Miracle-dz/UniFAR-FTUs huggingface dataset:
Brief Announcement: Resource Competitive Broadcast against Adaptive Adversary in Multi-channel Radio Networks

Broadcasting in wireless networks is vulnerable to adversarial jamming. To thwart such behavior, researchers have proposed resource competitive analysis. In this framework, sending, listening, or jamming on one channel for one time slot costs one unit of energy. The adversary can employ arbitrary strategy to disrupt communication, but has a limited energy budget T. The honest nodes, on the other hand, aim to accomplish broadcast while spending only o(T). Previous work has shown, in a C-channels network containing n nodes, each node can receive the message in roughly O(T/C) time, while spending only roughly [EQUATION] energy. However, these algorithms only work for C = O(n), and can only tolerate an oblivious adversary. We improve the result by considering an adaptive adversary and arbitrary values of n and C. In our algorithms, for large T values, each node's runtime is O(T/C), and each node's energy cost is [EQUATION]. The time complexity is asymptotically optimal, while the energy complexity is near optimal in some cases. We use "epidemic broadcast" with proper working probabilities to achieve time efficiency and resource competitiveness, and leverage coupling arguments in the analysis to handle the adaptivity of the adversary.
Nature of the phase transition for finite temperature 
Nf=3
 QCD with nonperturbatively 
O(a)
 improved Wilson fermions at 
Nt=12

We study the nature of the finite temperature phase transition for three-flavor QCD. In particular we investigate the location of the critical endpoint along the three flavor symmetric line in the light quark mass region of the Columbia plot. In the study, the Iwasaki gauge action and the nonperturvatively O($a$) improved Wilson-Clover fermion action are employed. We newly generate data at $N_{\rm t}=12$ and set an upper bound of the critical pseudoscalar meson mass in the continuum limit $m_{\rm PS,E}\lesssim 110$MeV.
'I Just Want to Hack Myself to Not Get Distracted': Evaluating Design Interventions for Self-Control on Facebook

Beyond being the world's largest social network, Facebook is for many also one of its greatest sources of digital distraction. For students, problematic use has been associated with negative effects on academic achievement and general wellbeing. To understand what strategies could help users regain control, we investigated how simple interventions to the Facebook UI affect behaviour and perceived control. We assigned 58 university students to one of three interventions: goal reminders, removed newsfeed, or white background (control). We logged use for 6 weeks, applied interventions in the middle weeks, and administered fortnightly surveys. Both goal reminders and removed newsfeed helped participants stay on task and avoid distraction. However, goal reminders were often annoying, and removing the newsfeed made some fear missing out on information. Our findings point to future interventions such as controls for adjusting types and amount of available information, and flexible blocking which matches individual definitions of 'distraction'.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation

Inductive transfer learning has had a big impact on computer vision and NLP domains but has not been used in the area of recommender systems. Even though there has been a large body of research on generating recommendations based on modeling user-item interaction sequences, few of them attempt to represent and transfer these models for serving downstream tasks where only limited data exists. In this paper, we delve on the task of effectively learning a single user representation that can be applied to a diversity of tasks, from cross-domain recommendations to user profile predictions. Fine-tuning a large pre-trained network and adapting it to downstream tasks is an effective way to solve such tasks. However, fine-tuning is parameter inefficient considering that an entire model needs to be re-trained for every new task. To overcome this issue, we develop a parameter-efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks. Specifically, PeterRec allows the pre-trained parameters to remain unaltered during fine-tuning by injecting a series of re-learned neural networks, which are small but as expressive as learning the entire network. We perform extensive experimental ablation to show the effectiveness of the learned user representation in five downstream tasks. Moreover, we show that PeterRec performs efficient transfer learning in multiple domains, where it achieves comparable or sometimes better performance relative to fine-tuning the entire model parameters. Codes and datasets are available at https://github.com/fajieyuan/sigir2020_peterrec.
Optimal rate of condensation for trapped bosons
in the Gross–Pitaevskii regime

We study the Bose-Einstein condensates of trapped Bose gases in the Gross-Pitaevskii regime. We show that the ground state energy and ground states of the many-body quantum system are correctly described by the Gross-Pitaevskii equation in the large particle number limit, and provide the optimal convergence rate. Our work extends the previous results of Lieb, Seiringer and Yngvason on the leading order convergence, and of Boccato, Brennecke, Cenatiempo and Schlein on the homogeneous gas. Our method relies on the idea of 'completing the square', inspired by recent works of Brietzke, Fournais and Solovej on the Lee-Huang-Yang formula, and a general estimate for Bogoliubov quadratic Hamiltonians on Fock space.
Prediction with eventual almost sure guarantees

