Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Siamese Networks for Semantic Code Search

With the increase in the number of open repositories and discussion forums, the use of natural language for semantic code search has become increasingly common. The accuracy of the results returned by such systems, however, can be low due to 1) limited shared vocabulary between code and user query and 2) inadequate semantic understanding of user query and its relation to code syntax. Siamese networks are well suited to learning such joint relations between data, but have not been explored in the context of code search. In this work, we evaluate Siamese networks for this task by exploring multiple extraction network architectures. These networks independently process code and text descriptions before passing them to a Siamese network to learn embeddings in a common space. We experiment on two different datasets and discover that Siamese networks can act as strong regularizers on networks that extract rich information from code and text, which in turn helps achieve impressive performance on code search beating previous baselines on $2$ programming languages. We also analyze the embedding space of these networks and provide directions to fully leverage the power of Siamese networks for semantic code search.
Stochastic Linear Bandits with Protected Subspace

We study a variant of the stochastic linear bandit problem wherein we optimize a linear objective function but rewards are accrued only orthogonal to an unknown subspace (which we interpret as a \textit{protected space}) given only zero-order stochastic oracle access to both the objective itself and protected subspace. In particular, at each round, the learner must choose whether to query the objective or the protected subspace alongside choosing an action. Our algorithm, derived from the OFUL principle, uses some of the queries to get an estimate of the protected space, and (in almost all rounds) plays optimistically with respect to a confidence set for this space. We provide a $\tilde{O}(sd\sqrt{T})$ regret upper bound in the case where the action space is the complete unit ball in $\mathbb{R}^d$, $s < d$ is the dimension of the protected subspace, and $T$ is the time horizon. Moreover, we demonstrate that a discrete action space can lead to linear regret with an optimistic algorithm, reinforcing the sub-optimality of optimism in certain settings. We also show that protection constraints imply that for certain settings, no consistent algorithm can have a regret smaller than $\Omega(T^{3/4}). $ We finally empirically validate our results with synthetic and real datasets.
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question. However, current datasets do not provide a complete explanation for the reasoning process from the question to the answer. Further, previous studies revealed that many examples in existing multi-hop datasets do not require multi-hop reasoning to answer a question. In this study, we present a new multi-hop QA dataset, called 2WikiMultiHopQA, which uses structured and unstructured data. In our dataset, we introduce the evidence information containing a reasoning path for multi-hop questions. The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model. We carefully design a pipeline and a set of templates when generating a question-answer pair that guarantees the multi-hop steps and the quality of the questions. We also exploit the structured format in Wikidata and use logical rules to create questions that are natural but still require multi-hop reasoning. Through experiments, we demonstrate that our dataset is challenging for multi-hop models and it ensures that multi-hop reasoning is required.
Budget Sharing for Multi-Analyst Differential Privacy

Large organizations that collect data about populations (like the US Census Bureau) release summary statistics that are used by multiple stakeholders for resource allocation and policy making problems. These organizations are also legally required to protect the privacy of individuals from whom they collect data. Differential Privacy (DP) provides a solution to release useful summary data while preserving privacy. Most DP mechanisms are designed to answer a single set of queries. In reality, there are often multiple stakeholders that use a given data release and have overlapping but not-identical queries. This introduces a novel joint optimization problem in DP where the privacy budget must be shared among different analysts. We initiate study into the problem of DP query answering across multiple analysts. To capture the competing goals and priorities of multiple analysts, we formulate three desiderata that any mechanism should satisfy in this setting - The Sharing Incentive, Non-interference, and Adaptivity - while still optimizing for overall error. We demonstrate how existing DP query answering mechanisms in the multi-analyst settings fail to satisfy at least one of the desiderata. We present novel DP algorithms that provably satisfy all our desiderata and empirically show that they incur low error on realistic tasks.
Causal Campbell-Goodhart's law and Reinforcement Learning

Campbell-Goodhart's law relates to the causal inference error whereby decision-making agents aim to influence variables which are correlated to their goal objective but do not reliably cause it. This is a well known error in Economics and Political Science but not widely labelled in Artificial Intelligence research. Through a simple example, we show how off-the-shelf deep Reinforcement Learning (RL) algorithms are not necessarily immune to this cognitive error. The off-policy learning method is tricked, whilst the on-policy method is not. The practical implication is that naive application of RL to complex real life problems can result in the same types of policy errors that humans make. Great care should be taken around understanding the causal model that underpins a solution derived from Reinforcement Learning.
The folded spin-1/2 XXZ model: II. Thermodynamics and hydrodynamics with a minimal set of charges

