Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distributional View on Multi-Objective Policy Optimization

Many real-world problems require trading off multiple competing objectives. However, these objectives are often in different units and/or scales, which can make it challenging for practitioners to express numerical preferences over objectives in their native units. In this paper we propose a novel algorithm for multi-objective reinforcement learning that enables setting desired preferences for objectives in a scale-invariant way. We propose to learn an action distribution for each objective, and we use supervised learning to fit a parametric policy to a combination of these distributions. We demonstrate the effectiveness of our approach on challenging high-dimensional real and simulated robotics tasks, and show that setting different preferences in our framework allows us to trace out the space of nondominated solutions.
Longitudinal and transverse electron paramagnetic resonance in a scanning tunneling microscope

Longitudinal and transverse magnetic field gradients drive single-atom EPR in an STM owing to piezoelectric excitations. Electron paramagnetic resonance (EPR) spectroscopy is widely used to characterize paramagnetic complexes. Recently, EPR combined with scanning tunneling microscopy (STM) achieved single-spin sensitivity with sub-angstrom spatial resolution. The excitation mechanism of EPR in STM, however, is broadly debated, raising concerns about widespread application of this technique. We present an extensive experimental study and modeling of EPR-STM of Fe and hydrogenated Ti atoms on a MgO surface. Our results support a piezoelectric coupling mechanism, in which the EPR species oscillate adiabatically in the inhomogeneous magnetic field of the STM tip. An analysis based on Bloch equations combined with atomic-multiplet calculations identifies different EPR driving forces. Specifically, transverse magnetic field gradients drive the spin-1/2 hydrogenated Ti, whereas longitudinal magnetic field gradients drive the spin-2 Fe. Also, our results highlight the potential of piezoelectric coupling to induce electric dipole moments, thereby broadening the scope of EPR-STM to nonpolar species and nonlinear excitation schemes.
Order out of chaos: Shifting paradigm of convective turbulence

Turbulence is ever produced in the low-viscosity/large-scale fluid flows by the velocity shears and, in unstable stratification, by buoyancy forces. It is commonly believed that both mechanisms produce the same type of chaotic motions, namely, the eddies breaking down into smaller ones and producing direct cascade of turbulent kinetic energy and other properties from large to small scales towards viscous dissipation. The conventional theory based on this vision yields a plausible picture of vertical mixing and remains in use since the middle of the 20th century in spite of increasing evidence of the fallacy of almost all other predictions. This paper reveals that in fact buoyancy produces chaotic vertical plumes, merging into larger ones and producing an inverse cascade towards their conversion into the self-organized regular motions. Herein, the velocity shears produce usual eddies spreading in all directions and making the direct cascade. This new paradigm is demonstrated and proved empirically; so, the paper launches a comprehensive revision of the theory of unstably stratified turbulence and its numerous geophysical or astrophysical applications.
Fmoc-Based Synthesis of Peptide-αThioesters: Application to the Total Chemical Synthesis of a Glycoprotein by Native Chemical Ligation

The technique of native chemical ligation has enabled the total chemical synthesis of proteins with molecular weights far in excess of those achievable by conventional stepwise solid-phase peptide synthesis. The method involves the condensation of two unprotected peptide segments, one bearing a C-terminal αthioester and the other an N-terminal cysteine residue, to afford a protein with a native amide linkage at the site of ligation. Here we report an extension of the native chemical ligation method to the total synthesis of a glycosylated protein, the antimicrobial O-linked glycoprotein diptericin. The major challenge in our synthesis was preparation of a 24-residue glycopeptide-αthioester segment, which was complicated by the incompatibility of glycosidic linkages with Boc chemistry and by the incompatibility of thioesters with Fmoc chemistry. The use of an alkanesulfonamide “safety-catch” linker circumvented this problem and permitted the solid-phase synthesis of the glycopeptide-αthioester using standa.
Context-Dependent Acoustic Modeling without Explicit Phone Clustering

Phoneme-based acoustic modeling of large vocabulary automatic speech recognition takes advantage of phoneme context. The large number of context-dependent (CD) phonemes and their highly varying statistics require tying or smoothing to enable robust training. Usually, Classification and Regression Trees are used for phonetic clustering, which is standard in Hidden Markov Model (HMM)-based systems. However, this solution introduces a secondary training objective and does not allow for end-to-end training. In this work, we address a direct phonetic context modeling for the hybrid Deep Neural Network (DNN)/HMM, that does not build on any phone clustering algorithm for the determination of the HMM state inventory. By performing different decompositions of the joint probability of the center phoneme state and its left and right contexts, we obtain a factorized network consisting of different components, trained jointly. Moreover, the representation of the phonetic context for the network relies on phoneme embeddings. The recognition accuracy of our proposed models on the Switchboard task is comparable and outperforms slightly the hybrid model using the standard state-tying decision trees.
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness

As a certified defensive technique, randomized smoothing has received considerable attention due to its scalability to large datasets and neural networks. However, several important questions remain unanswered, such as (i) whether the Gaussian mechanism is an appropriate option for certifying $\ell_2$-norm robustness, and (ii) whether there is an appropriate randomized (smoothing) mechanism to certify $\ell_\infty$-norm robustness. To shed light on these questions, we argue that the main difficulty is how to assess the appropriateness of each randomized mechanism. In this paper, we propose a generic framework that connects the existing frameworks in \cite{lecuyer2018certified, li2019certified}, to assess randomized mechanisms. Under our framework, for a randomized mechanism that can certify a certain extent of robustness, we define the magnitude of its required additive noise as the metric for assessing its appropriateness. We also prove lower bounds on this metric for the $\ell_2$-norm and $\ell_\infty$-norm cases as the criteria for assessment. Based on our framework, we assess the Gaussian and Exponential mechanisms by comparing the magnitude of additive noise required by these mechanisms and the lower bounds (criteria). We first conclude that the Gaussian mechanism is indeed an appropriate option to certify $\ell_2$-norm robustness. Surprisingly, we show that the Gaussian mechanism is also an appropriate option for certifying $\ell_\infty$-norm robustness, instead of the Exponential mechanism. Finally, we generalize our framework to $\ell_p$-norm for any $p\geq2$. Our theoretical findings are verified by evaluations on CIFAR10 and ImageNet.
Photometric study of the young open clusters IC 1442, King 21, and Trumpler 7

We carried out UBVRcIc photometric study of three poorly studied young open clusters IC 1442, King 21, and Trumpler 7 (Tr 7). We obtained 263, 244, and 128 member stars using Gaia DR2 proper motions and parallaxes in IC 1442, King 21, and Tr 7, respectively. The reddening, E(B-V), was derived to be 0.54+/-0.04, 0.76+/-0.06, and 0.38+/-0.04 mag for these clusters. The comparison of observed colour-magnitude diagrams (CMDs) with solar metallicity isochrones yields log(Age) = 7.40+/-0.30, 7.70+/-0.20, and 7.85+/-0.25 yr and corresponding distances 2847+/-238, 2622+/-156, and 1561+/-74 pc for IC 1442, King 21, and Tr 7, respectively. The estimated mass function (MF) slopes are found to be -1.94+/-0.18, -1.54+/-0.32, and -2.31+/-0.29 for IC 1442, King 21, and Tr 7, respectively. The study of MF slopes determined separately in the inner and the outer regions of these clusters gives a steeper slope in outer region which suggests spatial variation in slope and mass segregation in the clusters. We found evidence of mass segregation after dynamical study in these clusters. The obtained relaxation time is 74, 26, and 34 Myr for the clusters IC 1442, King 21, and Tr 7, respectively. The mass segregation in IC 1442 may be caused by early dynamical relaxation. The estimated relaxation time is well below to the ages of King 21 and Tr 7 which indicates that these clusters are dynamically relaxed.
Statistical Equity: A Fairness Classification Objective

Machine learning systems have been shown to propagate the societal errors of the past. In light of this, a wealth of research focuses on designing solutions that are "fair. " Even with this abundance of work, there is no singular definition of fairness, mainly because fairness is subjective and context dependent. We propose a new fairness definition, motivated by the principle of equity, that considers existing biases in the data and attempts to make equitable decisions that account for these previous historical biases. We formalize our definition of fairness, and motivate it with its appropriate contexts. Next, we operationalize it for equitable classification. We perform multiple automatic and human evaluations to show the effectiveness of our definition and demonstrate its utility for aspects of fairness, such as the feedback loop.
How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy

We consider the problem of hiding wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect whether any transmission of interest is present or not. There exists one transmitter that transmits to its receiver in the presence of an eavesdropper, while a cooperative jammer (CJ) transmits carefully crafted adversarial perturbations over the air to fool the eavesdropper into classifying the received superposition of signals as noise. The CJ puts an upper bound on the strength of perturbation signal to limit its impact on the bit error rate (BER) at the receiver. We show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with high probability while increasing the BER only slightly. On the other hand, the CJ cannot fool the eavesdropper by simply transmitting Gaussian noise as in conventional jamming and instead needs to craft perturbation signals built by adversarial machine learning to enable covert communications. Our results show that signals with different modulation types and eventually 5G communications can be effectively hidden from an eavesdropper even if it is equipped with a DL classifier to detect transmissions.
Epileptic Seizure Forecasting: Probabilistic seizure-risk assessment and data-fusion.

