Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical control of diffuse light storage in an ultracold atomic gas

We show that coherent multiple light scattering, or diffuse light propagation, in a disordered atomic media prepared at ultralow temperatures can be effectively delayed in the presence of a strong control field initiating a stimulated Raman process. On a relatively short time scale, when the atomic system can preserve its configuration and effects of atomic motion can be ignored, the scattered signal pulse, diffusely propagating via multiple coherent scattering through the media, can be stored in the spin subsystem through its stimulated Raman-type conversion into spin coherence. We demonstrate how this mechanism, potentially interesting for developing quantum memories, would work for the example of a coherent light pulse propagating through an alkali-metal atomic vapor under typical conditions attainable in experiments with ultracold atoms.
Data-adaptive Transfer Learning for Low-resource Translation: A Case Study in Haitian

Multilingual transfer techniques often improve 001 low-resource machine translation (MT). Many 002 of these techniques are applied without con-003 sidering data characteristics. We show in 004 the context of Haitian-to-English translation 005 that transfer effectiveness is correlated with 006 amount of training data and relationships be-007 tween knowledge-sharing languages. Our ex-008 periments suggest that beyond a threshold of 009 authentic data, back-translation augmentation 010 methods are counterproductive, while cross-011 lingual transfer during training is preferred. We 012 complement this finding by contributing a rule-013 based French-Haitian orthographic and syntac-014 tic engine and a novel method for phonologi-015 cal embedding. When used with multilingual 016 techniques, orthographic transformation signifi-017 cantly improves performance over conventional 018 methods, and phonological transfer greatly im-019 proves performance in Jamaican MT. 020
Testing new physics models by top charge asymmetry and polarization at the LHC

As a top quark factory, the LHC can test new physics models used to explain the top quark forwardbackward asymmetry A(FB)(t) measured at the Tevatron. In this work we perform a comparative study for two such models: the W' model and the color triplet diquark (phi) model. Requiring these models to explain A(FB)(t) and also satisfy the top pair production rate measured at the Tevatron, we examine their contributions to the LHC observables such as the polarizations and charge asymmetries in top quark productions and the charge asymmetry in W' (or phi) pair production. We find that these observables can be enhanced to their observable levels and current LHC measurement on the top charge asymmetry has already tightly constrained the W' model. We also find that each observable shows different characteristics in different models, which can be utilized to discriminate the models.
Tomato leaf disease detection using deep learning algorithm

: Convolutional Neural Network has actually considerably increasing the precision of image acceptance methods within the many years which are present. Most рrеtrained training that is actually deeply are offered for usage and this also studies have used one particular strong training unit particularly, ResNet-152 for finding leаf disorders suffering іn tomato vegetation. The transfer training method is actually used within the tomato-leaf diseasеd facts ѕet by fine-tuning the hyper-pаramеters and also the levels on the ResNet-152 design. The dataset consists of 18345 photos of tomato dried leaves infected with ten tоmato conditions. The ResNet-152 this is certainly fіne-tυned unit an reliability of 99% that will be seen to be a lot better than the present different versions. Symptoms and parts which are harmful tomato foliage had been recognized making use of photographs seized throυgh CCTV digital cameras from inside the areas. Thiѕ design maу end up being a good instrument fоr growers for very early discovery of leaf ailments and receiving a give that will be great.
High-redshift supernova rates measured with the gravitational telescope A 1689