We study the problem of predicting the properties of a probabilistic model and the next outcome of the model sequentially in the infinite horizon, so that the perdition will make finitely many errors with probability 1. We introduce a general framework that models such predication problems. We prove some general properties of the framework, and show some concrete examples with the application of such properties.
Food Supply Chain and Business Model Innovation

This paper investigates the contribution of business model innovations in the advancement of novel food supply chains. Through a systematic literature review, the notable business model innovations in the food industry are identified, surveyed, and evaluated. Findings reveal that the innovations in value proposition, value creation processes, and value delivery processes of business models are the successful strategies proposed in food industry. It is further disclosed that rural female entrepreneurs, social movements, and also urban conditions are the most important driving forces causing farmers to reconsider their business models. In addition, the new technologies and environmental factors are the secondary contributors in business model innovation for the food processors. It is concluded that digitalization has disruptively changed the food distributor models. E-commerce models and Internet-of-Things are reported as the essential factors causing retailers to innovate their business models. Furthermore, consumption demand and product quality are two main factors affecting the business models of all the firms operating in the food supply chain regardless of their positions in the chain. The findings of the current study provide an insight into the food industry to design a sustainable business model to bridge the gap between food supply and food demand.
Towards the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes.

Neuroprosthesis, as one type of precision medicine device, is aiming for manipulating neuronal signals of the brain in a closed-loop fashion, together with receiving stimulus from the environment and controlling some part of our brain/body. In terms of vision, incoming information can be processed by the brain in millisecond interval. The retina computes visual scenes and then sends its output as neuronal spikes to the cortex for further computation. Therefore, the neuronal signal of interest for retinal neuroprosthesis is spike. Closed-loop computation in neuroprosthesis includes two stages: encoding stimulus to neuronal signal, and decoding it into stimulus. Here we review some of the recent progress about visual computation models that use spikes for analyzing natural scenes, including static images and dynamic movies. We hypothesize that for a better understanding of computational principles in the retina, one needs a hypercircuit view of the retina, in which different functional network motifs revealed in the cortex neuronal network should be taken into consideration for the retina. Different building blocks of the retina, including a diversity of cell types and synaptic connections, either chemical synapses or electrical synapses (gap junctions), make the retina an ideal neuronal network to adapt the computational techniques developed in artificial intelligence for modeling of encoding/decoding visual scenes. Altogether, one needs a systems approach of visual computation with spikes to advance the next generation of retinal neuroprosthesis as an artificial visual system.
APEX observations of ortho-H2D+ towards dense cores in the Orion B9 filament

Context. Initial conditions and very early stages of star formation can be probed through spectroscopic observations of deuterated molecular species Aims. We aim to determine the ortho-H2D+ properties (e.g. column density and fractional abundance with respect to H2) in a sample of dense cores in the Orion B9 star-forming filament, and to compare those with the previously determined source characteristics, in particular with the gas kinetic temperature, [N2D+]/[N2H+] deuterium fractionation, and level of CO depletion. Methods. We used the Atacama Pathfinder EXperiment (APEX) telescope to observe the 372 GHz o-H2D+(JKa, Kc = 11, 0−11, 1) line towards three prestellar cores and three protostellar cores in Orion B9. We also employed our previous APEX observations of C17O, C18O, N2H+, and N2D+ line emission, and 870 μm dust continuum emission towards the target sources. Results. The o-H2D+(11, 0−11, 1) line was detected in all three prestellar cores, but in only one of the protostellar cores. The corresponding o-H2D+ abundances were derived to be ~ (12−30) × 10−11 and ~ 6 × 10−11. Two additional spectral lines, DCO+(5−4) and N2H+(4−3), were detected in the observed frequency bands with high detection rates of 100 and 83%, respectively. We did not find any significant correlations among the explored parameters, although our results are mostly consistent with theoretical expectations. Also, the Orion B9 cores were found to be consistent with the relationship between theo-H2D+ abundance and gas temperature obeyed by other low-mass dense cores. The o-H2D+ abundance was found to decrease as the core evolves. Conclusions. The o-H2D+ abundances in the Orion B9 cores are in line with those found in other low-mass dense cores and larger than derived for high-mass star-forming regions. The higher o-H2D+ abundance in prestellar cores compared to that in cores hosting protostars is to be expected from chemical reactions where higher concentrations of gas-phase CO and elevated gas temperature accelerate the destruction of H2D+. The validity of using the [o-H2D+]/[N2D+] abundance ratio as an evolutionary indicator, which has been proposed for massive clumps, remains to be determined when applied to these target cores. Similarly, the behaviour of the [o-H2D+]/[DCO+] ratio as the source evolves was found to be ambiguous. Still larger samples and observations of additional deuterated species are needed to explore these potential evolutionary indicators further. The low radial velocity of the line emission from one of the targeted prestellar cores, SMM 7 (~ 3.6 km s−1 versus the systemic Orion B9 velocity of ~ 9 km s−1), suggests that it is a chance superposition seen towards Orion B9. Overall, as located in a dynamic environment of the Orion B molecular cloud, the Orion B9 filament provides an interesting target system to investigate the deuterium-based chemistry, and further observations of species like para-H2D+ and D2H+ would be of particular interest.
Candidate Population III stellar complex at z = 6.629 in the MUSE Deep Lensed Field

