Miracle-dz/UniFAR-FTUs huggingface dataset:
ARDIR: Improving Robustness using Knowledge Distillation of Internal Representation

Adversarial training is the most promising method for learning robust models against adversarial examples. A recent study has shown that knowledge distillation between the same architectures is effective in improving the performance of adversarial training. Exploiting knowledge distillation is a new approach to improve adversarial training and has attracted much attention. However, its performance is still insufficient. Therefore, we propose Adversarial Robust Distillation with Internal Representation~(ARDIR) to utilize knowledge distillation even more effectively. In addition to the output of the teacher model, ARDIR uses the internal representation of the teacher model as a label for adversarial training. This enables the student model to be trained with richer, more informative labels. As a result, ARDIR can learn more robust student models. We show that ARDIR outperforms previous methods in our experiments.
LinkFormer: Automatic Contextualised Link Recovery of Software Artifacts in both Project-based and Transfer Learning Settings

Software artifacts often interact with each other throughout the software development cycle. Associating related artifacts is a common practice for effective documentation and maintenance of software projects. Conventionally, to register the link between an issue report and its associated commit, developers manually include the issue identiﬁer in the message of the relevant commit. Research has shown that developers tend to forget to connect said artifacts manually, resulting in a loss of links. Hence, several link recovery techniques were proposed to discover and revive such links automatically. However, the literature mainly focuses on improving the prediction accuracy on a randomly-split test set, while neglecting other important aspects of this problem, including the effect of time and generalizability of the predictive models. In this paper, we propose LinkFormer to address this problem from three aspects; 1) Accuracy : To better utilize contextual information for prediction, we employ the Transformer architecture and ﬁne-tune multiple pre-trained models on textual and metadata of issues and commits. 2) Data leakage : To empirically assess the impact of time through the splitting policy, we train and test our proposed model along with several existing approaches on both randomly-and temporally-split data. 3) Generalizability : To provide a generic model that can perform well across different projects, we further ﬁne-tune LinkFormer in two transfer-learning settings. We empirically show that researchers should preserve the temporal ﬂow of data when training learning-based models to resemble the real-world setting. In addition, LinkFormer signiﬁcantly outperforms the state-of-the-art by large margins (e.g., 48% improvement of F1-measure in the project-based setting). Furthermore, LinkFormer’s performance in the cross-project setting is on par with its average performance in the project-based case (88% vs. 90% F1-measure). As a result, LinkFormer is capable of extending the knowledge it learned to unseen projects with little to no historical data
A General Language for Modeling Social Media Account Behavior

Malicious actors exploit social media to inﬂate stock prices, sway elec-tions, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features speciﬁcally designed to target suspicious behaviors. However, the eﬀectiveness of these methods decays as malicious behaviors evolve. To address this challenge, we propose a general language for modeling social media account behavior. Words in this language, called BLOC, consist of symbols drawn from distinct alphabets representing user actions and content. The language is highly ﬂexible and can be applied to model a broad spectrum of legitimate and suspicious online behaviors without extensive ﬁne-tuning. Using BLOC to represent the behaviors of Twitter accounts, we achieve performance comparable to or better than state-of-the-art methods in the detection of social bots and coordinated inauthentic behavior.
Radio Observations of Four Active Galactic Nuclei Hosting Intermediate-mass Black Hole Candidates: Studying the Outflow Activity and Evolution