Aims. We present a ground-based, near-infrared search for lensed supernovae behind the massive cluster Abell 1689 at z = 0.18, which is one of the most powerful gravitational telescopes that nature provides. Methods. Our survey was based on multi-epoch J-band observations with the HAWK-I instrument on VLT, with supporting optical data from the Nordic Optical Telescope. Results. Our search resulted in the discovery of five photometrically classified, core-collapse supernovae with high redshifts of 0.671 < z < 1.703 and magnifications in the range Delta m = -0.31 to -1.58 mag, as calculated from lensing models in the literature. Owing to the power of the lensing cluster, the survey had the sensitivity to detect supernovae up to very high redshifts, z similar to 3, albeit for a limited region of space. We present a study of the core-collapse supernova rates for 0.4 < z < 2.9, and find good agreement with previous estimates and predictions from star formation history. During our survey, we also discovered two Type Ia supernovae in A 1689 cluster members, which allowed us to determine the cluster Ia rate to be 0.14(-0.09)(+0.19) SNuB h(2) (SNuB 10(-12) SNe L-circle dot,B(-1) yr(-1)), where the error bars indicate 1 sigma confidence intervals, statistical and systematic, respectively. The cluster rate normalized by the stellar mass is 0.10(-0.06)(+0.13) +/- 0.02 in SNuM h(2) (SNuM = 10(-12) SNe M-1 yr(-1)). Furthermore, we explore the optimal future survey for improving the core-collapse supernova rate measurements at z greater than or similar to 2 using gravitational telescopes, and for detections with multiply lensed images, and we find that the planned WFIRST space mission has excellent prospects. Conclusions. Massive clusters can be used as gravitational telescopes to significantly expand the survey range of supernova searches, with important implications for the study of the high-z transient Universe.
Entity Cloze By Date: Understanding what LMs know about unseen entities

Language models (LMs) are typically trained 001 once on a large-scale corpus and used for years 002 without being updated. Our world, however, 003 is dynamic, and new entities constantly arise. 004 We propose a framework to analyze what LMs 005 can infer about new entities that did not exist 006 when the LMs were pretrained. We derive a 007 dataset of entities indexed by their origination 008 date and paired with their English Wikipedia ar-009 ticles, from which we can find sentences about 010 each entity. We evaluate LMs’ perplexity on 011 masked spans within these sentences. We show 012 that models more informed about the entities, 013 such as those with access to a textual defini-014 tion of them, achieve lower perplexity on this 015 benchmark. Our experimental results demon-016 strate that making inferences about new entities 017 remains difficult for LMs. Given its wide cov-018 erage on entity knowledge and temporal index-019 ing, our dataset can be used to evaluate LMs 020 and techniques designed to modify or extend 021 their knowledge. Our automatic data collec-022 tion pipeline can be easily used to continually 023 update our benchmark. 024
Multilingual offensive lexicon annotated with contextual information

Online hate speech and offensive comments de-001 tection is not a trivial research problem since 002 pragmatic (contextual) factors influence what 003 is considered offensive. Moreover, offensive 004 terms are hardly found in classical lexical re-005 sources such as wordnets, sentiment, and emo-006 tion lexicons. In this paper, we embrace the 007 challenges and opportunities of the area and in-008 troduce the first multilingual offensive lexicon 009 (MOL), which is composed of 1,000 explicit 010 and implicit pejorative terms and expressions 011 annotated with contextual information. The 012 terms and expressions were manually extracted 013 by a specialist from Instagram abusive com-014 ments originally written in Portuguese and man-015 ually translated by American English, Latin 016 American Spanish, African French, and Ger-017 man native speakers. Each expression was an-018 notated by three different annotators, producing 019 high human inter-annotator agreement. Accord-020 ingly, this resource provides a new perspective 021 to explore abusive language detection. 022
Improving GPT-3 after deployment with a dynamic memory of feedback

Large LMs such as GPT -3, while powerful, are 001 not immune to mistakes, but are prohibitively 002 costly to retrain. One failure mode is misinter-003 preting a user’s instruction (e.g., GPT -3 inter-004 preting "What word is similar to ‘good’? " to 005 mean a homonym, while the user intended a 006 synonym). Our goal is to allow users to correct 007 such errors directly through interaction – with-008 out retraining. Our approach pairs GPT -3 with a 009 growing memory of cases where the model mis-010 understood the user’s intent and was provided 011 with feedback, clarifying the instruction. Given 012 a new query, our memory-enhanced GPT -3 uses 013 feedback from similar, prior queries to enrich 014 the prompt. Through simple proof-of-concept 015 experiments, we show how a (simulated) user 016 can interactively teach a deployed GPT -3, dou-017 bling its accuracy on basic lexical tasks (e.g., 018 generate a synonym) where users query in dif-019 ferent, novel (often misunderstood) ways. In 020 such scenarios, memory helps avoid repeating 021 similar past mistakes. Our simple idea is a first 022 step towards strengthening deployed models, 023 potentially broadening their utility. 1 024
Optimization of Suzhou Garden Infrastructure Layout Based on Federal Learning

