Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision and the foraging technique of skimmers (Rynchopidae)

In birds, the position and extent of the region of binocular vision appears to be determined primarily by feeding ecology. Of prime importance is the degree to which vision is used for the precise control of bill position when foraging. Skimmers (Rynchops, Rynchopidae, Charadriiformes) exhibit a unique foraging behaviour and associated structural adaptations. When foraging they fly low and straight over water with the mouth open and the mandible partially submerged. Items that are hit by the lower mandible are grasped by a rapid reflex bill closure. It is believed that this unique ‘skimming’ foraging technique is guided by tactile rather than visual cues. It is predicted therefore that the visual fields of skimmers will have similar topography to those of other tactile feeding birds. We determined retinal visual fields in Black Skimmers Rynchops niger using an ophthalmoscopic reflex technique. Contrary to expectation the visual fields of Black Skimmers are not like those of other tactile feeders. They show high similarity with those of birds that feed by precision-pecking. The projection of the bill tip when the mouth is closed and when open (as in skimming) falls within the frontal binocular field and there is an extensive blind area above and behind the head. We argue that this visual field topography functions to achieve accurate bill positioning with respect to the water surface when skimming and, because foraging skimmers cannot determine the identity of what they are seizing as they skim, to permit the visual identification of prey items held between the mandibles after they have been taken from the water surface. When skimming, only a small portion of the binocular field, approximately 5° wide and extending 5° above the horizontal, looks in the direction of travel. The small size of this forward-facing region of binocularity in skimmers suggests that control of locomotion in birds does not necessarily require extensive binocularity in the direction of travel.
On the Demystification of Knowledge Distillation: A Residual Network Perspective

Knowledge distillation (KD) is generally considered as a technique for performing model compression and learned-label smoothing. However, in this paper, we study and investigate the KD approach from a new perspective: we study its efficacy in training a deeper network without any residual connections. We find that in most of the cases, non-residual student networks perform equally or better than their residual versions trained on raw data without KD (baseline network). Surprisingly, in some cases, they surpass the accuracy of baseline networks even with the inferior teachers. After a certain depth of non-residual student network, the accuracy drop, coming from the removal of residual connections, is substantial, and training with KD boosts the accuracy of the student up to a great extent; however, it does not fully recover the accuracy drop. Furthermore, we observe that the conventional teacher-student view of KD is incomplete and does not adequately explain our findings. We propose a novel interpretation of KD with the Trainee-Mentor hypothesis, which provides a holistic view of KD. We also present two viewpoints, loss landscape, and feature reuse, to explain the interplay between residual connections and KD. We substantiate our claims through extensive experiments on residual networks.
A Novel Bistatic Joint Radar-Communication System in Multi-path Environments

Radar detection and communication can be operated simultaneously in joint radar-communication (JRC) system. In this paper, we propose a bistatic JRC system which is applicable in multi-path environments. Basing on a novel joint waveform, a joint detection process is designed for both target detection and channel estimation. Meanwhile, a low-cost channel equalization method that utilizes the channel state information acquired from the detection process is proposed. The numerical results show that the symbol error rate (SER) of the proposed system is similar to that of the binary frequency shift keying system, and the signal to noise ratio requirement in multi-path environments is less than 2 dB higher compared with that in single-path environment to reach a SER of 10-5. Besides, the knowledge of the embedded information is not required for the joint detection process and the detection performance is robust to unknown information.
Transactions on Red-black and AVL trees in NVRAM

Byte-addressable non-volatile memory (NVRAM) supports persistent storage with low latency and high bandwidth. Complex data structures in it ought to be updated transactionally, so that they remain recoverable at all times. Traditional database technologies such as keeping a separate log, a journal, or shadow data work on a coarse-grained level, where the whole transaction is made visible using a final atomic update operation. These methods typically need significant additional space overhead and induce non-trivial overhead for log pruning, state maintenance, and resource (de-)allocation. Thus, they are not necessarily the best choice for NVRAM, which supports fine-grained, byte-addressable access. We present a generic transaction mechanism to update dynamic complex data structures `in-place' with a constant memory overhead. It is independent of the size of the data structure. We demonstrate and evaluate our approach on Red-Black Trees and AVL Trees with a redo log of constant size (4 resp. 2 cache lines). The redo log guarantees that each accepted (started) transaction is executed eventually despite arbitrary many system crashes and recoveries in the meantime. We update complex data structures in local and remote NVRAM providing exactly once semantics and durable linearizability for multi-reader single-writer access. To persist data, we use the available processor instructions for NVRAM in the local case and remote direct memory access (RDMA) combined with a software agent in the remote case.
Penalized regression with multiple loss functions and selection by vote