Observational searches for intermediate-mass black holes (IMBHs; 102–106 M ⊙) include relatively isolated dwarf galaxies. For those that host active galactic nuclei (AGNs), the IMBH nature may be discerned through the accretion–jet activity. We present radio observations of four AGN-hosting dwarf galaxies, which potentially harbor IMBHs. Very large array (VLA) observations indicate steep spectra (indices of −0.63 to −1.05) between 1.4 and 9 GHz. However, a comparison with the 9 GHz in-band spectral index shows a steepening for GH047 and GH158 (implying older/relic emission) and flattening for GH106 and GH163 (implying recent activity). Overlapping emission regions in the VLA 1.4 GHz and our very long baseline array (VLBA) 1.5 GHz observations, and possibly symmetric pc-scale extensions, are consistent with recent activity in the latter two. Using the compact VLBA radio luminosity, X-ray luminosity (probing the accretion activity), and the black hole masses, all AGNs are found to lie on the empirical fundamental plane relation. The four AGNs are radio-quiet with relatively higher Eddington ratios (0.04–0.32) and resemble X-ray binaries during spectral state transitions that entail an outflow ejection. Furthermore, the radio to X-ray luminosity ratio logRX of −3.9 to −5.6 in these four sources support the scenarios that include corona mass ejection from the accretion disk and wind activity. The growth to kpc-scales likely proceeds along a similar trajectory to young AGNs and peaked spectrum sources. These complex clues can thus aid in the detection and monitoring of IMBHs in the nearby universe.
Joint Audio/Text Training for Transformer Rescorer of Streaming Speech Recognition

Recently, there has been an increasing interest in two-pass streaming end-to-end speech recognition (ASR) that incorporates a 2nd-pass rescoring model on top of the conventional 1st-pass streaming ASR model to improve recognition accuracy while keeping latency low. One of the latest 2nd-pass rescoring model, Transformer Rescorer, takes the n-best initial outputs and audio embeddings from the 1st-pass model, and then choose the best output by re-scoring the n-best initial outputs. However, training this Transformer Rescorer requires expensive paired audio-text training data because the model uses audio embeddings as input. In this work, we present our Joint Audio/Text training method for Transformer Rescorer, to leverage unpaired text-only data which is relatively cheaper than paired audio-text data. We evaluate Transformer Rescorer with our Joint Audio/Text training on Librispeech dataset as well as our large-scale in-house dataset and show that our training method can improve word error rate (WER) significantly compared to standard Transformer Rescorer without requiring any extra model parameters or latency.
WELL-POSEDNESS AND PROPERTIES OF THE FLOW FOR SEMILINEAR BOUNDARY CONTROL SYSTEMS

We derive conditions for well-posedness of semilinear evolution equations with unbounded input operators and the corresponding boundary control systems. Based on this, we provide suﬃcient conditions for Lipschitz continuity of the ﬂow map, bounded-implies-continuation property, bounded- ness of reachability sets, etc. These properties represent a basic toolbox for stability and robustness analysis of semilinear boundary control systems. We cover systems governed by general C 0 -semigroups, and analytic semigroups that may have both boundary and distributed disturbances. We illus- trate our ﬁndings on an example of a Burger’s equation with nonlinear local dynamics and both distributed and boundary disturbances.
Complex topological features of reservoirs shape learning performances in bio-inspired recurrent neural networks

Recurrent networks are a special class of artiﬁcial neural systems that use their internal states to perform computing tasks for machine learning. One of its state-of-the-art developments, i.e. reservoir computing (RC), uses the internal structure – usually a static network with random structure – to map an input signal into a nonlinear dynamical system deﬁned in a higher dimensional space. Reservoirs are characterized by nonlinear interactions among their units and their ability to store information through recurrent loops, allowing to train artiﬁcial systems to learn task-speciﬁc dynamics. However, it is fundamentally unknown how the random topology of the reservoir aﬀects the learning performance. Here, we ﬁll this gap by considering a battery of synthetic networks – characterized by diﬀerent topological features – and 45 empirical connectomes – sampled from brain regions of organisms belonging to 8 diﬀerent species – to build the reservoir and testing the learning performance against a prediction task with a variety of complex input signals. We ﬁnd nontrivial correlations between RC performances and both the number of nodes and rank of the covariance matrix of activation states, with performance depending on the nature – stochastic or deterministic – of input signals. Remarkably, the modularity and the link density of the reservoir are found to aﬀect RC performances: these results cannot be predicted by models only accounting for simple topological features of the reservoir. Overall, our ﬁndings highlight that the complex topological features characterizing biophysical computing systems such as connectomes can be used to design eﬃcient bio-inspired artiﬁcial neural networks.
Empowering Data Centers for Next Generation Trusted Computing

