Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Communications: Principles and Challenges

Semantic communication, regarded as the breakthrough beyond the Shannon paradigm, aims at the successful transmission of semantic information conveyed by the source rather than the accurate reception of each single symbol or bit regardless of its meaning. This article provides an overview on semantic communications. After a brief review of Shannon information theory, we discuss semantic communications with theory, framework, and system design enabled by deep learning. Different from the symbol/bit error rate used for measuring conventional communication systems, performance metrics for semantic communications are also discussed. The article concludes with several open questions in semantic communications.
ALMA Measures Molecular Gas Reservoirs Comparable to Field Galaxies in a Low-mass Galaxy Cluster at z = 1.3

We report the serendipitous discovery of an overdensity of CO emitters in an X-ray-identified cluster (Log10 M halo/M ⊙ ∼ 13.6 at z = 1.3188) using ALMA. We present spectroscopic confirmation of six new cluster members exhibiting CO(2–1) emission, adding to two existing optical/IR spectroscopic members undetected in CO. This is the lowest-mass cluster to date at z > 1 with molecular gas measurements, bridging the observational gap between galaxies in the more extreme, well-studied clusters (Log10 M halo/M ⊙ ≳ 14) and those in group or field environments at cosmic noon. The CO sources are concentrated on the sky (within ∼1 arcmin diameter) and phase space analysis indicates the gas resides in galaxies already within the cluster environment. We find that CO sources sit in similar phase space as CO-rich galaxies in more massive clusters at similar redshifts (have similar accretion histories) while maintaining field-like molecular gas reservoirs, compared to scaling relations. This work presents the deepest CO survey to date in a galaxy cluster at z > 1, uncovering gas reservoirs down to MH2>1.6×1010 M ⊙ (5σ at 50% primary beam). Our deep limits rule out the presence of gas content in excess of the field scaling relations; however, combined with literature CO detections, cluster gas fractions in general appear systematically high, on the upper envelope or above the field. This study is the first demonstration that low-mass clusters at z ∼ 1–2 can host overdensities of CO emitters with surviving gas reservoirs, in line with the prediction that quenching is delayed after first infall while galaxies consume the gas bound to the disk.
An approximate Bayes factor based high dimensional MANOVA using Random Projections

High-dimensional mean vector testing problems for two or more independent groups remain a very active research area. When the length of the vector mean exceeds the groups’ combined sample sizes, traditional tests are not ap-plicable since they involve the inversion of rank deﬁcient sample covariance matrices. Most approaches considered in the literature overcome this limitation by imposing a structure on the covariance matrices. Unfortunately, these assumptions are often unrealistic and di ﬃ cult to justify in practice. We develop a Bayes factor (BF)-based testing procedure for comparing two or more population means in (very) high dimensional settings while making no a priori assumptions about the structure of the large unknown covariance matrices. Our test is based on random projections (RPs), a popular data perturbation technique. RPs are appealing since they make no assumptions about the form of the dependency across features in the data. Two versions of the Bayes factor-based test statistics are considered. As is common with data perturbation techniques, tests based on a single random projection can be misleading. Thus, our ﬁnal test statistic is based on an ensemble of Bayes factors corresponding to multiple replications of randomly projected data. Both proposed test statistics are compared through a battery of simulation settings. Finally they are applied to the analysis of a publicly available single cell RNA-seq (scRNA-seq) dataset.
Isolated Massive Star Formation in G28.20-0.05

We report high-resolution 1.3 mm continuum and molecular line observations of the massive protostar G28.20-0.05 with Atacama Large Millimeter/submillimeter Array. The continuum image reveals a ring-like structure with 2000 au radius, similar to morphology seen in archival 1.3 cm Very Large Array observations. Based on its spectral index and associated H30α emission, this structure mainly traces ionized gas. However, there is evidence for ∼30 M ⊙ of dusty gas near the main millimeter continuum peak on one side of the ring, as well as in adjacent regions within 3000 au. A virial analysis on scales of ∼2000 au from hot core line emission yields a dynamical mass of ∼80 M ⊙. A strong velocity gradient in the H30α emission is evidence for a rotating, ionized disk wind, which drives a larger-scale molecular outflow. An infrared spectral energy distribution (SED) analysis indicates a current protostellar mass of m * ∼ 40 M ⊙ forming from a core with initial mass M c ∼ 300 M ⊙ in a clump with mass surface density of Σcl ∼ 0.8 g cm−2. Thus the SED and other properties of the system can be understood in the context of core accretion models. A structure-finding analysis on the larger-scale continuum image indicates G28.20-0.05 is forming in a relatively isolated environment, with no other concentrated sources, i.e., protostellar cores, above ∼1 M ⊙ found from ∼0.1 to 0.4 pc around the source. This implies that a massive star can form in relative isolation, and the dearth of other protostellar companions within the ∼1 pc environs is a strong constraint on massive star formation theories that predict the presence of a surrounding protocluster.
ARCLIN: Automated API Mention Resolution for Unformatted Texts

