. However, existing upsample techniques leverage little information from downsampling paths. The local and detailed feature from the shallower layer such as boundary and tissue texture is crucial in segmentation, especially medical image segmentation. To this end, we propose a novel upsample approach for medical image segmentation, Window Attention Upsample (WAU), which upsamples features conditioned on local and detailed features from downsampling path in local windows by introducing attention decoders of Transformer. WAU could serve as a general upsample method and be incorporated into any segmentation model that possesses lateral connections. We first propose the Attention Upsample which consists of Attention Decoder (AD) and bilinear upsample. AD leverages pixel-level attention to model longrange dependency and global information for a better upsample. Bilinear upsample is introduced as the residual connection to complement the upsampled features. Moreover, considering the extensive memory and computation cost of pixel-level attention, we further design a window attention scheme to restrict attention computation in local windows instead of the global range. We evaluate our method (WAU) on classic UNet structure with lateral connections and achieve state-of-the-art performance on Medical Segmentation Decathlon (MSD) Brain and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. We also validate the effectiveness of our method on multiple classic architectures and achieve consistent improvement.
Polaronic Correlations in Magnetic Behaviour of Oxygen Deficient V2O5

In this paper, we investigate the magnetic behaviour of oxygen deficient vanadium pentoxide and its correlation with DC electrical transport. The undoped V2O5 sample is synthesized by thermal decomposition resulting in oxygen deficient polycrystalline nature. It has a crystallite size of about 80 nm and a chemical stoichiometry of V2O4.65 with a significant V 4+ states in addition to V 5+ as seen from X-ray diffraction and X-ray photoelectron spectroscopy respectively. The optical studies reveal a bandgap of 2.2 eV and a significant emission due to defects (1.8 eV) as well as Moss-Burstein effect(3.1 eV) which arises due to gap states and split in conduction band respectively. The temperature dependant electrical conductivity and thermopower measurements clearly demonstrate a polaronic conduction mechanism of small polaron hopping with hopping energy of 0.112 eV. The V2O5 sample is found to show a strong field as well as temperature dependent magnetic behaviour when measured using sensitive SQUID magnetometer. It shows a positive magnetic susceptibility at all temperatures (5-300 K) at low field (μ0H= 0.1 T). The observed behavior is found to be a mix of ferromagnetism (FM) and antiferromagnetism (AFM, θ = −2.1 K in CW fit). On the other hand, for high field (μ0H= 1 T), it showed FM only at low temperatures and abruptly becomes diamagnetic at 31.2 K. This varied magnetic behavior has been attributed to small polaron hopping which arises because of oxygen vacancy defects compensated by charge defects of V 4+ and transition from bound to free polarons seen from transport. This is further revealed as strong hybridization of V bonded to oxygen vacancy VO and neighboring V 5+ ions, resulting in net magnetic moment per vacancy (3.959 μB) as predicted from first principle calculations. The insignificantly low energy difference between FM and AFM interactions observed from calculations and their critical dependence on oxygen vacancy concentrations justify the varied magnetic behaviour of V2O5 reported in the literature. ∗ corresponding author email:kbvinayak@iisertvm.ac. in
A Concavification Approach to Ambiguous Persuasion

This note shows that the value of ambiguous persuasion characterized in Beauchêne, Li and Li (2019) can be given by a concavification program as in Bayesian persuasion (Kamenica and Gentzkow, 2011). In addition, it implies that an ambiguous persuasion game can be equivalently formalized as a Bayesian persuasion game by distorting the utility functions. This result is obtained under a novel construction of ambiguous persuasion.
Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling

The evaluation of rare but high-stakes events remains one of the main difﬁculties in obtaining reliable policies from intelligent agents, especially in large or continuous state/action spaces where limited scalability enforces the use of a prohibitively large number of testing iterations. On the other hand, a biased or inaccurate policy evaluation in a safety-critical system could potentially cause unexpected catastrophic failures during deployment. In this paper, we propose the Accelerated Policy Evaluation (APE) method, which simultaneously uncovers rare events and estimates the rare event probability in Markov decision processes. The APE method treats the environment nature as an adversarial agent and learns towards, through adaptive importance sampling, the zero-variance sampling distribution for the policy evaluation. Moreover, APE is scalable to large discrete or continuous spaces by incorporating function approximators. We investigate the convergence properties of proposed algorithms under suitable regularity conditions. Our empirical studies show that APE estimates rare event probability with a smaller variance while only using orders of magnitude fewer samples compared to baseline methods in both multi-agent and single-agent environments.
Non-native English lexicon creation for bilingual speech synthesis

Bilingual English speakers speak English as one of their languages. Their English is of a non-native kind, and their conversations are of a code-mixed fashion. The intelligibility of a bilingual text-to-speech (TTS) system for such non-native English speakers depends on a lexicon that captures the phoneme sequence used by non-native speakers. However, due to the lack of non-native English lexicon, existing bilingual TTS systems employ native English lexicons that are widely available, in addition to their native language lexicon. Due to the inconsistency between the non-native English pronunciation in the audio and native English lexicon in the text, the intelligibility of synthesized speech in such TTS systems is significantly reduced. This paper is motivated by the knowledge that the native language of the speaker highly influences non-native English pronunciation. We propose a generic approach to obtain rules based on letter to phoneme alignment to map native English lexicon to their non-native version. The effectiveness of such mapping is studied by comparing bilingual (Indian English and Hindi) TTS systems trained with and without the proposed rules. The subjective evaluation shows that the bilingual TTS system trained with the proposed non-native English lexicon rules obtains a 6% absolute improvement in preference.
Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets

Language models can generate harmful and biased outputs and exhibit undesirable behavior according to a given cultural context. We propose a Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets, an iterative process to significantly change model behavior by crafting and fine-tuning on a dataset that reflects a predetermined set of target values. We evaluate our process using three metrics: quantitative metrics with human evaluations that score output adherence to a target value, toxicity scoring on outputs; and qualitative metrics analyzing the most common word associated with a given social category. Through each iteration, we add additional training dataset examples based on observed shortcomings from evaluations. PALMS performs significantly better on all metrics compared to baseline and control models for a broad range of GPT-3 language model sizes without compromising capability integrity. We find that the effectiveness of PALMS increases with model size. We show that significantly adjusting language model behavior is feasible with a small, hand-curated dataset.
A Disk and No Signatures of Tidal Distortion in the Galaxy “Lacking” Dark Matter NGC 1052-DF2

Using ultra-deep imaging (μ g = 30.4 mag arcsec−2; 3σ, 10″ × 10″), we probed the surroundings of the first galaxy “lacking” dark matter (DM) KKS2000[04] (NGC 1052–DF2). Signs of tidal stripping in this galaxy would explain its claimed low content of DM. However, we find no evidence of tidal tails. In fact, the galaxy remains undisturbed down to a radial distance of 80″. This radial distance triples previous spatial explorations of the stellar distribution of this galaxy. In addition, the distribution of its globular clusters (GCs) is not extended in relation to the bulk of the galaxy (the radius containing half of the GCs is 21″). We also found that the surface brightness radial profiles of this galaxy in the g and r bands decline exponentially from 35″ to 80″. Together with a constant ellipticity and position angle in the outer parts of the galaxy, this strongly suggests the presence of a low-inclination disk. This is consistent with the evidence of rotation found for this object. This finding implies that the dynamical mass of this galaxy is a factor of 2 higher than previously reported, which brings the DM content of this galaxy in line with galaxies of similar stellar mass.
Designing an integrated autonomous mobility-on-demand and urban transit system

We envision a multimodal transportation system in the near future when autonomous vehicles (AVs) will be used to serve the ﬁrst mile and last mile of transit trips. For this purpose, the current research proposes an optimization model for designing an integrated autonomous mobility-on-demand (AMoD) and urban transit system. The proposed model is a mixed-integer non-linear programming model that captures the strategic behavior of passengers in a multimodal network through a passenger assignment model. It determines which transit routes to operate, the frequency of the operating routes, the ﬂeet size of AVs required in each transportation analysis zone to serve the demand and the passenger ﬂow on both road and transit network. A Benders decomposition approach with several enhancements is proposed to solve the given optimization program. Computational experiments are presented for the Sioux Falls multimodal network. The results show a signiﬁcant improvement in the congestion in the city center with the introduction and optimization of the integrated transportation system.
Optimal Adaptive Control of a Knee Joint Exoskeleton for Lower Limb Functional Rehabilitation

Lower limb exoskeleton robots hold great potential for rehabilitation, movement assistance, and strength augmentation. Design control to guarantee optimal needed assistance is still a challenge considering the pathological variances between patients. In this paper, we proposed an optimal adaptive control scheme based on Particle Swarm Optimization (PSO) Algorithm. The proposed controller is based on a well-known dynamic model of the knee joint exoskeleton, and the optimization algorithm is used to minimize a square error fitness function, which quantifies tracking performances. Control parameters are tuned respecting some nonlinear constraints for step response of the system and boundaries constraints. Numerical simulation results are presented to show the validity and the high performances of the proposed approach.
Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification

Fine-grained visual classification (FGVC) aims to classify sub-classes of objects in the same super-class (e.g., species of birds, models of cars). For the FGVC tasks, the essential solution is to find discriminative subtle information of the target from local regions. Traditional FGVC models preferred to use the refined features, i.e., high-level semantic information for recognition and rarely use low-level information. However, it turns out that low-level information which contains rich detail information also has effect on improving performance. Therefore, in this paper, we propose cross-layer navigation convolutional neural network for feature fusion. First, the feature maps extracted by the backbone network are fed into a convolutional long short-term memory model sequentially from high-level to low-level to perform feature aggregation. Then, attention mechanisms are used after feature fusion to extract spatial and channel information while linking the high-level semantic information and the low-level texture features, which can better locate the discriminative regions for the FGVC. In the experiments, three commonly used FGVC datasets, including CUB-200-2011, Stanford-Cars, and FGVC-Aircraft datasets, are used for evaluation and we demonstrate the superiority of the proposed method by comparing it with other referred FGVC methods to show that this method achieves superior results. https://github.com/PRIS-CV/CN-CNN.git
Sublinear Time Hypergraph Sparsification via Cut and Edge Sampling Queries

The problem of sparsifying a graph or a hypergraph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Bencz\'ur and Karger (1996) showed that given any $n$-vertex undirected weighted graph $G$ and a parameter $\varepsilon \in (0,1)$, there is a near-linear time algorithm that outputs a weighted subgraph $G'$ of $G$ of size $\tilde{O}(n/\varepsilon^2)$ such that the weight of every cut in $G$ is preserved to within a $(1 \pm \varepsilon)$-factor in $G'$. The graph $G'$ is referred to as a {\em $(1 \pm \varepsilon)$-approximate cut sparsifier} of $G$. Subsequent recent work has obtained a similar result for the more general problem of hypergraph cut sparsifiers. However, all known sparsification algorithms require $\Omega(n + m)$ time where $n$ denotes the number of vertices and $m$ denotes the number of hyperedges in the hypergraph. Since $m$ can be exponentially large in $n$, a natural question is if it is possible to create a hypergraph cut sparsifier in time polynomial in $n$, {\em independent of the number of edges}. We resolve this question in the affirmative, giving the first sublinear time algorithm for this problem, given appropriate query access to the hypergraph.