Modern data centers have grown beyond CPU nodes to provide domain-speciﬁc accelerators such as GPUs and FPGAs to their customers. From a security standpoint, cloud customers want to protect their data. They are willing to pay additional costs for trusted execution environments such as enclaves provided by Intel SGX and AMD SEV. Unfortu-nately, the customers have to make a critical choice—either use domain-speciﬁc accelerators for speed or use CPU-based conﬁdential computing solutions. To bridge this gap, we aim to enable data-center scale conﬁdential computing that expands across CPUs and accelerators. We argue that having wide-scale TEE-support for accelerators presents a technically easier solution, but is far away from being a reality. Instead, our hybrid design provides enclaved execution guarantees for computation distributed over multiple CPU nodes and devices with/without TEE support. Our solution scales gracefully in two dimensions—it can handle a large number of heterogeneous nodes and it can accommo-date TEE-enabled devices as and when they are available in the future. We observe marginal overheads of 0 . 42–8% on real-world AI data center workloads that are independent of the number of nodes in the data center. We add custom TEE support to two accelerators (AI and storage) and integrate it into our solution, thus demonstrating that it can cater to future TEE devices.
Igusa-Todorov dimensions and derived dimensions of artin algebras

We introduce the notion of Igusa-Todorov dimension, and prove that this dimension is an invariant under derived equivalent. Igusa-Todorov dimension also give a characterization of Igusa-Todorov algebras, and in fact that an artin algebra is an Igusa-Todorov algebra if and only if its Igusa-Todorov dimension at most 1 . We point out some artin algebras having Igusa-Todorov dimension more than one. We also give a new upper bound for the derived dimension of ( m, n )-Igusa-Todorv algebra.
SPEECH EMOTION RECOGNITION USING MULTI-TASK LEARNING AND A MULTIMODAL DYNAMIC FUSION NETWORK

Emotion Recognition (ER) aims to classify human utterances into different emotion categories. Based on early-fusion and self-attention-based multimodal interaction between text and acoustic modalities, in this paper, we propose MMER, a multimodal multitask learning approach for ER from individual utterances in isolation. Our proposed MMER leverages a multimodal dynamic fusion network that adds minimal parameters over an existing speech encoder to leverage the semantic and syntactic properties hidden in text. Experiments on the IEMOCAP benchmark show that our proposed model achieves state-of-the-art performance. In addition, strong baselines and ablation studies prove the effectiveness of our proposed approach. We make our code publicly available on GitHub 1 .
AI-powered mechanisms as judges: Breaking ties in chess and beyond

Recently, Artiﬁcial Intelligence (AI) technology use has been rising in sports. For example, to reduce staﬀ during the COVID-19 pandemic, major tennis tournaments replaced human line judges with Hawk-Eye Live technology. AI is now ready to move beyond such mundane tasks, however. A case in point and a perfect application ground is chess. To reduce the growing incidence of draws, many elite tournaments have resorted to fast chess tiebreakers. However, these tiebreakers are vulnerable to strategic manipulation , e.g., in the last game of the 2018 World Chess Championship, Carlsen—in a signiﬁcantly advantageous position—oﬀered a draw to Caruana (whom accepted the oﬀer) to proceed to fast chess tiebreaks in which Carlsen had even better odds of winning the championship. By contrast, we prove that our AI-based method can serve as a judge to break ties without being vulnerable to such manipulation. It relies on measuring the diﬀerence between the evaluations of a player’s actual move and the best move as deemed by a powerful chess engine. If there is a tie, the player with the higher quality measure wins the tiebreak. We generalize our method to all competitive sports and games in which AI’s superiority is—or can be—established.
Multidimensional Astrophysical Structural and Dynamical Analysis. I. Development of a Nonlinear Finite Element Approach