Along with the accelerating urbanization process in China, the problem of urban infrastructure layout has become increasingly prominent. e high density of buildings and the extremely unreasonable distribution of infrastructure make the development face great resistance. is paper reveals the problems in the layout of garden infrastructure by studying and analyzing the theoretical foundations of federal learning and distributed learning and provides an in-depth analysis and elaboration of the problem. e paper uses the shape index and landscape index of green infrastructure (green space, arable land, and water bodies), the average width of roads, road network density, and weighted buildings to conduct a comparative study through the di erences in ventilation speed and temperature at di erent layout garden scales. According to the problems existing in the garden layout in the experimental results, corresponding improvement measures are targeted, and the infrastructure layout of the garden is combined with ecology to make the layout within the garden more suitable.
The Use of a Two-Pole Linear Prediction Model in Speech Recognition

Abstract : In speech recognition applications, it is often desirable to make a gross characterization of the shape of the spectrum of a particular sound. The autocorrelation method of linear prediction analysis leads to an all-pole approximation to the signal spectrum. Hence an LPC analysis using two poles produces one possible gross characterization. The two poles are computed as the roots of a quadratic equation whose coefficients are the linear prediction parameters, which are simple functions of the autocorrelation coefficients (R sub 0), (R sub 1), and (R sub 2). The poles are either both real or form a conjugate pair in the z plane. This fact, together with the exact positions of the poles, is particularly useful in describing certain gross characteristics of the spectrum. The spectral dynamic range of the two-pole spectrum and the normalized minimum error are suggested as more suitable substitutes for the two- pole bandwidths in interpreting the information supplied by the model for the purpose of spectral characterization.
A Pre-trained Document-Grounded Conversation Model with Learning Low-frequency Patterns

Currently, the Generative Pre-trained Trans-001 former model (GPT-2) has achieved remark-002 able performance in document-grounded di-003 alogue generation since high-frequency pat-004 terns in the large corpora are well memorized 005 during its pre-train procedure. However, it 006 is still hard to capture low-frequency task-007 speciﬁc patterns especially when directly tak-008 ing given documents and dialogue context as 009 input. Here we propose an encoder-decoder 010 framework including a semantic-oriented en-011 coder and GPT-2 decoder with knowledge-012 aware classiﬁcation, which strengthens the 013 learning of two following task-speciﬁc pat-014 terns. One pattern is how to semantically se-015 lect the crucial information of dialogue context 016 and corresponding history knowledge from 017 documents; the other is when to generate a re-018 sponse with knowledge since many responses 019 do not contain it. With learned high-and low-020 frequency patterns, empirical study shows that 021 our method has better generative performance 022 than state-of-the-arts. 023
CS294-226: Advances in Error-Correcting Codes

In the first part of lecture, we will see Gopalan’s presentation of a particular LDC construction [Gop09], that achieves 3-query LDCs with subexponential block size in the dimension, in contrast to 2-LDCs which require exponential block size. For ease of exposition, in this lecture we will describe how to construct a 6-query LDC rather than a 3-query LDC. This construction is based on the following combinatorial object:
End-to-End Dereverberation, Beamforming, and Speech Recognition in a Cocktail Party