This article considers a linear model in a high dimensional data scenario. We propose a process which uses multiple loss functions both to select relevant predictors and to estimate parameters, and study its asymptotic properties. Variable selection is conducted by a procedure called "vote", which aggregates results from penalized loss functions. Using multiple objective functions separately simplifies algorithms and allows parallel computing, which is convenient and fast. As a special example we consider a quantile regression model, which optimally combines multiple quantile levels. We show that the resulting estimators for the parameter vector are asymptotically efficient. Simulations and a data application confirm the three main advantages of our approach: (a) reducing the false discovery rate of variable selection; (b) improving the quality of parameter estimation; (c) increasing the efficiency of computation.
Ontology-guided Semantic Composition for Zero-Shot Learning

Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, we propose to model the compositional and expressive semantics of class labels by an OWL (Web Ontology Language) ontology, and further develop a new ZSL framework with ontology embedding. The effectiveness has been verified by some primary experiments on animal image classification and visual question answering.
New Developer Metrics for Open Source Software Development Challenges: An Empirical Study of Project Recommendation Systems

Software collaboration platforms where millions of developers from diverse locations can contribute to the common open source projects have recently become popular. On these platforms, various information is obtained from developer activities that can then be used as developer metrics to solve a variety of challenges. In this study, we proposed new developer metrics extracted from the issue, commit, and pull request activities of developers on GitHub. We created developer metrics from the individual activities and combined certain activities according to some common traits. To evaluate these metrics, we created an item-based project recommendation system. In order to validate this system, we calculated the similarity score using two methods and assessed top-n hit scores using two different approaches. The results for all scores with these methods indicated that the most successful metrics were binary_issue_related, issue_commented, binary_pr_related, and issue_opened. To verify our results, we compared our metrics with another metric generated from a very similar study and found that most of our metrics gave better scores that metric. In conclusion, the issue feature is more crucial for GitHub compared with other features. Moreover, commenting activity in projects can be equally as valuable as code contributions. The most of binary metrics that were generated, regardless of the number of activities, also showed remarkable results. In this context, we presented improvable and noteworthy developer metrics that can be used for a wide range of open-source software development challenges, such as user characterization, project recommendation, and code review assignment.
Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs

Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-purpose processors such as CPUs and GPUs. In fact, due to being unable to leverage bit-level-parallelism with a word-based architecture, GPUs have been criticized for extremely low utilization (1 percent) when executing BNNs. Consequently, the latest tensorcores in NVIDIA Turing GPUs start to experimentally support bit computation. In this article, we look into this brand new bit computation capability and characterize its unique features. We show that the stride of memory access can significantly affect performance delivery and a data-format co-design is highly desired to support the tensorcores for achieving superior performance than existing software solutions without tensorcores. We realize the tensorcore-accelerated BNN design, particularly the major functions for fully-connect and convolution layers — bit matrix multiplication and bit convolution. Evaluations on two NVIDIA Turing GPUs show that, with ResNet-18, our BTC-BNN design can process ImageNet at a rate of 5.6K images per second, 77 percent faster than state-of-the-art. Our BNN approach is released on https://github.com/pnnl/TCBNN.
Vertex guarding for dynamic orthogonal art galleries

We devise an algorithm for surveying a dynamic orthogonal polygonal domain by placing one guard at each vertex in a subset of its vertices, i.e., whenever an orthogonal polygonal domain [Formula: see text] is modified to result in another orthogonal polygonal domain [Formula: see text], our algorithm updates the set of vertex guards surveying [Formula: see text] so that the updated guard set surveys [Formula: see text]. Our algorithm modifies the guard placement in [Formula: see text] amortized time, while ensuring the updated orthogonal polygonal domain with [Formula: see text] holes and [Formula: see text] vertices is guarded using at most [Formula: see text] vertex guards. For the special case of the initial orthogonal polygon being hole-free and each update resulting in a hole-free orthogonal polygon, our guard update algorithm takes [Formula: see text] worst-case time. Here, [Formula: see text] and [Formula: see text] are the number of vertices of the orthogonal polygon before and after the update, respectively; and, [Formula: see text] is the sum of [Formula: see text] and the number of updates to a few structures maintained by our algorithm. Further, by giving a construction, we show it suffices for the algorithm to consider only the case in which the parity of the number of reflex vertices of both [Formula: see text] and [Formula: see text] are equal.
Matchings in regular graphs: minimizing the partition function

