Miracle-dz/UniFAR-FTUs huggingface dataset:
Neutrinos as a probe of the Universe

The group theory of SM unification can be written as SU(3) × SU(2)L × U(1), the “S” indicating that there are N − 1 force particles, one removed from the special (Det = +1) unitary group U(N), while the matter particles of fermions (half-integral quantum mechanical spin) sit in irreducible representations of these three groups [3–7]. Not included is gravity, which cannot be a gauge theory as it is not renormalizable. Gravity/general relativity is a geometric theory [8]. Thus, we have 8 gluons, 3 W bosons, and a singlet “photon. ” Adding a complex doublet of Higgs particles to break the SU(2)L×U(1) symmetry completes the SM [9]. The SU(3) is called QCD, short for quantum chromodynamics, and the SU(2)L × U(1) when suitably broken will contain quantum electrodynamics (or QED) and weak force.
Time Evolution of Quantum Effects

For quantum effects a and b we define the a-evolution of b at time t denoted by b(t | a). We interpret b(t | a) as the influence that a has on b at time t when a occurs, but is not measured at time t = 0. Using b(t | a) we define the time-dependent sequential product a[t]b. This is interpreted as an effect that results from first measuring a and then measuring b after a time delay t. Various properties of a[t]b are derived and it is shown that a[t]b is constant in time if and only if a and b commute or a is a multiple of a projection. These concepts are extended to observables for a quantum system. The ideas are illustrated with some examples.
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting

Low-complexity models such as linear function representation play a pivotal role in enabling sample-efficient reinforcement learning (RL). The current paper pertains to a scenario with value-based linear representation, which postulates the linear realizability of the optimal Q-function (also called the"linear $Q^{\star}$ problem"). While linear realizability alone does not allow for sample-efficient solutions in general, the presence of a large sub-optimality gap is a potential game changer, depending on the sampling mechanism in use. Informally, sample efficiency is achievable with a large sub-optimality gap when a generative model is available but is unfortunately infeasible when we turn to standard online RL settings. In this paper, we make progress towards understanding this linear $Q^{\star}$ problem by investigating a new sampling protocol, which draws samples in an online/exploratory fashion but allows one to backtrack and revisit previous states in a controlled and infrequent manner. This protocol is more flexible than the standard online RL setting, while being practically relevant and far more restrictive than the generative model. We develop an algorithm tailored to this setting, achieving a sample complexity that scales polynomially with the feature dimension, the horizon, and the inverse sub-optimality gap, but not the size of the state/action space. Our findings underscore the fundamental interplay between sampling protocols and low-complexity structural representation in RL.
Regret Minimization Experience Replay

Experience replay is widely used in various deep off-policy reinforcement learning (RL) algorithms. It stores previously collected samples for further reuse. To better utilize these samples, prioritized sampling is a promising technique to improve the performance of RL agents. Previous prioritization methods based on temporal-difference (TD) error are highly heuristic and divergent from the objective of RL. In this work, we analyze the optimal prioritization strategy that can minimize the regret of RL policy theoretically. Our theory suggests that the data with higher TD error, better on-policiness and more corrective feedback should be assigned with higher weights during sampling. Based on this theory, we propose two practical algorithms, RM-DisCor and RM-TCE. RM-DisCor is a general algorithm and RM-TCE is a more efﬁcient variant relying on the temporal ordering of states. Both algorithms improve the performance of off-policy RL algorithms in challenging RL benchmarks, including MuJoCo , Atari and Meta-World .
Stage-wise Fine-tuning for Graph-to-Text Generation

Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders. However, they fail to fully utilize the structure information of the input graph. In this paper, we aim to further improve the performance of the pre-trained language model by proposing a structured graph-to-text model with a two-step fine-tuning mechanism which first fine-tunes model on Wikipedia before adapting to the graph-to-text generation. In addition to using the traditional token and position embeddings to encode the knowledge graph (KG), we propose a novel tree-level embedding method to capture the inter-dependency structures of the input graph. This new approach has significantly improved the performance of all text generation metrics for the English WebNLG 2017 dataset.
Dynamic Routing and Spectrum Assignment Based on the Consecutive Sub-Channels in Flexible-Grid Optical Networks

Variable bandwidth channels can be created in Flexible Grid Optical Networks using Optical Orthogonal Frequency Division Multiplexing (O-OFDM). This allows more efficient spectrum use by allocating integral multiple of basic bandwidth slots (sub-channels) to the lightpath requests. In these networks, the constraint of keeping all the allocated slots together is added when deciding routes for the requests. This makes the Routing and Spectrum Assignment (RSA) algorithms in Flexible Grid Optical Networks more challenging. In any network, the lightpath requests will arrive and depart dynamically, leading to spectrum fragmentation. It leads to a reduction of maximum possible utilization as well as an increase in blocking probability. In this paper, we have presented an improvised RSA algorithm using consecutive spectrum slots as an adaptive parameter, that leads to reduced fragmentation. It is evident from the results that the presented RSA algorithm using adaptive parameters reduces the blocking probability and fragmentation more effectively than the shortest path and k-shortest path algorithms reported in the earlier works.
Deep regression for uncertainty-aware and interpretable analysis of large-scale body MRI