Epileptic seizure forecasting, combined with the delivery of preventative therapies, holds the potential to greatly improve the quality of life for epilepsy patients and their caregivers. Forecasting seizures could prevent some potentially catastrophic consequences such as injury and death in addition to a long list of potential clinical benefits it may provide for patient care in hospitals. The challenge of seizure forecasting lies within the seemingly unpredictable transitions of brain dynamics into the ictal state. The main body of computational research on determining seizure risk has been focused solely on prediction algorithms, which involves a remarkable issue of balancing accuracy and false-alarms. In this paper, we developed a seizure-risk warning system that employs Bayesian convolutional neural network (BCNN) to provide meaningful information to the patient and provide a greater opportunity for him/her to be potentially more in charge of his/her health. We use scalp electroencephalogram (EEG) signals and release information on the certainty of our automatic seizure-risk assessment. In the process, we pave the ground-work towards incorporating auxiliary signals to improve our EEG-based seizure-risk assessment system. Our previous CNN results show an average AUC of 74.65% while we could achieve on an EEG-only BCNN an average AUC of 68.70%. This drop in performance is the cost of providing richer information to the patient at this stage of this research.
Decoding of NB-LDPC Codes Over Subfields

Non-binary low-density parity-check (NB-LDPC) codes can offer promising performance advantages but suffer from high decoding complexity. To tackle this challenge, in this paper, we consider NB-LDPC codes over finite fields as codes over subfields as a means of reducing decoding complexity. In particular, our approach is based on a novel method of expanding a non-binary Tanner graph over a finite field into a graph over a subfield. This approach offers several decoding strategies for a single NB-LDPC code, with varying levels of performance-complexity trade-offs. Simulation results demonstrate that in a majority of cases, performance loss is minimal when compared with the complexity gains.
Explicit matrix coefficients and test vectors for discrete series representations

For the discrete series representations of ${\rm GL}(n)$ over a non-archimedean local field $F$, we define a notion of functions similar to "zonal spherical functions" for unramified principal series. We prove the existence of such functions in the level $0$ case. As for unramified principal series, they give rise to explicit coefficients. We deduce a local proof of Matringe's criterion of distinction of discrete series, in the level $0$ case, for the Galois symmetric space ${\rm GL}(n,F)/{\rm GL}(n,F_0 )$, for any unramified quadratic extension $F/F_0$. We also exhibit explicit test vectors when these representations are distinguished.
A Unified Theory of Fractional Nonlocal and Weighted Nonlocal Vector Calculus.

Nonlocal and fractional models capture effects that classical (or standard) partial differential equations cannot describe; for this reason, they are suitable for a broad class of engineering and scientific applications that feature multiscale or anomalous behavior. This has driven a desire for a vector calculus based on nonlocal and fractional derivatives to derive models of, e.g., subsurface transport, turbulence, and conservation laws. In the literature, several independent definitions and theories of nonlocal and fractional vector calculus have been put forward. Some have been studied rigorously and in depth, while others have been introduced ad-hoc for specific applications. At the moment, this fragmented literature suffers from a lack of rigorous comparison and unified notation, hindering the development of nonlocal modeling. The ultimate goal of this work is to provide a new theory and to "connect all the dots" by defining a universal form of nonlocal vector calculus operators under a theory that includes, as a special case, several well-known proposals for fractional vector calculus in the limit of infinite interactions. We show that this formulation enjoys a form of Green's identity, enabling a unified variational theory for the resulting nonlocal exterior-value problems, and is consistent with several independent results in the fractional calculus literature. The proposed unified vector calculus has the potential to go beyond the analysis of nonlocal equations by supporting new model discovery, establishing theory and interpretation for a broad class of operators and providing useful analogues of standard tools from classical vector calculus.
Towards Learning Instantiated Logical Rules from Knowledge Graphs

Efficiently inducing high-level interpretable regularities from knowledge graphs (KGs) is an essential yet challenging task that benefits many downstream applications. In this work, we present GPFL, a probabilistic rule learner optimized to mine instantiated first-order logic rules from KGs. Instantiated rules contain constants extracted from KGs. Compared to abstract rules that contain no constants, instantiated rules are capable of explaining and expressing concepts in more details. GPFL utilizes a novel two-stage rule generation mechanism that first generalizes extracted paths into templates that are acyclic abstract rules until a certain degree of template saturation is achieved, then specializes the generated templates into instantiated rules. Unlike existing works that ground every mined instantiated rule for evaluation, GPFL shares groundings between structurally similar rules for collective evaluation. Moreover, we reveal the presence of overfitting rules, their impact on the predictive performance, and the effectiveness of a simple validation method filtering out overfitting rules. Through extensive experiments on public benchmark datasets, we show that GPFL 1. ) significantly reduces the runtime on evaluating instantiated rules; 2. ) discovers much more quality instantiated rules than existing works; 3. ) improves the predictive performance of learned rules by removing overfitting rules via validation; 4. ) is competitive on knowledge graph completion task compared to state-of-the-art baselines.
An alternate Lagrangian scheme for spatially inhomogeneous evolutionary games.