We study the (dual) folded spin-1/2 XXZ model in the thermodynamic limit. We focus, in particular, on a class of local macrostates that includes Gibbs ensembles. We develop a thermodynamic Bethe Ansatz description and work out generalised hydrodynamics at the leading order. Remarkably, in the ballistic scaling limit the junction of two local macrostates results in a discontinuity in the profile of essentially any local observable.
Differential Dynamic Programming With Nonlinear Safety Constraints Under System Uncertainties

Safe operation of systems such as robots requires them to plan and execute trajectories subject to safety constraints. When those systems are subject to uncertainties in their dynamics, it is challenging to ensure that the constraints are not violated. In this letter, we propose Safe-CDDP, a safe trajectory optimization and control approach for systems under additive uncertainties and nonlinear safety constraints based on constrained differential dynamic programming (DDP). The safety of the robot during its motion is formulated as chance constraints with user-chosen probabilities of constraint satisfaction. The chance constraints are transformed into deterministic ones in DDP formulation by constraint tightening. To avoid over-conservatism during constraint tightening, linear control gains of the feedback policy derived from the constrained DDP are used in the approximation of closed-loop uncertainty propagation in prediction. The proposed algorithm is empirically evaluated on three different robot dynamics with up to 12 degrees of freedom in simulation. The computational feasibility and applicability of the approach are demonstrated with a physical hardware implementation.
Using radial velocities to reveal black holes in binaries: A test case

Aims. Large radial velocity variations in the LAMOST spectra of giant stars have been used to infer the presence of unseen companions. Some of them have been proposed as possible black hole candidates. We test this selection by investigating the classification of the one candidate that has a known X-ray counterpart (UCAC4 721-037069). Methods. We obtained time-resolved spectra from the Liverpool Telescope and a 5 ks observation from the Chandra observatory to fully constrain the orbital parameters and the X-ray emission of this system. Results. We find the source to be an eclipsing stellar binary that can be classified as an RS CVn. The giant star fills its Roche lobe, and the binary mass ratio is greater than one. The system may be an example of stable mass transfer from an intermediate-mass star with a convective envelope. Conclusions. Using only radial velocity to identify black hole candidates can lead to many false positives. The presence of an optical orbital modulation, such as what has been observed for all LAMOST candidates, will in most cases indicate that the system is a stellar binary.
Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets

The availability of different pre-trained semantic models has enabled the quick development of machine learning components for downstream applications. However, even if texts are abundant for low-resource languages, there are very few semantic models publicly available. Most of the publicly available pre-trained models are usually built as a multilingual version of semantic models that will not fit well with the need for low-resource languages. We introduce different semantic models for Amharic, a morphologically complex Ethio-Semitic language. After we investigate the publicly available pre-trained semantic models, we fine-tune two pre-trained models and train seven new different models. The models include Word2Vec embeddings, distributional thesaurus (DT), BERT-like contextual embeddings, and DT embeddings obtained via network embedding algorithms. Moreover, we employ these models for different NLP tasks and study their impact. We find that newly-trained models perform better than pre-trained multilingual models. Furthermore, models based on contextual embeddings from FLAIR and RoBERTa perform better than word2Vec models for the NER and POS tagging tasks. DT-based network embeddings are suitable for the sentiment classification task. We publicly release all the semantic models, machine learning components, and several benchmark datasets such as NER, POS tagging, sentiment classification, as well as Amharic versions of WordSim353 and SimLex999.
Adversarial Examples in Constrained Domains

Machine learning algorithms have been shown to be vulnerable to adversarial manipulation through systematic modification of inputs (e.g., adversarial examples) in domains such as image recognition. In the default threat model, the adversary exploits the unconstrained nature of images–each feature (pixel) is fully under control of the adversary. However, it is not clear how these attacks translate to constrained domains (e.g., network intrusion detection) that limit which and how features can be modified by the adversary. In this paper, we explore whether constrained domains are less vulnerable than unconstrained domains to adversarial example generation algorithms. We create an algorithm for generating adversarial sketches : targeted universal perturbation vectors that encode feature saliency within the envelope of domain constraints. To assess how these algorithms perform, we evaluate them in constrained (e.g., network intrusion detection) and unconstrained (e.g., image recognition) domains. The results demonstrate that our approaches generate misclassification rates in constrained domains that were comparable to those of unconstrained domains (greater than 95%). Our investigation shows that the narrow attack surface exposed by constrained domains is still sufficiently large to craft successful adversarial examples–and thus, constraints do not appear to make a domain robust. Indeed, even if a defender constrains an adversary to as little as five random features, generating adversarial examples is still possible.
ASIST: Annotation-free synthetic instance segmentation and tracking for microscope video analysis