Online technical forums (e.g., StackOverflow) are popular platforms for developers to discuss technical problems such as how to use a specific Application Programming Interface (API), how to solve the programming tasks, or how to fix bugs in their code. These discussions can often provide auxiliary knowledge of how to use the software that is not covered by the official documents. The automatic extraction of such knowledge may support a set of down-stream tasks like API searching or indexing. However, unlike official documentation written by experts, discussions in open forums are made by regular developers who write in short and informal texts, including spelling errors or abbreviations. There are three major challenges for the accurate APIs recognition and linking mentioned APIs from unstructured natural language documents to an entry in the API repository: (1) distinguishing API mentions from common words; (2) identifying API mentions without a fully qualified name; and (3) disambiguating API mentions with similar method names but in a different library. In this paper, to tackle these challenges, we propose an ARCLIN tool, which can effectively distinguish and link APIs without using human annotations. Specifically, we first design an API recognizer to automatically extract API mentions from natural language sentences by a Conditional Random Field (CRF) on the top of a Bi-directional Long Short-Term Memory (Bi-LSTM) module, then we apply a context-aware scoring mechanism to compute the mention-entry similarity for each entry in an API repository. Compared to previous approaches with heuristic rules, our proposed tool without manual inspection outperforms by 8% in a high-quality dataset Py-mention, which contains 558 mentions and 2,830 sentences from five popular Python libraries. To our best knowledge, ARCLIN is the first approach to achieve full automation of API mention resolution from unformatted text without manually collected labels.
Modeling Human Driver Interactions Using an Infinite Policy Space Through Gaussian Processes

This paper proposes a method for modeling human driver interactions that relies on multi-output gaussian processes. The proposed method is developed as a refinement of the game theoretical hierarchical reasoning approach called"level-k reasoning"which conventionally assigns discrete levels of behaviors to agents. Although it is shown to be an effective modeling tool, the level-k reasoning approach may pose undesired constraints for predicting human decision making due to a limited number (usually 2 or 3) of driver policies it extracts. The proposed approach is put forward to fill this gap in the literature by introducing a continuous domain framework that enables an infinite policy space. By using the approach presented in this paper, more accurate driver models can be obtained, which can then be employed for creating high fidelity simulation platforms for the validation of autonomous vehicle control algorithms. The proposed method is validated on a real traffic dataset and compared with the conventional level-k approach to demonstrate its contributions and implications.
High-dimensional variable selection with heterogeneous signals: A precise asymptotic perspective

We study the problem of exact support recovery for high-dimensional sparse linear regression under independent Gaussian design when the signals are weak, rare, and possibly heterogeneous. Under a suitable scaling of the sample size and signal sparsity, we fix the minimum signal magnitude at the information-theoretic optimal rate and investigate the asymptotic selection accuracy of best subset selection (BSS) and marginal screening (MS) procedures. We show that despite the ideal setup, somewhat surprisingly, marginal screening can fail to achieve exact recovery with probability converging to one in the presence of heterogeneous signals, whereas BSS enjoys model consistency whenever the minimum signal strength is above the information-theoretic threshold. To mitigate the computational intractability of BSS, we also propose an efficient two-stage algorithmic framework called ETS (Estimate Then Screen) comprised of an estimation step and gradient coordinate screening step, and under the same scaling assumption on sample size and sparsity, we show that ETS achieves model consistency under the same information-theoretic optimal requirement on the minimum signal strength as BSS. Finally, we present a simulation study comparing ETS with LASSO and marginal screening. The numerical results agree with our asymptotic theory even for realistic values of the sample size, dimension and sparsity.
Design of Placement Delivery Arrays for Coded Caching With Small Subpacketizations and Flexible Memory Sizes

Coded caching is an emerging technique to reduce the data transmission load during the peak-traffic times. In such a scheme, each file in the data center or library is divided into a number of packets to pursue a low broadcasting rate based on the designed placements at each user’s cache. However, the implementation complexity of this scheme increases with the number of packets. It is crucial to design a scheme with a small subpacketization level, while maintaining a relatively low transmission rate. Recently, a combinatorial structure called placement delivery array (PDA) was proposed as an effective tool to design coded caching schemes with a relatively low subpacketization level. This paper proposes a novel PDA construction by selecting proper orthogonal arrays (POAs), which generalizes the existing construction but with a more flexible memory size. Based on the proposed PDA construction, an effective transform is further proposed to enable a coded caching scheme to achieve a smaller subpacketization level. Moreover, two new coded caching schemes with the coded placement are derived. It is shown that the proposed schemes can yield a lower subpacketization level or transmission rate over the benchmark schemes.
Underrepresentation of women in computer systems research

