Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution

Model quantization is challenging due to many tedious hyper-parameters such as precision (bitwidth), dynamic range (minimum and maximum discrete values) and stepsize (interval between discrete values). Unlike prior arts that carefully tune these values, we present a fully differentiable approach to learn all of them, named Differentiable Dynamic Quantization (DDQ), which has several benefits. (1) DDQ is able to quantize challenging lightweight architectures like MobileNets, where different layers prefer different quantization parameters. (2) DDQ is hardwarefriendly and can be easily implemented using lowprecision matrix-vector multiplication, making it capable in many hardware such as ARM. (3) DDQ reduces training runtime by 25% compared to state-of-the-arts. Extensive experiments show that DDQ outperforms prior arts on many networks and benchmarks, especially when models are already efficient and compact. e.g. DDQ is the first approach that achieves lossless 4-bit quantization for MobileNetV2 on ImageNet.
Measurements of Born cross sections of 
e+e−→Ds*+DsJ−+c.c.

The Born cross sections are measured for the first time for the processes $e^+e^-\to D_s^{*+}D_{s0}^*(2317)^- +c.c. $ and $e^+e^-\to D_s^{*+}D_{s1}(2460)^- +c.c. $ at the center-of-mass energy $\sqrt{s}=$ 4.600~GeV, 4.612~GeV, 4.626~GeV, 4.640~GeV, 4.660~GeV, 4.68~GeV, and 4.700~GeV, and for $e^+e^-\to D_s^{*+}D_{s1}(2536)^- +c.c. $ at $\sqrt{s}=$ 4.660~GeV, 4.680~GeV, and 4.700~GeV, using data samples collected with the BESIII detector at the BEPCII collider. No structures are observed in cross-section distributions for any of the processes.
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels

Optimization plays a key role in the training of deep neural networks. Deciding when to stop training can have a substantial impact on the performance of the network during inference. Under certain conditions, the generalization error can display a double descent pattern during training: the learning curve is non-monotonic and seemingly diverges before converging again after additional epochs. This optimization pattern can lead to early stopping procedures to stop training before the second convergence and consequently select a suboptimal set of parameters for the network, with worse performance during inference. In this work, in addition to confirming that double descent occurs with small datasets and noisy labels as evidenced by others, we show that noisy labels must be present both in the training and generalization sets to observe a double descent pattern. We also show that the learning rate has an influence on double descent, and study how different optimizers and optimizer parameters influence the apparition of double descent. Finally, we show that increasing the learning rate can create an aliasing effect that masks the double descent pattern without suppressing it. We study this phenomenon through extensive experiments on variants of CIFAR-10 and show that they translate to a real world application: the forecast of seizure events in epileptic patients from continuous electroencephalographic recordings.
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms

One of the key drivers of complexity in the classical (stochastic) multi-armed bandit (MAB) problem is the difference between mean rewards in the top two arms, also known as the instance gap. The celebrated Upper Confidence Bound (UCB) policy is among the simplest optimism-based MAB algorithms that naturally adapts to this gap: for a horizon of play n, it achieves optimal O(log n) regret in instances with"large"gaps, and a near-optimal O(\sqrt{n log n}) minimax regret when the gap can be arbitrarily"small. "This paper provides new results on the arm-sampling behavior of UCB, leading to several important insights. Among these, it is shown that arm-sampling rates under UCB are asymptotically deterministic, regardless of the problem complexity. This discovery facilitates new sharp asymptotics and a novel alternative proof for the O(\sqrt{n log n}) minimax regret of UCB. Furthermore, the paper also provides the first complete process-level characterization of the MAB problem under UCB in the conventional diffusion scaling. Among other things, the"small"gap worst-case lens adopted in this paper also reveals profound distinctions between the behavior of UCB and Thompson Sampling, such as an"incomplete learning"phenomenon characteristic of the latter.
The dipper population of Taurus seen with K2