A new field of numerical astrophysics is introduced that addresses the solution of large multidimensional structural or slowly evolving problems (rotating stars, interacting binaries, thick advective accretion disks, four-dimensional spacetimes, etc.), as well as rapidly evolving systems. The technique employed is the finite element method (FEM), which has been used to solve engineering structural problems for more than three decades. The approach developed here has the following key features: 1. The computational mesh can extend into the time dimension, as well as space—generally, only a few cells deep for most (flat-space) astrophysical problems, but throughout spacetime for solving Einstein's field equations. 2. When time is treated as a mesh dimension, virtually all equations describing the astrophysics of continuous media, including the field equations, can be written in a compact form similar to that routinely solved by most engineering finite element codes (albeit for nonlinear equations in a four-dimensional spacetime instead of linear ones in two or three space dimensions): the divergence of a generalized stress tensor equals a generalized body-force vector, both of which are functions only of position, the state variables, and their gradients. 3. The transformations that occur naturally in the four-dimensional FEM possess both coordinate and boost features, such that: (A) although the computational mesh may have a complex, nonanalytic, curvilinear structure, and may be adapted to the geometry of the problem, the physical equations still can be written in a simple coordinate system that is independent of the mesh structure; and (B) if the mesh has a complex flow velocity with respect to coordinate space, the transformations will form the proper advective derivatives, automatically converting the equations to arbitrary Lagrangian-Eulerian. 4. Only relatively simple differential equations need to be encoded. The complex difference equations on the arbitrary curvilinear grid are generated automatically by the FEM integrals. A different integration method must be used for equations of odd and even orders. This first paper concentrates on developing a robust and widely applicable set of techniques using the nonlinear FEM, and presents some examples. The second paper in this series will deal with making the method fast and efficient, so that large, astrophysically interesting computational meshes can be employed.
A study of high-redshift AGN feedback in SZ cluster samples

We present a study of AGN feedback at higher redshifts ($0.3 0.6$, implying a transition from HERG-mode accretion to lower-power low-excitation radio galaxy (LERG)-mode accretion at intermediate redshifts. Additionally, we use local radio-to-jet power scaling relations to estimate feedback power and find that half of the cooling flow systems in our sample probably have enough heating to balance cooling. However, we postulate that the local relations are likely not well suited to predict feedback power in high-luminosity HERGs, as they are derived from samples composed mainly of lower-luminosity LERGs.
A slow bar in the dwarf irregular galaxy NGC 3741

Using the Tremaine-Weinberg method, we measure the speed of the HI bar seen in the disk of NGC 3741. NGC 3741 is an extremely gas rich galaxy with an Hi disk which extends to about 8.3 times its Holmberg radius. It is also highly dark matterdominated. Our calculated value of the pattern speed p is 17.1 3.4 km s 1 kpc 1 . We also nd the ratio of the co-rotation radius to the bar semi-major axis to be (1.6 0.3), indicating a slow bar. This is consistent with bar models in which dynamical friction results in a slow bar in dark matter dominated galaxies.
The negative influence of chronic alcohol abuse on acute cognitive recovery after a traumatic brain injury

ABSTRACT Objective Cognitive recovery after a traumatic brain injury (TBI) may be negatively affected by a prior alcohol use disorder (AUD). This study aims to compare the cognitive recovery of patients who had comorbid TBI and AUD relative to TBI alone and investigate the influence of blood alcohol level (BAL) at hospital admission on this recovery. Method The sample consisted of 42 patients who had sustained a TBI (mild or moderate) and had an AUD diagnosis (TBI+AUD), and 42 patients who had sustained a TBI alone (TBI). The Brief Cognitive Exam in Traumatology (EXACT), designed to evaluate cognitive functions in the acute phase of TBI was administered (± 2 weeks post-injury). Results After controlling for BAL at admission, the TBI+AUD group had a lower EXACT total score compared to the TBI group. The negative influence of age on the results was more pronounced in the TBI+AUD group. The number of intoxicated patients at admission was also higher in this group, although there was no correlation between BAL at admission and cognitive outcome. Conclusion The presence of an AUD diagnosis seems to exert a greater negative influence on cognitive recovery following a mild/moderate TBI than BAL at admission, especially in older patients.
para-Aminobenzoic Acid Biosynthesis Is Required for Listeria monocytogenes Growth and Pathogenesis