The gender gap in computer science (CS) research is a well-studied problem, with an estimated ratio of 15%–30% women researchers. However, far less is known about gender representation in specific fields within CS. Here, we investigate the gender gap in one large field, computer systems. To this end, we collected data from 72 leading peer-reviewed CS conferences, totalling 6,949 accepted papers and 19,829 unique authors (2,946 women, 16,307 men, the rest unknown). We combined these data with external demographic and bibliometric data to evaluate the ratio of women authors and the factors that might affect this ratio. Our main findings are that women represent only about 10% of systems researchers, and that this ratio is not associated with various conference factors such as size, prestige, double-blind reviewing, and inclusivity policies. Author research experience also does not significantly affect this ratio, although author country and work sector do. The 10% ratio of women authors is significantly lower than the 16% in the rest of CS. Our findings suggest that focusing on inclusivity policies alone cannot address this large gap. Increasing women’s participation in systems research will require addressing the systemic causes of their exclusion, which are even more pronounced in systems than in the rest of CS.
Robust Self-Supervised Audio-Visual Speech Recognition

Audio-based automatic speech recognition (ASR) degrades significantly in noisy environments and is particularly vulnerable to interfering speech, as the model cannot determine which speaker to transcribe. Audio-visual speech recognition (AVSR) systems improve robustness by complementing the audio stream with the visual information that is invariant to noise and helps the model focus on the desired speaker. However, previous AVSR work focused solely on the supervised learning setup; hence the progress was hindered by the amount of labeled data available. In this work, we present a self-supervised AVSR framework built upon Audio-Visual HuBERT (AV-HuBERT), a state-of-the-art audio-visual speech representation learning model. On the largest available AVSR benchmark dataset LRS3, our approach outperforms prior state-of-the-art by ~50% (28.0% vs. 14.1%) using less than 10% of labeled data (433hr vs. 30hr) in the presence of babble noise, while reducing the WER of an audio-based model by over 75% (25.8% vs. 5.8%) on average.
Spectral Efficiency optimization for mmWave Wideband MIMO RIS-assisted Communication

Reconfigurable Intelligent Surfaces (RIS) are passive or semi-passive heterogeneous metasurfaces which consist of many tunable elements. RIS is gaining momentum as a promising new technology to enable transforming the propagation environment into controllable parameter. In this paper, we investigate the co-design of per-subcarrier power allocation matrices and multielement RIS phase shifts in downlink wideband MIMO transmission using 28 GHz frequency bands. Our contributions in improving RIS-aided links include (1) enhanced system modeling with pathloss and blockage modeling, and uniform rectangular array (URA) design, (2) design of gradient ascent co-design algorithm, and (3) asymptotic (Big O) complexity analysis of proposed algorithm and runtime complexity evaluation.
Offsetting Unequal Competition Through RL-Assisted Incentive Schemes

This article investigates the dynamics of competition among organizations with unequal expertise. Multiagent reinforcement learning (MARL) has been used to simulate and understand the impact of various incentive schemes designed to offset such inequality. We design Touch-Mark, a game based on well-known multiagent particle environment, where two teams (weak and strong) with unequal but changing skill levels compete against each other. For training such a game, we propose a novel controller-assisted MARL algorithm C-MADDPG, which empowers each agent with an ensemble of policies along with a supervised controller that by selectively partitioning the sample space and triggers intelligent role division among the teammates. Using C-MADDPG as an underlying framework, we propose an incentive scheme for the weak team such that the final rewards of both teams become the same. We find that despite the incentive, the final reward of the weak team falls short of the strong team. On inspecting, we realize that an overall incentive scheme for the weak team does not incentivize the weaker agents within that team to learn and improve. To offset this, we now specially incentivize the weaker player to learn and, as a result, observe that the weak team beyond an initial phase performs at par with the stronger team. The final goal of this article has been to formulate a dynamic incentive scheme that continuously balances the reward of the two teams. This is achieved by devising an incentive scheme enriched with an RL agent, which takes minimum information from the environment.
The 1S0 Pairing Gap in Neutron Matter