For a graph $G$ on $v(G)$ vertices let $m_k(G)$ denote the number of matchings of size $k$, and consider the partition function $M_{G}(\lambda)=\sum_{k=0}^nm_k(G)\lambda^k$. In this paper we show that if $G$ is a $d$--regular graph and $0 \frac{1}{v(K_{d+1})}\ln M_{K_{d+1}}(\lambda). $$ The same inequality holds true if $d=3$ and $\lambda<0.3575$. More precise conjectures are also given.
On Bellman's Optimality Principle for zs-POSGs

Many non-trivial sequential decision-making problems are efficiently solved by relying on Bellman's optimality principle, i.e., exploiting the fact that sub-problems are nested recursively within the original problem. Here we show how it can apply to (infinite horizon) 2-player zero-sum partially observable stochastic games (zs-POSGs) by (i) taking a central planner's viewpoint, which can only reason on a sufficient statistic called occupancy state, and (ii) turning such problems into zero-sum occupancy Markov games (zs-OMGs). Then, exploiting the Lipschitz-continuity of the value function in occupancy space, one can derive a version of the HSVI algorithm (Heuristic Search Value Iteration) that provably finds an $\epsilon$-Nash equilibrium in finite time.
Properly Colored Hamilton Cycles in Dirac-Type Hypergraphs

We consider a robust variant of Dirac-type problems in $k$-uniform hypergraphs. For instance, we prove that if $\mathcal{H}$ is a $k$-uniform hypergraph with minimum codegree at least $\left(\frac 12 + \gamma \right)n$, $\gamma >0$, and $n$ is sufficiently large, then any edge coloring $\phi$ satisfying appropriate local constraints yields a properly colored tight Hamilton cycle in $\mathcal{H}$. Similar results for loose cycles are also shown.
The Ophiuchus DIsc Survey Employing ALMA (ODISEA) – II. The effect of stellar multiplicity on disc properties

We present Adaptive Optics (AO) near infrared (NIR) observations using VLT/NACO and Keck/NIRC2 of ODISEA targets. ODISEA is an ALMA survey of the entire population of circumstellar discs in the Ophiuchus molecular cloud. From the whole sample of ODISEA we select all the discs that are not already observed in the NIR with AO and that are observable with NACO or NIRC2. The NIR-ODISEA survey consists of 147 stars observed in NIR AO imaging for the first time, as well as revisiting almost all the binary systems of Ophiuchus present in the literature (20 out of 21). In total, we detect 20 new binary systems and one triple system. For each of them we calculate the projected separation and position angle of the companion, as well as their NIR and millimeter flux ratios. From the NIR contrast we derived the masses of the secondaries, finding that 9 of them are in the sub-stellar regime (30-50 \MJup). Discs in multiple systems reach a maximum total dust mass of $\sim$ 50 M$_{\oplus}$, while discs in single stars can reach a dust mass of 200 M$_{\oplus}$. Discs with masses above 10 M$_{\oplus}$ are found only around binaries with projected separations larger than $\sim$ 110 au. The maximum disc size is also larger around single star than binaries. However, since most discs in Ophiuchus are very small and low-mass, the effect of visual binaries is relatively weak in the general disc population.
Large deviation limits of invariant measures

This paper is concerned with the general theme of relating the Large Deviation Principle (LDP) for the invariant measures of stochastic processes to the associated sample path LDP. It is shown that if the sample path deviation function possesses certain structure, then the LDP for the invariant measures is implied by the sample path LDP, no other properties of the stochastic processes in question being material. As an application, we obtain an LDP for the stationary distributions of jump diffusions. Methods of large deviation convergence and idempotent probability play an integral part.
Model-based Reinforcement Learning: A Survey

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a key challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This paper presents a survey of the integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps. First, we systematically cover approaches to dynamics model learning, including challenges like dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. Second, we present a systematic categorization of planning-learning integration, including aspects like: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. After these two key sections, we also discuss the potential benefits of model-based RL, like enhanced data efficiency, targeted exploration, and improved stability. Along the survey, we also draw connections to several related RL fields, like hierarchical RL and transfer, and other research disciplines, like behavioural psychology. Altogether, the survey presents a broad conceptual overview of planning-learning combinations for MDP optimization.
Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark

Designing compounds with desired properties is a key element of the drug discovery process. However, measuring progress in the field has been challenging due to the lack of realistic retrospective benchmarks, and the large cost of prospective validation. To close this gap, we propose a benchmark based on docking, a widely used computational method for assessing molecule binding to a protein. Concretely, the goal is to generate drug-like molecules that are scored highly by SMINA, a popular docking software. We observe that various graph-based generative models fail to propose molecules with a high docking score when trained using a realistically sized training set. This suggests a limitation of the current incarnation of models for de novo drug design. Finally, we also include simpler tasks in the benchmark based on a simpler scoring function. We release the benchmark as an easy to use package available at https://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our benchmark will serve as a stepping stone toward the goal of automatically generating promising drug candidates.
HGKT : Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing

Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. Given learners' exercise records, a knowledge tracing model can trace their hidden knowledge state dynamically. In recent years, many deep learning models have been applied to tackle the KT task, which has shown promising results. However, they still have limitations. Most existing methods simplify the exercising records as knowledge sequence, which fails to explore rich information existed in exercise texts. Besides, the latent hierarchical graph nature of exercises and knowledge remain unexplored. Thus, in this paper, we propose a hierarchical graph knowledge tracing model framework (HGKT) which could leverage the advantages of hierarchical exercise graph and sequence model to enhance the ability of knowledge tracing. Besides, we introduce the concept of problem schema to better represent a group of similar exercises and propose a hierarchical graph neural network to learn representations of problem schemas. Moreover, in the sequence model, we employ two attention mechanisms to highlight important historical states of students. In the testing stage, we present a K&S diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which could more easily be applied to different applications. Finally, we conduct extensive experiments to evaluate the model on a large scale real-world dataset. The results prove the effectiveness of our model and the diversity of its application scenarios.
Efficient Algorithms for Device Placement of DNN Graph Operators

Modern machine learning workloads use large models, with complex structures, that are very expensive to execute. The devices that execute complex models are becoming increasingly heterogeneous as we see a flourishing of domain-specific accelerators being offered as hardware accelerators in addition to CPUs. These trends necessitate distributing the workload across multiple devices. Recent work has shown that significant gains can be obtained with model parallelism, i.e, partitioning a neural network's computational graph onto multiple devices. In particular, this form of parallelism assumes a pipeline of devices, which is fed a stream of samples and yields high throughput for training and inference of DNNs. However, for such settings (large models and multiple heterogeneous devices), we require automated algorithms and toolchains that can partition the ML workload across devices. In this paper, we identify and isolate the structured optimization problem at the core of device placement of DNN operators, for both inference and training, especially in modern pipelined settings. We then provide algorithms that solve this problem to optimality. We demonstrate the applicability and efficiency of our approaches using several contemporary DNN computation graphs.
A Generative Neural Network Framework for Automated Software Testing

Search Based Software Testing (SBST) is a popular automated testing technique which uses a feedback mechanism to search for faults in software. Despite its popularity, it has fundamental challenges related to the design, construction and interpretation of the feedback. Neural Networks (NN) have been hugely popular in recent years for a wide range of tasks. We believe that they can address many of the issues inherent to common SBST approaches. Unfortunately, NNs require large and representative training datasets. In this work we present an SBST framework based on a deconvolutional generative neural network. Not only does it retain the beneficial qualities that make NNs appropriate for SBST tasks, it also produces its own training data which circumvents the problem of acquiring a training dataset that limits the use of NNs. We demonstrate through a series of experiments that this architecture is possible and practical. It generates diverse, sensible program inputs, while exploring the space of program behaviours. It also creates a meaningful ordering over program behaviours and is able to find crashing executions. This is all done without any prior knowledge of the program. We believe this proof of concept opens new directions for future work at the intersection of SBST and neural networks.
Learning Sparse Prototypes for Text Generation

Prototype-driven text generation uses non-parametric models that first choose from a library of sentence "prototypes" and then modify the prototype to generate the output text. While effective, these methods are inefficient at test time as a result of needing to store and index the entire training corpus. Further, existing methods often require heuristics to identify which prototypes to reference at training time. In this paper, we propose a novel generative model that automatically learns a \emph{sparse} prototype support set that, nonetheless, achieves strong language modeling performance. This is achieved by (1) imposing a sparsity-inducing prior on the prototype selection distribution, and (2) utilizing amortized variational inference to \emph{learn} a prototype retrieval function. In experiments, our model outperforms previous prototype-driven language models while achieving up to a 1000x memory reduction, as well as a 1000x speed-up at test time. More interestingly, we show that the learned prototypes are able to capture semantics and syntax at different granularity as we vary the sparsity of prototype selection, and that certain sentence attributes can be controlled by specifying the prototype for generation.