Biosyntheses of para-aminobenzoic acid (PABA) and its downstream folic acid metabolites are essential for one-carbon metabolism in all life forms and the targets of sulfonamide and trimethoprim antibiotics. In this study, we identified and characterized two genes (pabA and pabBC) required for PABA biosynthesis in Listeria monocytogenes. ABSTRACT Biosyntheses of para-aminobenzoic acid (PABA) and its downstream folic acid metabolites are essential for one-carbon metabolism in all life forms and the targets of sulfonamide and trimethoprim antibiotics. In this study, we identified and characterized two genes (pabA and pabBC) required for PABA biosynthesis in Listeria monocytogenes. Mutants in PABA biosynthesis were able to grow normally in rich media but not in defined media lacking PABA, but growth was restored by the addition of PABA or its downstream metabolites. PABA biosynthesis mutants were attenuated for intracellular growth in bone marrow-derived macrophages, produced extremely small plaques in fibroblast monolayers, and were highly attenuated for virulence in mice. PABA biosynthesis genes were upregulated upon infection and induced during growth in broth in a strain in which the master virulence regulator, PrfA, was genetically locked in its active state (PrfA*). To gain further insight into why PABA mutants were so attenuated, we screened for transposon-induced suppressor mutations that formed larger plaques. Suppressor mutants in relA, which are predicted to have higher levels of (p)ppGpp, and mutants in codY, which is a GTP-binding repressor of many biosynthetic genes, partially rescued the plaque defect but, notably, restored the capacity of the mutants to escape from phagosomes and induce the polymerization of host cell actin. However, these suppressor mutant strains remained attenuated for virulence in mice. These data suggest that even though folic acid metabolites exist in host cells and might be available during infection, de novo synthesis of PABA is required for L. monocytogenes pathogenesis.
Typical viewpoints of objects are better detected than atypical ones

Previous work has claimed that canonical viewpoints of objects are more readily perceived than noncanonical viewpoints. However, all of these studies required participants to identify the object, a late perceptual process at best and arguably a cognitive process (Pylyshyn, 1999). Here, we extend this work to early vision by removing the explicit need to identify the objects. In particular, we asked participants to make an intact/scrambled discrimination of briefly presented objects that were viewed from either typical or atypical viewpoints. Notably, participants did not have to identify the object; only discriminate it from noise (scrambled). Participants were more sensitive in discriminating objects presented in typically encountered orientations than when objects were presented in atypical depth rotations (Experiment 1). However, the same effect for objects presented in atypical picture plane rotations (as opposed to typical ones) did not reach statistical significance (Experiments 2 and 3), suggesting that particular informative views may play a critical role in this effect. We interpret this enhanced perceptibility, for both these items and good exemplars and probable scenes, as deriving from their high real-world statistical regularity.
Variations in the origin of the anterior spinal artery in a Colombian population sample

Based on most neuroanatomical descriptions, the anterior spinal artery (ASA) originates from two small vessels of the vertebral arteries, which are anastomosed just before forming the basilar artery. This study aimed to determine and quantify the possible variants of the origin of the ASA and its trajectory in samples of human brain stems. Male brain stems with the superior portion of the spinal cord until myelomera C3 of 23 adult human, and no evident morphological alterations were selected. The brain stems were collected for three years and fixed in a 10% formalin solution at the Anatomy Laboratory of the Universidad de Caldas (Colombia). Five samples (21.7%) had variations in the origin and trajectory of the anterior spinal artery. The variations in the origin of the ASA could generate morphofunctional advantages instead of leading to complications. That is the case when there are two anterior spinal arteries since it would increase tissue perfusion, thus protecting part of the spinal cord from ischemic pathologies. It is essential to consider the variations that may exist in the supply of the anterior region of the spinal cord for clinical and surgical assessments due to variations in its supplied territory.