We report ab initio calculations of the S wave pairing gap in neutron matter calculated using realistic nuclear Hamiltonians that include two- and three-body interactions. We use a trial state, properly optimized to capture the essential pairing correlations, from which we extract ground state properties by means of auxiliary field diffusion Monte Carlo simulations. We extrapolate our results to the thermodynamic limit by studying the finite-size effects in the symmetry-restored projected Bardeen-Cooper-Schrieffer (PBCS) theory and compare our results to other ab initio studies done in the past. Our quantum Monte Carlo results for the pairing gap show a modest suppression with respect to the mean-field BCS values. These results can be connected to cold atom experiments, via the unitarity regime where fermionic superfluidity assumes a unified description, and they are important in the prediction of thermal properties and the cooling of neutron stars.
Information geometry of quantum critical submanifolds: Relevant, marginal, and irrelevant operators

We analyze the thermodynamical limit of the quantum metric along critical submanifolds of theory space. Building upon various results previously known in the literature, we relate its singular behavior to normal directions, which are naturally associated with relevant operators in the renormalization group sense. We formulate these results in the language of information theory and differential geometry. We exemplify our theory through the paradigmatic examples of the XY and Haldane models, where the normal directions to the critical submanifolds are seen to be precisely those along which the metric has singular behavior, while for the tangent ones it vanishes -- these directions lie in the kernel of the metric.
A New UV Spectral Feature on Europa: Confirmation of NaCl in Leading-hemisphere Chaos Terrain

Recent visible-wavelength observations of Europa’s surface obtained with the Hubble Space Telescope (HST) revealed the presence of an absorption feature near 450 nm that appears spatially correlated with leading-hemisphere chaos terrain. This feature was interpreted to reflect the presence of irradiated sodium chloride ultimately sourced from the interior. Here, we use ultraviolet spectra also collected with the HST to detect an additional previously unseen absorption near 230 nm, which spatially correlates with the 450 nm feature and with the same leading-hemisphere chaos terrain. We find that the new ultraviolet feature is also well matched by irradiated sodium chloride at Europa-like conditions. Such confirmation of sodium chloride within geologically young regions has important implications for Europa’s subsurface composition.
Global-in-time Boundedness of solution for Cauchy problem to the Parabolic-Parabolic Keller-Segel system with logistic growth

We study global-in-time well-posedness and the behaviour and of the solution to Cauchy problem in the classical Keller-Segel system with logistic term ∂tn−∆n =− χ∇ · (n∇c) + λn− μn τ∂tc−∆c =− c+ n } in R × R, where d ≥ 1, τ, χ, μ > 0 and λ ≥ 0. It’s inspired by a previous result [25, M. Winkler, Commun. Part. Diff. Eq., 35 (2010), 1516-1537], where the global-in-time boundedness of the above Keller-Segel system in smooth bounded convex domains is established for large μ. However, his approach in bounded domain ceases to directly apply in the entire space R, and then they raised an interesting question whether a similar global-in-time boundedness statement remains true of Cauchy problem. In this paper, we answer this open problem by developing local-in-space estimates. More precisely, we prove that the above KellerSegel system possesses a uniquely global-in-time bounded solution for any τ > 0 under the assumption that μ is large. The key point of our proof heavily relies on localization in space of solution caused by “local effect” of L(R)-norm.
Bridging Adversarial and Nonstationary Multi-armed Bandit

In the multi-armed bandit framework, there are two formulations that are commonly employed to handle time-varying reward distributions: adversarial bandit and nonstationary bandit. Although their oracles, algorithms, and regret analysis differ significantly, we provide a unified formulation in this paper that smoothly bridges the two as special cases. The formulation uses an oracle that takes the best-fixed arm within time windows. Depending on the window size, it turns into the oracle in hindsight in the adversarial bandit and dynamic oracle in the nonstationary bandit. We provide algorithms that attain the optimal regret with the matching lower bound.
Teaching concepts utilizing active learning computer environments

Teaching conceptual and qualitative material well is generally considered lacking in undergraduate engineering education. The education of structural engineers, in particular, overemphasizes the role of structural analysis. This thesis proposes a solution to encourage conceptual understanding through effective interactive computeraided learning tools. The approach emulates a tutor to engage the student in a one-onone active experience, utilizing components of multimedia, simulation, and interactive exercises. The student is forced to engage in conversation as she proceeds along a narrative path for a specific conceptual topic. Fully controlling the student's progress ensures she interacts appropriately with the material and yet still allows a sense of experimentation. The essential key in creating an environment having effective engagement is the provision of continuous deep feedback tailored to the student's own interaction with the material. This thesis presents a methodology for building effective learning experiences. The full building process is included: design, implementation, test. This methodology is formulated, a prototype module is created within the teaching context of structural behavior, and this module is then evaluated rigorously for effectiveness in the classroom. Also included is a review of current criticisms and desired qualities of structural engineering education and the basic pedagogy for learning. Thesis Supervisor: Jerome J. Connor Title: Professor of Civil and Environmental Engineering 3