Context. Dippers are typically low-mass, pre-main-sequence stars that display dips in their light curves. These dips have been attributed to dusty warps that form in the inner part of the disk. Aims. Our goal is to derive the characteristics of dipper stars in Taurus to assess the physical mechanisms that induce dipper light curves. Methods. We used the light curves of the fourth and thirteenth campaigns of K2 to select a dipper sample among 179 members and possible members of the Taurus star-forming region based on the light-curve morphology. We studied the periodicities by combining periodograms with wavelet analysis and derived the stellar parameters of the sample from the photometry. We also studied the morphology of the photometric dips. Results. We find a dipper occurrence of ~30% in disk-bearing stars observed with K2 that were identified visually by us. This represents a lower limit to their true occurrence, on the one hand because they are ephemeral, and on the other because there are detection limits. About half of the dippers are aperiodic, and most of these are dominated by another type of variability. The chosen sample is of late spectral type (K/M), low mass (mostly <1 M⊙), and moderate mass accretion rates and has periods of a few days. We observed a transient dipper over a few rotation cycles and observed a dipper with a changing period. The structure of the dips can be complex and varies strongly over timescales of down to one stellar rotation. The corotation radii are located at a few stellar radii, and the temperatures at corotation allow dust survival. Many of the systems are seen at moderate to high inclination. We find that the angular extension of the dusty structure producing the dips is correlated with the stellar period. Conclusions. Magnetospheric accretion, which causes an accretion column and its base to occult the star, can explain most of the observed light curves. Although compatible with the model, many of the stellar inclination angles are moderate and do not exclude mechanisms other than the occultation by an inner disk warp to account for dipper light curves.
Specular reflections removal in colposcopic images based on neural networks: Supervised training with no ground truth previous knowledge

Cervical cancer is a malignant tumor that seriously threatens women’s health, and is one of the most common that affects women worldwide. For its early detection, colposcopic images of the cervix are used for searching for possible injuries or abnormalities. An inherent characteristic of these images is the presence of specular reflections (brightness) that make it difficult to observe some regions, which might imply a misdiagnosis. In this paper, a new strategy based on neural networks is introduced for eliminating specular reflections and estimating the unobserved anatomical cervix portion under the bright zones. We present a supervised learning method, despite not knowing the ground truth from the beginning, based on training a neural network to learn how to restore any hidden region of colposcopic images. Once the specular reflections are identified, they are removed from the image and the previously trained network is used to fulfill these deleted areas. The quality of the processed images was evaluated quantitatively and qualitatively. In 21 of the 22 evaluated images, the detected specular reflections were totally eliminated, whereas, in the remaining one, these reflections were almost completely eliminated. The distribution of the colors and the content of the restored images are similar to those of the originals. The evaluation carried out by a specialist in Cervix Pathology concluded that, after eliminating the specular reflections, the anatomical and physiological elements of the cervix are observable in the restored images, which facilitates the medical diagnosis of cervical pathologies. Our method has the potential to improve the early detection of cervical cancer.
Improved Distributed Lower Bounds for MIS and Bounded (Out-)Degree Dominating Sets in Trees

Recently, Balliu, Brandt, and Olivetti [FOCS '20] showed the first ω(log n) lower bound for the maximal independent set (MIS) problem in trees. In this work we prove lower bounds for a much more relaxed family of distributed symmetry breaking problems. As a by-product, we obtain improved lower bounds for the distributed MIS problem in trees. For a parameter k and an orientation of the edges of a graph G, we say that a subset S of the nodes of G is a k-outdegree dominating set if S is a dominating set of G and if in the induced subgraph G[S], every node in S has outdegree at most k. Note that for k=0, this definition coincides with the definition of an MIS. For a given k, we consider the problem of computing a k-outdegree dominating set. We show that, even in regular trees of degree at most Δ, in the standard ŁOCAL model, there exists a constant ε>0 such that for k ≤Δ^ε, for the problem of computing a k-outdegree dominating set, any randomized algorithm requires at least Ω(min logΔ,√log log n ) rounds and any deterministic algorithm requires at least Ω(min logΔ,√log n ) rounds. The proof of our lower bounds is based on the recently highly successful round elimination technique. We provide a novel way to do simplifications for round elimination, which we expect to be of independent interest. Our new proof is considerably simpler than the lower bound proof in [FOCS '20]. In particular, our round elimination proof uses a family of problems that can be described by only a constant number of labels. The existence of such a proof for the MIS problem was believed impossible by the authors of [FOCS '20].
Physical Properties of Massive Compact Starburst Galaxies with Extreme Outflows