Large-scale medical studies such as the UK Biobank examine thousands of volunteer participants with medical imaging techniques. Combined with the vast amount of collected metadata, anatomical information from these images has the potential for medical analyses at unprecedented scale. However, their evaluation often requires manual input and long processing times, limiting the amount of reference values for biomarkers and other measurements available for research. Recent approaches with convolutional neural networks for regression can perform these evaluations automatically. On magnetic resonance imaging (MRI) data of more than 40,000 UK Biobank subjects, these systems can estimate human age, body composition and more. This style of analysis is almost entirely data-driven and no manual intervention or guidance with manually segmented ground truth images is required. The networks often closely emulate the reference method that provided their training data and can reach levels of agreement comparable to the expected variability between established medical gold standard techniques. The risk of silent failure can be individually quantified by predictive uncertainty obtained from a mean-variance criterion and ensembling. Saliency analysis furthermore enables an interpretation of the underlying relevant image features and showed that the networks learned to correctly target specific organs, limbs, and regions of interest.
Monomial size vs. Bit-complexity in Sums-of-Squares and Polynomial Calculus

In this paper we consider the relationship between monomial-size and bit-complexity in Sums-of-Squares (SOS) in Polynomial Calculus Resolution over rationals $({\text{PCR}}/\mathbb{Q})$. We show that there is a set of polynomial constraints Qn over Boolean variables that has both SOS and ${\text{PCR}}/\mathbb{Q}$ refutations of degree 2 and thus with only polynomially many monomials, but for which any SOS or ${\text{PCR}}/\mathbb{Q}$ refutation must have exponential bit-complexity, when the rational coefficients are represented with their reduced fractions written in binary.
Continual Learning with Echo State Networks

Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge. The study of CL for sequential patterns revolves around trained recurrent networks. In this work, instead, we introduce CL in the context of Echo State Networks (ESNs), where the recurrent component is kept fixed. We provide the first evaluation of catastrophic forgetting in ESNs and we highlight the benefits in using CL strategies which are not applicable to trained recurrent models. Our results confirm the ESN as a promising model for CL and open to its use in streaming scenarios.
The Boombox: Visual Reconstruction from Acoustic Vibrations

Interacting with bins and containers is a fundamental task in robotics, making state estimation of the objects inside the bin critical. While robots often use cameras for state estimation, the visual modality is not always ideal due to occlusions and poor illumination. We introduce The Boombox, a container that uses sound to estimate the state of the contents inside a box. Based on the observation that the collision between objects and its containers will cause an acoustic vibration, we present a convolutional network for learning to reconstruct visual scenes. Although we use low-cost and low-power contact microphones to detect the vibrations, our results show that learning from multimodal data enables state estimation from affordable audio sensors. Due to the many ways that robots use containers, we believe the box will have a number of applications in robotics. Our project website is at: boombox.cs. columbia.edu
Left Dihedral Codes over Finite Chain Rings

Let R be a finite commutative chain ring, D2n be the dihedral group of size 2n and R[D2n] be the dihedral group ring. In this paper, we completely characterize left ideals of R[D2n] (called left D2n-codes) when gcd(char(R), n) = 1. In this way, we explore the structure of some skewcyclic codes of length 2 over R and also over R × S, where S is an isomorphic copy of R. As a particular result, we give the structure of cyclic codes of length 2 over R. In the case where R = Fpm is a Galois field, we give a classification for left D2N -codes over Fpm , for any positive integer N . In both cases we determine dual codes and identify self-dual ones.
SA-GAN: Structure-Aware Generative Adversarial Network for Shape-Preserving Synthetic CT Generation

In medical image synthesis, model training could be challenging due to the inconsistencies between images of diﬀerent modalities even with the same patient, typically caused by internal status/tissue changes as diﬀerent modalities are usually obtained at a diﬀerent time. This paper proposes a novel deep learning method, Structure-aware Generative Adversarial Network (SA-GAN), that preserves the shapes and locations of in-consistent structures when generating medical images. SA-GAN is employed to generate synthetic computed tomography (synCT) images from magnetic resonance imaging (MRI) with two parallel streams: the global stream translates the input from the MRI to the CT domain while the local stream automatically segments the inconsistent organs, maintains their locations and shapes in MRI, and translates the organ intensities to CT. Through extensive experiments on a pelvic dataset, we demonstrate that SA-GAN provides clinically acceptable accuracy on both synCTs and organ segmentation and supports MR-only treatment planning in disease sites with internal organ status changes.
Approximate solutions to the Travelling Salesperson Problem on semimetric graphs