Far-field multi-speaker automatic speech recognition (ASR) has drawn increasing attention in recent years. Most existing methods feature a signal processing frontend and an ASR backend. In realistic scenarios, these modules are usually trained separately or progressively, which suffers from either inter-module mismatch or a complicated training process. In this paper, we propose an end-to-end multi-channel model that jointly optimizes the speech enhancement (including speech dereverberation, denoising, and separation) frontend and the ASR backend as a single system. To the best of our knowledge, this is the first work that proposes to optimize dereverberation, beamforming, and multi-speaker ASR in a fully end-to-end manner. The frontend module consists of a weighted prediction error (WPE) based submodule for dereverberation and a neural beamformer for denoising and speech separation. For the backend, we adopt a widely used end-to-end (E2E) ASR architecture. It is worth noting that the entire model is differentiable and can be optimized in a fully end-to-end manner using only the ASR criterion, without the need of parallel signal-level labels. We evaluate the proposed model on several multi-speaker benchmark datasets, and experimental results show that the fully E2E ASR model can achieve competitive performance on both noisy and reverberant conditions, with over 30% relative word error rate (WER) reduction over the single-channel baseline systems.
Towards Interpretable Math Word Problem Solving with Grounded Linguistic Logic Reasoning

Automatically math word problem (MWP) 001 solving is a challenging artificial intelligence 002 task since a machine should be able to not only 003 understand a problem comprehensively on lin-004 guistics but also the grounded math logic en-005 tailed in the problem. Recently, lots of deep 006 learning models have made great progress in 007 MWP solving on answer accuracy, they rely 008 on shallow heuristics to achieve high perfor-009 mance, lacking of grounded math logic reason-010 ing, which makes them uninterpretable. To ad-011 dress this issue and push the research boundary 012 of MWPs to interpretable MWP solving, we 013 construct a large-scale and high-quality MWP 014 dataset named InterMWP which consists of 015 11,507 MWP data and annotates interpretable 016 algebraic knowledge formulas as the grounded 017 linguistic logic of each solving equation and 018 asks for a solver to output the formulas when 019 it decides current predicted node is an inner-020 node (operator) during expression reasoning. 021 We further propose a strong baseline called 022 InterSolver to show the effectiveness of our 023 constructed dataset and show how to harvest 024 these logic knowledge by fusing logic knowl-025 edge with semantic representation to improve 026 problem solving and make a step towards pro-027 viding interpretability. Experimental results 028 show that our InterSolver has strong logical 029 formula-based interpretability while achieving 030 high answer accuracy simultaneously. 031
Context-Paraphrase Enhanced Commonsense Question Answering

Commonsense question answering (CQA) gen-001 erally means that the machine use the mastered 002 commonsense to answer questions without rel-003 evant background material, which is a challeng-004 ing task in natural language processing. Many 005 prior methods mainly retrieve question related 006 evidences from the structured knowledge base 007 as the background material of the question, 008 while the extracted evidence is generally de-009 scribed through the entities and the relationship 010 between the entities, making it difﬁcult for the 011 machine to understand the meaning of the ev-012 idence completely. In this paper, we integrate 013 the paraphrase in WordNet and Wiktionary into 014 the evidence extraction process and machine 015 reading comprehension (MRC) model, and pro-016 pose a context-paraphrase enhanced common-017 sense question answering method. Speciﬁ-018 cally, the context-paraphrase obtained by Word-019 Net and Wiktionary is ﬁrst incorporated into 020 the construction process of the heterogeneous 021 graph, and the question related triple is ex-022 tracted based on the heterogeneous graph, the 023 triple is converted to triple-text based on a re-024 lational template. Then, the triple-text is used 025 as the context of the question to establish an as-026 sociation graph containing the relationship be-027 tween the context entities and the paraphrases. 028 We further integrate the association graph into 029 the MRC model to better guide the model to 030 answer. Experimental results on Common-031 senseQA and OpenBookQA show that context-032 paraphrase is effective in improving the answer 033 accuracy of the MRC model.
Service-Aware User Association and Resource Allocation in Integrated Terrestrial and Non-Terrestrial Networks: A Genetic Algorithm Approach