We present results on the nature of extreme ejective feedback episodes and the physical conditions of a population of massive (M * ∼ 1011 M ⊙), compact starburst galaxies at z = 0.4–0.7. We use data from Keck/NIRSPEC, SDSS, Gemini/GMOS, MMT, and Magellan/MagE to measure rest-frame optical and near-IR spectra of 14 starburst galaxies with extremely high star formation rate surface densities (mean ΣSFR ∼ 2000 M ⊙ yr−1 kpc−2) and powerful galactic outflows (maximum speeds v 98 ∼ 1000–3000 km s−1). Our unique data set includes an ensemble of both emission ([O ii] λλ3726,3729, Hβ, [O iii] λλ4959,5007, Hα, [N ii] λλ6549,6585, and [S ii] λλ6716,6731) and absorption (Mg ii λλ2796,2803, and Fe ii λ2586) lines that allow us to investigate the kinematics of the cool gas phase (T ∼ 104 K) in the outflows. Employing a suite of line ratio diagnostic diagrams, we find that the central starbursts are characterized by high electron densities (median n e ∼ 530 cm−3), and high metallicity (solar or supersolar). We show that the outflows are most likely driven by stellar feedback emerging from the extreme central starburst, rather than by an AGN. We also present multiple intriguing observational signatures suggesting that these galaxies may have substantial Lyman continuum (LyC) photon leakage, including weak [S ii] nebular emission lines. Our results imply that these galaxies may be captured in a short-lived phase of extreme star formation and feedback where much of their gas is violently blown out by powerful outflows that open up channels for LyC photons to escape.
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks. Its success is partly because of parameter sharing among agents. However, such sharing may lead agents to behave similarly and limit their coordination capacity. In this paper, we aim to introduce diversity in both optimization and representation of shared multi-agent reinforcement learning. Specifically, we propose an information-theoretical regularization to maximize the mutual information between agents' identities and their trajectories, encouraging extensive exploration and diverse individualized behaviors. In representation, we incorporate agent-specific modules in the shared neural network architecture, which are regularized by L1-norm to promote learning sharing among agents while keeping necessary diversity. Empirical results show that our method achieves state-of-the-art performance on Google Research Football and super hard StarCraft II micromanagement tasks.
Time-dependent relations between gaps and returns in a Bitcoin order book

Several studies have shown that large changes in the returns of an asset are associated with the sized of the gaps present in the order book. In general, these associations have been studied without explicitly considering the dynamics of either gaps or returns. Here we present a study of these relationships. Our results suggest that the causal relationship between gaps and returns is limited to instantaneous causation.
cs60075_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora

The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits

In this paper we investigate the problem of stochastic multi-armed bandits (MAB) in the (local) differential privacy (DP/LDP) model. Unlike previous results that assume bounded/sub-Gaussian reward distributions, we focus on the setting where each arm's reward distribution only has $(1+v)$-th moment with some $v\in (0, 1]$. In the first part, we study the problem in the central $\epsilon$-DP model. We first provide a near-optimal result by developing a private and robust Upper Confidence Bound (UCB) algorithm. Then, we improve the result via a private and robust version of the Successive Elimination (SE) algorithm. Finally, we establish the lower bound to show that the instance-dependent regret of our improved algorithm is optimal. In the second part, we study the problem in the $\epsilon$-LDP model. We propose an algorithm that can be seen as locally private and robust version of SE algorithm, which provably achieves (near) optimal rates for both instance-dependent and instance-independent regret. Our results reveal differences between the problem of private MAB with bounded/sub-Gaussian rewards and heavy-tailed rewards. To achieve these (near) optimal rates, we develop several new hard instances and private robust estimators as byproducts, which might be used to other related problems. Finally, experiments also support our theoretical findings and show the effectiveness of our algorithms.
Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

Plant species identification in the wild is a difficult problem in part due to the high variability of the input data, but also because of complications induced by the long-tail effects of the datasets distribution. Inspired by the most recent fine-grained visual classification approaches which are based on attention to mitigate the effects of data variability, we explore the idea of using object detection as a form of attention. We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers. We also curate a new dataset with a long-tail distribution for evaluating plant organ detection and organ-based species identification, which is publicly available.
Retrieve & Memorize: Dialog Policy Learning with Multi-Action Memory

Dialogue policy learning, a subtask that determines the content of system response generation and then the degree of task completion, is essential for task-oriented dialogue systems. However, the unbalanced distribution of system actions in dialogue datasets often causes difficulty in learning to generate desired actions and responses. In this paper, we propose a retrieve-and-memorize framework to enhance the learning of system actions. Specially, we first design a neural context-aware retrieval module to retrieve multiple candidate system actions from the training set given a dialogue context. Then, we propose a memory-augmented multi-decoder network to generate the system actions conditioned on the candidate actions, which allows the network to adaptively select key information in the candidate actions and ignore noises. We conduct experiments on the large-scale multi-domain task-oriented dialogue dataset MultiWOZ 2.0 and MultiWOZ 2.1. Experimental results show that our method achieves competitive performance among several state-of-the-art models in the context-to-response generation task.
A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving

Accurate localization is of crucial importance for autonomous driving tasks. Nowadays, we have seen a lot of sensor-rich vehicles (e.g. Robo-taxi) driving on the street autonomously, which rely on high-accurate sensors (e.g. Lidar and RTK GPS) and high-resolution map. However, low-cost production cars cannot afford such high expenses on sensors and maps. How to reduce costs? How do sensor-rich vehicles benefit low-cost cars? In this paper, we proposed a light-weight localization solution, which relies on low-cost cameras and compact visual semantic maps. The map is easily produced and updated by sensor-rich vehicles in a crowd-sourced way. Specifically, the map consists of several semantic elements, such as lane line, crosswalk, ground sign, and stop line on the road surface. We introduce the whole framework of on-vehicle mapping, on-cloud maintenance, and user-end localization. The map data is collected and preprocessed on vehicles. Then, the crowd-sourced data is uploaded to a cloud server. The mass data from multiple vehicles are merged on the cloud so that the semantic map is updated in time. Finally, the semantic map is compressed and distributed to production cars, which use this map for localization. We validate the performance of the proposed map in real-world experiments and compare it against other algorithms. The average size of the semantic map is 36 kb/km. We highlight that this framework is a reliable and practical localization solution for autonomous driving.
Synthesis of New Metallized Polyimide Films with High Optical and Physical Performances

Electroconductive and reflective metallized polyimide films have been prepared by heterogeneous chemical modification of polyimide surface. By carrying out the chemical reactions in situ in the modified layers of polyimide surface, a metal phase strongly impregnated into the polyimide surface is obtained. The steps of chemical modification have been studied on the model compound вЂ“ poly(amic acid) on the basis of isophthaloylchloride and methylenedianthranilic acid which forms insoluble sodium or potassium poly(amicacid) salts (polyamate). Metallization of Kapton HN & JP (from DuPont) and Upilex S (from Ube) films has been carried out and the films have been characterized by X-ray diffraction (XRD), X-ray fine diffraction (XRFD), measurements of reflectivity in the visible range and surface resistivity at elevated temperatures. It is shown that reflectivity coefficients of silvered films are 90-92% and surface resistivity is about 0.5 О©/sq.
Fair Exploration via Axiomatic Bargaining

Exploration is often necessary in online learning to maximize long-term rewards, but it comes at the cost of short-term “regret. ” We study how this cost of exploration is shared across multiple groups. For example, in a clinical trial setting, patients who are assigned a suboptimal treatment effectively incur the cost of exploration. When patients are associated with natural groups on the basis of, say, race or age, it is natural to ask whether the cost of exploration borne by any single group is “fair. ” So motivated, we introduce the “grouped” bandit model. We leverage the theory of axiomatic bargaining, and the Nash bargaining solution in particular, to formalize what might constitute a fair division of the cost of exploration across groups. On one hand, we show that any regret-optimal policy strikingly results in the least fair outcome: such policies will perversely leverage the most “disadvantaged” groups when they can. More constructively, we derive policies that are optimally fair and simultaneously enjoy a small “price of fairness. ” We illustrate the relative merits of our algorithmic framework with a case study on contextual bandits for warfarin dosing where we are concerned with the cost of exploration across multiple races and age groups. This paper was accepted by David Simchi-Levi, data science. Funding: This work was supported by the National Science Foundation, Division of Civil, Mechanical and Manufacturing Innovation [Grant 1727239]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022. 01985 .
Imaging Spin‐Wave Damping Underneath Metals Using Electron Spins in Diamond

Spin waves in magnetic insulators are low‐damping signal carriers that can enable a new generation of spintronic devices. The excitation, control, and detection of spin waves by metal electrodes is crucial for interfacing these devices to electrical circuits. As such, it is important to understand metal‐induced damping of spin‐wave transport, but characterizing this process requires access to the underlying magnetic films. Here it is shown that electronic sensor spins in diamond enable imaging of spin waves that propagate underneath metals in magnetic insulators. This capability is then used to reveal a 100‐fold metal‐induced increase in spin‐wave damping. The damping enhancement is attributed to spin‐wave‐induced electrical currents as well as, above a certain frequency, three‐magnon scattering processes. This interpretation is supported by deriving expressions for the current‐induced damping and the three‐magnon threshold from the Landau–Lifshitz–Gilbert equation that agree well with the observations. The detection of buried scattering centers further highlights the technique's power for assessing spintronic device quality. These results open new avenues for studying metal – spin‐wave interactions and provide access to interfacial processes such as spin‐wave injection via the spin‐Hall effect.