With the aid of the relaxed polygonal inequality (introduced by Fagin et al.) we strive to extend the applicability of Christofides approximation technique to the scope of all complete finite weighted graphs with positive weights. First section acquaints the Reader with the class of semimetric graphs and proves that every finite graph admits γ-polygon structure. Sections 2 and 3 establish the necessary notions from the graph and optimization theory to tackle the Traveling Salesperson Problem. In section 4 the minimal spanning tree method is introduced, while section 5 focuses on the analysis of this method through the lens of γ-polygon graphs. The final section of the paper adjusts the technique of Christofides by obtaining 3γ 2 −approximation for the TSP.
A unified approach to mean-field team: homogeneity, heterogeneity and quasi-exchangeability

This paper aims to systematically solve stochastic team optimization of large-scale system, in a rather general framework. Concretely, the underlying large-scale system involves considerable weaklycoupled cooperative agents for which the individual admissible controls: (i) enter the diffusion terms, (ii) are constrained in some closed-convex subsets, and (iii) subject to a general partial decentralized information structure. A more important but serious feature: (iv) all agents are heterogenous with continuum instead finite diversity. Combination of (i)-(iv) yields a quite general modeling of stochastic team-optimization, but on the other hand, also fails current existing techniques of team analysis. In particular, classical team consistency with continuum heterogeneity collapses because of (i). As the resolution, a novel unified approach is proposed under which the intractable continuum heterogeneity can be converted to a more tractable homogeneity. As a trade-off, the underlying randomness is augmented, and all agents become (quasi) weakly-exchangeable. Such approach essentially involves a subtle balance between homogeneity v.s. heterogeneity, and left (prior-sampling)v.s. right (posterior-sampling) information filtration. Subsequently, the consistency condition (CC) system takes a new type of forward-backward stochastic system with double-projections (due to (ii), (iii)), along with spatial mean on continuum heterogenous index (due to (iv)). Such system is new in team literature and its well-posedness is also challenging. We address this issue under mild conditions. Related asymptotic optimality is also established.
Autonomous Experiments for Neutron Three-Axis Spectrometers (TAS) with Log-Gaussian Processes

Autonomous experiments are excellent tools to increase the efficiency of material discovery. Indeed, AI and ML methods can help optimizing valuable experimental resources as, for example, beam time in neutron scattering experiments, in addition to scientists’ knowledge and experience. Active learning methods form a particular class of techniques that acquire knowledge on a specific quantity of interest by autonomous decisions on what or where to investigate next based on previous measurements. For instance, Gaussian Process Regression (GPR) [4] is a well-known technique that can be exploited to accomplish active learning tasks for scattering experiments as was recently demonstrated [1, 2]. Gaussian processes are not only capable to approximate functions by their posterior mean function, but can also quantify uncertainty about the approximation itself. Hence, if we perform function evaluations at locations of highest uncertainty, the function can be “optimally” learned in an iterative manner; see Fig. 1.
Sound Event Detection with Adaptive Frequency Selection

In this work, we present HIDACT, a novel network architecture for adaptive computation for efficiently recognizing acoustic events. We evaluate the model on a sound event detection task where we train it to adaptively process frequency bands. The model learns to adapt to the input without requesting all frequency sub-bands provided. It can make confident predictions within fewer processing steps, hence reducing the amount of computation. Experimental results show that HIDACT has comparable performance to baseline models with more parameters and higher computational complexity. Furthermore, the model can adjust the amount of computation based on the data and computational budget.
PPCA: Privacy-preserving Principal Component Analysis Using Secure Multiparty Computation(MPC)

Privacy-preserving data mining has become an important topic. People have built several multi-party-computation (MPC)-based frameworks to provide theoretically guaranteed privacy, the poor performance of real-world algorithms have always been a challenge. Using Principal Component Analysis (PCA) as an example, we show that by considering the unique performance characters of the MPC platform, we can design highly effective algorithm-level optimizations, such as replacing expensive operators and batching up. We achieve about 200$\times$ performance boost over existing privacy-preserving PCA algorithms with the same level of privacy guarantee. Also, using real-world datasets, we show that by combining multi-party data, we can achieve better training results.
Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos

Aerial pixel-wise scene perception of the surrounding environment is an important task for UAVs (Unmanned Aerial Vehicles). Previous research works mainly adopt conventional pinhole cameras or fisheye cameras as the imaging device. However, these imaging systems cannot achieve large Field of View (FoV), small size, and lightweight at the same time. To this end, we design a UAV system with a Panoramic Annular Lens (PAL), which has the characteristics of small size, low weight, and a 360° annular FoV. A lightweight panoramic annular semantic segmentation neural network model is designed to achieve high-accuracy and real-time scene parsing. In addition, we present the first drone-perspective panoramic scene segmentation dataset Aerial-PASS, with annotated labels of track, field, and others. A comprehensive variety of experiments shows that the designed system performs satisfactorily in aerial panoramic scene parsing. In particular, our proposed model strikes an excellent trade-off between segmentation performance and inference speed, validated on both public street-scene and our established aerial-scene datasets.
Analysis of Bitcoin Vulnerability to Bribery Attacks Launched Through Large Transactions

Bitcoin uses blockchain technology to maintain transactions order and provides probabilistic guarantee to prevent double-spending, assuming that an attacker's computational power does not exceed %50 of the network power. In this paper, we design a novel bribery attack and show that this guarantee can be hugely undermined. Miners are assumed to be rational in this setup and they are given incentives that are dynamically calculated. In this attack, the adversary misuses the Bitcoin protocol to bribe miners and maximize their gained advantage. We will reformulate the bribery attack to propose a general mathematical foundation upon which we build multiple strategies. We show that, unlike Whale Attack, these strategies are practical. If the rationality assumption holds, this shows how vulnerable blockchain-based systems like Bitcoin are. We suggest a soft fork on Bitcoin to fix this issue at the end.
Dynamic Proportional Rankings

Proportional ranking rules aggregate approval-style preferences of agents into a collective ranking such that groups of agents with similar preferences are adequately represented. Motivated by the application of live Q&A platforms, where submitted questions need to be ranked based on the interests of the audience, we study a dynamic extension of the proportional rankings setting. In our setting, the goal is to maintain the proportionality of a ranking when alternatives (i.e., questions)---not necessarily from the top of the ranking---get selected sequentially. We propose generalizations of well-known aggregation rules to this setting and study their monotonicity and proportionality properties. We also evaluate the performance of these rules experimentally, using realistic probabilistic assumptions on the selection procedure.
MUSE-ALMA haloes VI: coupling atomic, ionized, and molecular gas kinematics of galaxies

We present results of MUSE-ALMA haloes, an ongoing study of the circumgalactic medium (CGM) of galaxies (z ≤ 1.4). Using multiphase observations we probe the neutral, ionized, and molecular gas in a subsample containing six absorbers and nine associated galaxies in the redshift range z ∼ 0.3–0.75. Here, we give an in-depth analysis of the newly CO-detected galaxy Q2131−G1 (z = 0.42974), while providing stringent mass and depletion time limits for the non-detected galaxies. Q2131−G1 is associated with an absorber with column densities of log(NH i/cm−2) ∼ 19.5 and $\textrm {log}(N_{\textrm {H}_2}/\textrm {cm}^{-2}) \sim 16.5$, and has a star formation rate of SFR = 2.00 ± 0.20 M⊙yr−1, a dark matter fraction of fDM(r1/2) = 0.24–0.54, and a molecular gas mass of $M_\textrm {mol} = 3.52 ^{+3.95}_{-0.31} \times 10^9 \,\, \textrm {M}_{\odot }$ resulting in a depletion time of τdep < 4.15 Gyr. Kinematic modelling of both the CO (3–2) and [O iii] λ5008 emission lines of Q2131−G1 shows that the molecular and ionized gas phases are well aligned directionally and that the maximum rotation velocities closely match. These two gas phases within the disc are strongly coupled. The metallicity, kinematics, and orientation of the atomic and molecular gas traced by a two-component absorption feature are consistent with being part of the extended rotating disc with a well-separated additional component associated with infalling gas. Compared to emission-selected samples, we find that H i-selected galaxies have high molecular gas masses given their low star formation rate. We consequently derive high depletion times for these objects.
openwifi CSI fuzzer for authorized sensing and covert channels

CSI (Channel State Information) of WiFi systems contains the environment channel response between the transmitter and the receiver, so the people/objects and their movement in between can be sensed. To get CSI, the receiver performs channel estimation based on the pre-known training field of the transmitted WiFi signal. CSI related technology is useful in many cases, but it also brings concerns on privacy and security. In this paper, we open sourced a CSI fuzzer to enhance the privacy and security of WiFi CSI applications. It is built and embedded into the transmitter of openwifi, which is an open source full-stack WiFi chip design, to prevent unauthorized sensing without sacrificing the WiFi link performance. The CSI fuzzer imposes an artificial channel response to the signal before it is transmitted, so the CSI seen by the receiver will indicate the actual channel response combined with the artificial response. Only the authorized receiver, that knows the artificial response, can calculate the actual channel response and perform the CSI sensing. Another potential application of the CSI fuzzer is covert channels based on a set of pre-defined artificial response patterns. Our work resolves the pain point of implementing the anti-sensing idea based on the commercial off-the-shelf WiFi devices.