In 6G networks and beyond, multiple radio access networks (RANs), including; the satellite, high altitude platforms, low altitude platforms, and the terrestrial network, will co-exist. These networks are characterized by different capabilities and limitations in meeting the envisioned 6G contrasting user requirements. Therefore, associating users with the appropriate radio access network (RAN) in such an integrated network is rigorous and complex. In this work, the user association and resource allocation problem is formulated as a multi-objective optimization problem (MOOP), aiming to maximize data rate while minimizing mobility-induced handoff in the integrated network. Moreover, the problem is formulated in such a way as to prioritize the service provisioning of mission-critical users. The weighted sum method is adopted to simplify and transform the MOOP into a single-objective optimization problem (SOOP). In order to solve the formulated NP-hard SOOP, a genetic algorithm (GA) whose fitness value is based on the user’s service group is proposed. The performance of the proposed algorithm is evaluated by comparing it to the optimal solution, the greedy signal-to-interference-plus-noise ratio (SINR) based association, and the random user association algorithms. Simulation results show that as the number of access nodes in the network increases, the GA’s spectrum efficiency (SE) remains within 0.4% of the optimal solution. Moreover, the GA outperforms all three schemes in user acceptance ratio (AR) and handoff probability.
Effect of water deficit on growth and concentration of secondary metabolites of Thymus vulgaris

The aim of the experiment was to investigate the effect of the level and timing of watering on the productivity and chemical constituents of garden thyme ( Thymus vulgaris L., cultivar ‘Varico 3’) at growth stage (GS) 202 based on the extended BBCH scale in growth chamber for 13 weeks. Different irrigation regimes have been applied based on the soil water content (SWC%) as follows: 1) control (C: 1–13th weeks – 70% SWC), 2) mild drought stress (S1: 1–4th weeks – 70% SWC, from the 6th week, continuously decreasing drought stress to 40% DVD for 10–13th weeks), 3) sudden drought stress (S2: 1–9th weeks – 70% SWC, 10–13th weeks – 40% DVD), and 4) constant drought stress (S3: 1–4th weeks – 70% DVK, 5–13th weeks – 40% SWC). At the end of the experiment, it was determined that water deficiency induced no direct impact on the width and height of garden thyme plants, while internode and shoot lengths were affected significantly. The plants grown under constant drought stress (S3) conditions showed the minimum values concerning lengths (internode 1.30 cm, shoot 7.12 cm), while the highest ones (internode 2.50 cm, shoot 22.08 cm) were found in the control treatment. However, water supply had no significant effect on the shoot number and chlorophyll content of the leaves. The impact of drought stress both on rosmarinic acid and total phenolic content (TPC) was significant and showed the highest values in the S2 and S3 treatments: S2 had the highest rosmarinic acid concentration of 4.61%, and S3 had the highest total phenolic concentration of 0.75 mg GAE ml -1 . A similar result was found for antioxidant capacity in the S3 treatment, where plants reached the highest values (275.44 mg AAE g -1 dry weight), while the total flavonoid content was not significantly affected by different water supply levels. The results show that not only the level of water deficiency but also the change in water supply and the timing of the drought may have a significant effect on the quantity and quality of garden thyme production. of different irrigation regimes on the antioxidant capacity of dried and powdered Thymus
Tight-binding study of bilayer graphene Josephson junctions

Using highly efficient simulations of the tight-binding Bogoliubov-de Gennes model we solved self-consistently for the pair correlation and the Josephson current in a Superconducting-Bilayer graphene-Superconducting Josephson junction. Different doping levels for the non-superconducting link are considered in the short and long junction regime. Self-consistent results for the pair correlation and superconducting current resemble those reported previously for single layer graphene except in the Dirac point where remarkable differences in the proximity effect are found as well as a suppression of the superconducting current in long junction regime. Inversion symmetry is broken by considering a potential difference between the layers and we found that the supercurrent can be switched if junction length is larger than the Fermi length.