An alternate Lagrangian scheme at discrete times is proposed for the approximation of a nonlinear continuity equation arising as a mean-field limit of spatially inhomogeneous evolutionary games, describing the evolution of a system of spatially distributed agents with strategies, or labels, whose payoff depends also on the current position of the agents. The scheme is Lagrangian, as it traces the evolution of position and labels along characteristics and is alternate, as it consists of the following two steps: first the distribution of strategies or labels is updated according to a best performance criterion and then this is used by the agents to evolve their position. A general convergence result is provided in the space of probability measures. In the special cases of replicator-type systems and reversible Markov chains, variants of the scheme, where the explicit step in the evolution of the labels is replaced by an implicit one, are also considered and convergence results are provided.
Safe Motion Planning for Autonomous Driving using an Adversarial Road Model

This paper presents a game-theoretic path-following formulation where the opponent is an adversary road model. This formulation allows us to compute safe sets using tools from viability theory, that can be used as terminal constraints in an optimization-based motion planner. Based on the adversary road model, we first derive an analytical discriminating domain, which even allows guaranteeing safety in the case when steering rate constraints are considered. Second, we compute the discriminating kernel and show that the output of the gridding based algorithm can be accurately approximated by a fully connected neural network, which can again be used as a terminal constraint. Finally, we show that by using our proposed safe sets, an optimization-based motion planner can successfully drive on city and country roads with prediction horizons too short for other baselines to complete the task.
Formation of twisted liquid jets

Liquid jets issued from a non-circular orifice exhibit oscillation owing to the surface tension. When the orifice has an $n$-fold rotational symmetry, a material cross section of the jet interchanges two symmetric shapes alternately. This oscillation, called axis switching, is a superposition of two ripples oppositely propagating in the azimuthal direction around the axis. In this study, we used computer simulations to demonstrate that we can pick up one of the two ripples by adjusting the initial velocity profile of the orifice. As a result of the single wave propagation in the azimuth, the jet surface shows a twisted appearance. In contrast to the swirling jets, the twisted jet has no angular momentum around the axis. We numerically demonstrated the formation of twisted jets with various cross sections, including a regular square.
Rheology of tunable materials

Materials with tunable mechanical properties are ubiquitous in biology and engineering, such as muscle, the cytoskeleton, soft robotic actuators, and magnetorheological fluids. Their functionality arises through an external signal that modulates intrinsic mechanical properties, like neural spikes in muscle. However, current rheological methods assume approximately invariant mechanical properties during measurement, and thus cannot be used for characterizing the rheology of tunable materials. Here, we develop a geometric framework for characterizing tunable materials by combining classical oscillatory rheology that uses Lissajous force-length loops to characterize non-tunable materials, with the technique of work loops that is used to study muscle's work output under varying stimulation. We derive the force-length loop under varying stimulation by splicing Lissajous loops obtained under constant stimulation. This splicing approach captures the force-length loop shapes in muscle that are not seen in non-tunable materials, and yields a nondimensional parametrization for the space of possible responses of tunable materials.
Analytical entropic bounds for multiparty device-independent cryptography

We consider a device-independent (DI) scenario where $N$ parties test a generic full-correlator Bell inequality with two inputs and two outcomes per party. By exploiting the inequality's symmetries, we drastically simplify the general form of the quantum state that can be considered, without loss of generality. We then focus on the Mermin-Ardehali-Belinskii-Klyshko (MABK) inequality and derive an upper bound on the maximal violation of the MABK inequality by an arbitrary $N$-qubit state, as a function of the state's parameters. The two results enable us to derive analytical bounds on the von Neumann entropy of the parties' outcomes, conditioned on the eavesdropper's information. These quantities are crucial for the security of most cryptographic protocols and better bounds significantly impact the protocols' performance. In particular, we bound the conditional entropy of a single party's outcome and the joint conditional entropy of two parties' outcomes, as a function of the MABK violation observed by three parties. We extend the former bound to $N$ parties and prove its tightness, while we observe that the latter bound significantly improves previous results.