We discovered a strongly lensed (μ ≳ 40) Ly α emission at z = 6.629 (S/N ≃ 18) in the MUSE Deep Lensed Field (MDLF) targeting the Hubble Frontier Field (HFF) galaxy cluster MACS J0416. Dedicated lensing simulations imply that the Ly α emitting region necessarily crosses the caustic. The arc-like shape of the Ly α extends 3 arcsec on the observed plane and is the result of two merged multiple images, each one with a de-lensed Ly α luminosity L ≲ 2.8 × 1040 erg s−1 arising from a confined region (≲150 pc effective radius). A spatially unresolved Hubble Space Telescope(HST) counterpart is barely detected at S/N ≃ 2 after stacking the near-infrared bands, corresponding to an observed (intrinsic) magnitude m1500 ≳ 30.8 (≳35.0). The inferred rest-frame Ly α equivalent width is EW0 > 1120 Å if the IGM transmission is TIGM < 0.5. The low luminosities and the extremely large Ly α EW0 match the case of a Population III (Pop III) star complex made of several dozens stars (∼104 M⊙) that irradiate an H ii region crossing the caustic. While the Ly α and stellar continuum are among the faintest ever observed at this redshift, the continuum and the Ly α emissions could be affected by differential magnification, possibly biasing the EW0 estimate. The aforementioned tentative HST detection tends to favour a large EW0, making such a faint Pop III candidate a key target for the James Webb Space Telescope and Extremely Large Telescopes.
A multi‐country dynamic factor model with stochastic volatility for euro area business cycle analysis

Abstract This paper develops a dynamic factor model that uses euro area country‐specific information on output and inflation to estimate an area‐wide measure of the output gap. Our model assumes that output and inflation can be decomposed into country‐specific stochastic trends and a common cyclical component. Comovement in the trends is introduced by imposing a factor structure on the shocks to the latent states. We moreover introduce flexible stochastic volatility specifications to control for heteroscedasticity in the measurement errors and innovations to the latent states. Carefully specified shrinkage priors allow for pushing the model towards a homoscedastic specification, if supported by the data. Our measure of the output gap closely tracks other commonly adopted measures, with small differences in magnitudes and timing. To assess whether the model‐based output gap helps in forecasting inflation, we perform an out‐of‐sample forecasting exercise. The findings indicate that our approach yields superior inflation forecasts, both in terms of point and density predictions.
Scattering and Sparse Partitions, and their Applications

A partition $\mathcal{P}$ of a weighted graph $G$ is $(\sigma,\tau,\Delta)$-sparse if every cluster has diameter at most $\Delta$, and every ball of radius $\Delta/\sigma$ intersects at most $\tau$ clusters. Similarly, $\mathcal{P}$ is $(\sigma,\tau,\Delta)$-scattering if instead for balls we require that every shortest path of length at most $\Delta/\sigma$ intersects at most $\tau$ clusters. Given a graph $G$ that admits a $(\sigma,\tau,\Delta)$-sparse partition for all $\Delta>0$, Jia et al. [STOC05] constructed a solution for the Universal Steiner Tree problem (and also Universal TSP) with stretch $O(\tau\sigma^2\log_\tau n)$. Given a graph $G$ that admits a $(\sigma,\tau,\Delta)$-scattering partition for all $\Delta>0$, we construct a solution for the Steiner Point Removal problem with stretch $O(\tau^3\sigma^3)$. We then construct sparse and scattering partitions for various different graph families, receiving many new results for the Universal Steiner Tree and Steiner Point Removal problems.
Learning to Locomote with Deep Neural-Network and CPG-based Control in a Soft Snake Robot

In this paper, we present a new locomotion control method for soft robot snakes. Inspired by biological snakes, our control architecture is composed of two key modules: A deep reinforcement learning (RL) module for achieving adaptive goal-tracking behaviors with changing goals, and a central pattern generator (CPG) system with Matsuoka oscillators for generating stable and diverse locomotion patterns. The two modules are interconnected into a closed-loop system: The RL module, analogizing the locomotion region located in the midbrain of vertebrate animals, regulates the input to the CPG system given state feedback from the robot. The output of the CPG system is then translated into pressure inputs to pneumatic actuators of the soft snake robot. Based on the fact that the oscillation frequency and wave amplitude of the Matsuoka oscillator can be independently controlled under different time scales, we further adapt the option-critic framework to improve the learning performance measured by optimality and data efficiency. The performance of the proposed controller is experimentally validated with both simulated and real soft snake robots.
Many-body electronic structure of 
LaScO3
 by real-space quantum Monte Carlo