Instance object segmentation and tracking provide comprehensive quantification of objects across microscope videos. The recent single-stage pixel-embedding based deep learning approach has shown its superior performance compared with "segment-then-associate" two-stage solutions. However, one major limitation of applying a supervised pixel-embedding based method to microscope videos is the resource-intensive manual labeling, which involves tracing hundreds of overlapped objects with their temporal associations across video frames. Inspired by the recent generative adversarial network (GAN) based annotation-free image segmentation, we propose a novel annotation-free synthetic instance segmentation and tracking (ASIST) algorithm for analyzing microscope videos of sub-cellular microvilli. The contributions of this paper are three-fold: (1) proposing a new annotation-free video analysis paradigm is proposed. (2) aggregating the embedding based instance segmentation and tracking with annotation-free synthetic learning as a holistic framework; and (3) to the best of our knowledge, this is first study to investigate microvilli instance segmentation and tracking using embedding based deep learning. From the experimental results, the proposed annotation-free method achieved superior performance compared with supervised learning.
Flexible File Address Space Management

Many applications store their data using structured files. In order to insert or remove a record in the middle of a structured file, the application needs to shift the positions of existing data. To this end, the existing data after the insertion or removal point must be rewritten to admit the change in place, which can be unaffordable for applications that make frequent updates. Alternatively, the out-of-place update approach can admit changes with reduced rewrites. However, it leads to increased access costs and excessive complexity. This paper presents a novel mechanism for managing file address space, named FlexFile, that enables fast insertion and removal of arbitrary-sized data in a file. Based on FlexFile, applications can perform in-place updates to a file without rewriting existing data, and efficiently manage data on a linear file layout with minimal complexity. Evaluation results show that a simple key-value store built on top of FlexFile can achieve high performance for both reads and writes.
Nanotesla Magnetometry with the Silicon Vacancy in Silicon Carbide

Silicon Carbide is a promising host material for spin defect based quantum sensors owing to its commercial availability and established techniques for electrical and optical microfabricated device integration. The negatively charged silicon vacancy is one of the leading spin defects studied in silicon carbide owing to its near telecom photoemission, high spin number, and nearly temperature independent ground state zero field splitting. We report the realization of nanoTesla shot-noise limited ensemble magnetometry based on optically detected magnetic resonance with the silicon vacancy in 4H silicon carbide. By coarsely optimizing the anneal parameters and minimizing power broadening, we achieved a sensitivity of 3.5 nT/$\sqrt{Hz}$. This was accomplished without utilizing complex photonic engineering, control protocols, or applying excitation powers greater than a Watt. This work demonstrates that the silicon vacancy in silicon carbide provides a low-cost and simple approach to quantum sensing of magnetic fields.
Supermassive neutron stars rule out twin stars

We investigate the implications of a hypothetical $2.5\,\mathrm{M_\odot}$ neutron star in regard to the possibility of a strong phase transition to quark matter. We use equations of state (EoS) of varying stiffness provided by a parameterizable relativistic mean filed model transitioning in a first order phase transition to quark matter with a constant speed of sound. We find a strong connection between the discontinuity in energy density and the maximal mass generated by the EoS. We demonstrate, that high maximal masses cannot be realized for large discontinuities in energy density, which are necessary for visible twin stars, especially for soft EoSs. As a result twin stars and maximal masses of $M_{max}\gtrsim2.2\,M_\odot$ are mutually exclusive.
Facial Keypoint Sequence Generation from Audio

Whenever we speak, our voice is accompanied by facial movements and expressions. Several recent works have shown the synthesis of highly photo-realistic videos of talking faces, but they either require a source video to drive the target face or only generate videos with a fixed head pose. This lack of facial movement is because most of these works focus on the lip movement in sync with the audio while assuming the remaining facial keypoints' fixed nature. To address this, a unique audio-keypoint dataset of over 150,000 videos at 224p and 25fps is introduced that relates the facial keypoint movement for the given audio. This dataset is then further used to train the model, Audio2Keypoint, a novel approach for synthesizing facial keypoint movement to go with the audio. Given a single image of the target person and an audio sequence (in any language), Audio2Keypoint generates a plausible keypoint movement sequence in sync with the input audio, conditioned on the input image to preserve the target person's facial characteristics. To the best of our knowledge, this is the first work that proposes an audio-keypoint dataset and learns a model to output the plausible keypoint sequence to go with audio of any arbitrary length. Audio2Keypoint generalizes across unseen people with a different facial structure allowing us to generate the sequence with the voice from any source or even synthetic voices. Instead of learning a direct mapping from audio to video domain, this work aims to learn the audio-keypoint mapping that allows for in-plane and out-of-plane head rotations, while preserving the person's identity using a Pose Invariant (PIV) Encoder.
Contextual Salience for Fast and Accurate Sentence Vectors.