We present real space quantum Monte Carlo (QMC) calculations of the scandate LaScO$_3$ that proved to be challenging for traditional electronic structure approaches due to strong correlation effects resulting in inaccurate band gaps from DFT and $GW$ methods when compared with existing experimental data. Besides calculating an accurate QMC band gap corrected for supercell size biases and in agreement with numerous experiments, we also predict the cohesive energy of the crystal using the standard fixed-node QMC without any empirical or non-variational parameters. We show that promotion (optical) gap and fundamental gap agree with each other illustrating a clear absence of significant excitonic effects in the ideal crystal. We obtained these results in perfect consistency in two independent tracks that employ different basis sets (plane wave vs. localized gaussians), different codes for generating orbitals (\textsc{Quantum Espresso} vs. \textsc{Crystal}), different QMC codes (\textsc{Qmcpack} vs. \textsc{Qwalk}) and different high-accuracy pseudopotentials (ccECPs vs. Troullier-Martins) presenting the maturity and consistency of QMC methodology and tools for studies of strongly correlated problems.
Constraint on the mass of fuzzy dark matter from the rotation curve of the Milky Way

The fuzzy Dark Matter (FDM) is one of the recent models for dark matter. According to this model, dark matter is made of very light scalar particles with considerable quantum mechanical effects on the galactic scales which solves many problems of the Cold Dark Matter (CDM). Here we use the observed data from the rotation curve of the Milky Way (MW) galaxy to compare the results from FDM and CDM models. We show that FDM adds a local peak on the rotation curve close to the center of the bulge where its position and amplitude depend on the mass of FDM particles. From fitting the observed rotation curve with our expectation from FDM, we find the mass of FDM is $m = 2.5^{+3.6}_{-2.0} \times10^{-21}$eV. We note that the local peak of the rotation curve in MW can also be explained in the CDM model with an extra inner bulge model for the MW galaxy. We conclude that the FDM model explains this peak without need to extra structure for the bulge.
Symplectic networks: Intrinsic structure-preserving networks for identifying Hamiltonian systems

This work presents a framework of constructing the neural networks preserving the symplectic structure, so-called symplectic networks (SympNets). With the symplectic networks, we show some numerical results about (\romannumeral1) solving the Hamiltonian systems by learning abundant data points over the phase space, and (\romannumeral2) predicting the phase flows by learning a series of points depending on time. All the experiments point out that the symplectic networks perform much more better than the fully-connected networks that without any prior information, especially in the task of predicting which is unable to do within the conventional numerical methods.
Tangent-Space Gradient Optimization of Tensor Network for Machine Learning

The gradient-based optimization method for deep machine learning models suffers from gradient vanishing and exploding problems, particularly when the computational graph becomes deep. In this work, we propose the tangent-space gradient optimization (TSGO) for probabilistic models to keep the gradients from vanishing or exploding. The central idea is to guarantee the orthogonality between variational parameters and gradients. The optimization is then implemented by rotating the parameter vector towards the direction of gradient. We explain and test TSGO in tensor network (TN) machine learning, where TN describes the joint probability distribution as a normalized state |ψ〉 in Hilbert space. We show that the gradient can be restricted in tangent space of 〈ψ|ψ〉=1 hypersphere. Instead of additional adaptive methods to control the learning rate η in deep learning, the learning rate of TSGO is naturally determined by rotation angle θ as η=tanθ. Our numerical results reveal better convergence of TSGO in comparison to the off-the-shelf Adam.
Achieving Fair Random Access Performance in Massive MIMO Crowded Machine-Type Networks

The use of massive multiple-input multiple-output (MIMO) to serve a crowd of user equipments (UEs) is challenged by the deficit of pilots. Assuming that the UEs are intermittently active, this problem can be addressed by a shared access to the pilots and a suitable random access (RA) protocol. The strongest-user collision resolution (SUCRe) is a previously proposed RA protocol that often privileges the UEs closer to the base station (BS). In contrast, we propose a novel RA protocol using a decentralized pilot power allocation method that aims at a fairer performance. The proposed access class barring with power control (ACBPC) protocol allows each UE to estimate, without additional overhead, how many UEs collided for the chosen pilot and calculate an ACB factor, which is then used to determine the pilot retransmission probability in the next protocol step. The results show that the proposed ACBPC protocol is superior to SUCRe in terms of providing a fair connectivity for very crowded networks, although still being distributed and uncoordinated as the original SUCRe protocol.