Unsupervised vector representations of sentences or documents are a major building block for many language tasks such as sentiment classification. However, current methods are uninterpretable and slow or require large training datasets. Recent word vector-based proposals implicitly assume that distances in a word embedding space are equally important, regardless of context. We introduce contextual salience (CoSal), a measure of word importance that uses the distribution of context vectors to normalize distances and weights. CoSal relies on the insight that unusual word vectors disproportionately affect phrase vectors. A bag-of-words model with CoSal-based weights produces accurate unsupervised sentence or document representations for classification, requiring little computation to evaluate and only a single covariance calculation to ``train. " CoSal supports small contexts, out-of context words and outperforms SkipThought on most benchmarks, beats tf-idf on all benchmarks, and is competitive with the unsupervised state-of-the-art.
Supersonic turbulence in giant HII regions: clues from 30 Doradus

The tight correlation between turbulence and luminosity in giant HII regions (GHRs) is not well understood. While the luminosity is due to the UV radiation from the massive stars in the ionizing clusters, it is not clear what powers the turbulence. Observations of the two prototypical GHRs in the local Universe, 30 Doradus and NGC 604, show that part of the kinetic energy of the nebular gas comes from the combined stellar winds of the most massive stars, the cluster winds, but not all. We present a study of the kinematics of 30 Doradus based on archival VLT FLAMES/GIRAFFE data and new high-resolution observations with HARPS. We find that the nebular structure and kinematics are shaped by a hot cluster wind and not by the stellar winds of individual stars. The cluster wind powers most of the turbulence of the nebular gas, with a small contribution from the combined gravitational potential of stars and gas. We estimate the total mass of 30 Doradus and we argue that the region does not contain significant amounts of neutral (HI) gas, and that the giant molecular cloud 30 Dor-10, which is close to the center of the nebula in projection, is in fact an inflating cloud tens of parsecs away from R136, the core of the ionizing cluster. We rule out a Kolmogorov-like turbulent kinetic energy cascade as the source of supersonic turbulence in GHRs.
Generalized One-to-One Mappings between Homomorphism Sets of Digraphs.

Structural properties of finite digraphs $R$ and $S$ are studied which enforce $\# {\cal H}(G,R) \leq \# {\cal H}(G,S)$ for every finite digraph $G \in \mathfrak{ D }'$, where ${\cal H}(G,H)$ is the set of homomorphisms from $G$ to $H$, and $\mathfrak{ D }'$ is a class of digraphs. In a previous study, we have seen that the key for such a relation between $R$ and $S$ is the existence of a strong S-scheme from $R$ to $S$. Such an S-scheme $\rho$ defines a one-to-one mapping $\rho_G : {\cal S}(G,R) \rightarrow {\cal S}(G,S)$ for every $G \in \mathfrak{ D }'$, where ${\cal S}(G,H)$ is the set of homomorphisms from $G$ to $H$ mapping proper arcs of $G$ to proper arcs of $H$. In the present article, we characterize S-schemes $\rho$ which are induced by strict homomorphisms $\epsilon : {\cal E}(R) \rightarrow {\cal E}(S)$ between auxiliary systems of $R$ and $S$, and we analyze the mutual dependency between the properties of $\rho$ and $\epsilon$. Wide applicability of the theory is ensured by specifying the auxiliary systems ${\cal E}(R)$ and ${\cal E}(S)$ as EV-systems of $R$ and $S$. The results are applied on a rearrangement method for digraphs and on undirected graphs.
Molecular survey for the honey bee (Apis mellifera L.) trypanosome parasites Crithidia mellificae and Lotmaria passim

Honey bee populations in the United States have been fluctuating in recent years. Although this has not been attributed to any one cause, recent studies have shown that multiple interactions among mircoorganisms may contribute to their decline. Several honey bee internal parasites have been overlooked as probable causes of decline; these include two different species of trypanosomes Crithidia mellificae and Lotmaria passim. Both parasites have been understudied in honey bee populations and could contribute to population disease dynamics. This study explored a molecular diagnostic survey for C. mellificae and L. passim using multiplex-PCR. Honey bee samples from both feral and managed populations were collected from eight states. This is the first national honey bee trypanosome survey in the United States. A total of 1,360 samples were surveyed during this study in which, 11% were positive for L. passim; while no cases of C. mellificae were detected using multiplex-PCR analysis. Infection rates of states positive for L. passim ranged from 17% (New York) to 0.70% (Utah). Only one state (Mississippi) was not positive for L. passim. The proportion of positive L. passim samples was significantly different between managed and feral honey bee colonies. Results revealed that the honey bee parasitic trypanosome L. passim has a widespread distribution in the United States and should be considered as a potential contributor to honey bee health decline.