Miracle-dz/UniFAR-FTUs huggingface dataset:
Is attention to bounding boxes all you need for pedestrian action prediction?

The human driver is no longer the only one concerned with the complexity of the driving scenarios. Autonomous vehicles (AV) are similarly becoming involved in the process. Nowadays, the development of AV in urban places raises essential safety concerns for vulnerable road users (VRUs) such as pedestrians. Therefore, to make the roads safer, it is critical to classify and predict the pedestrians’ future behavior. In this paper, we present a framework based on multiple variations of the Transformer models able to predict the pedestrian street-crossing decision-making based on the dynamics of its initiated trajectory. We showed that using solely bounding boxes as input features can outperform the previous state-of-the-art results by reaching a prediction accuracy of 91% and an F1-score of 0.83 on the PIE dataset. In addition, we introduced a large-size simulated dataset (CP2A) using CARLA for action prediction. Our model has similarly reached high accuracy (91%) and F1-score (0.91) on this dataset. Interestingly, we showed that pre-training our Transformer model on the CP2A dataset and then fine-tuning it on the PIE dataset is beneficial for the action prediction task. Finally, our model’s results are successfully supported by the “human attention to bounding boxes” experiment which we created to test humans’ ability for pedestrian action prediction without the need for environmental context. The code for the dataset and the models is available at: https://github.com/ linaashaji/Action_Anticipation
Unpaired cross-modality educed distillation (CMEDL) applied to CT lung tumor segmentation

—Accurate and robust segmentation of lung cancers from CTs is needed to more accurately plan and deliver radiotherapy and to measure treatment response. This is particularly difﬁ-cult for tumors located close to mediastium, due to low soft-tissue contrast. Therefore, we developed a new cross-modality educed distillation (CMEDL) approach, using unpaired CT and MRI scans, whereby a teacher MRI network guides a student CT network to extract features that signal the difference between foreground and background. Our contribution eliminates two requirements of distillation methods: (i) paired image sets by using an image to image (I2I) translation and (ii) pre-training of the teacher network with a large training set by using concurrent training of all networks. Our framework uses an end-to-end trained unpaired I2I translation, teacher, and student segmentation networks. Our framework can be combined with any I2I and segmentation network. We demonstrate our framework’s feasibility using 3 segmentation and 2 I2I methods. All networks were trained with 377 CT and 82 T2w MRI from different sets of patients. Ablation tests and different strategies for incorporating MRI information into CT were performed. Accuracy was measured using Dice similarity (DSC), surface Dice (sDSC), and Hausdorff distance at the 95 th percentile (HD95). The CMEDL approach was signiﬁcantly (p < 0.001) more accurate than non-CMEDL methods, quantitatively and visually. It produced the highest segmentation accuracy (sDSC of 0.83 ± 0.16 and HD95 of 5.20 ± 6.86mm). CMEDL was also more accurate than using either pMRI’s or the combination of CT’s with pMRI’s for segmentation.
Anna L. Choi and Tze Leung Lai's contribution to the ‘First Discussion Meeting on Statistical Aspects of the Covid‐19 Pandemic’

Knowledge of the current state of economies, how theyrespond to COVID-19 mitigations and indicators, andwhat the future might hold for them is important.We use recently developed generalised network autoregres-sive (GNAR) models, using trade-determined networks, tomodel and forecast the Purchasing Managers' Indices for anumber of countries. We use networks that link countrieswhere the links themselves, or their weights, are deter-mined by the degree of export trade between the coun-tries. We extend these models to include node-specific timeseries exogenous variables (GNARX models), using this toincorporate COVID-19 mitigation stringency indices andCOVID-19 death rates into our analysis. The highly par-simonious GNAR models considerably outperform vectorautoregressive models in terms of mean-squared forecast-ing error and our GNARX models themselves outperform GNAR ones. Further mixed frequency modelling predictsthe extent to which that the UK economy will be affectedby harsher, weaker or no interventions
Hybrid Analog and Digital Beamforming Design for Channel Estimation in Correlated Massive MIMO Systems

In this paper, we study the channel estimation problem in correlated massive multiple-input-multiple-output (MIMO) systems with a reduced number of radio-frequency (RF) chains. Importantly, other than the knowledge of channel correlation matrices, we make no assumption as to the structure of the channel. To address the limitation in the number of RF chains, we employ hybrid beamforming, comprising a low dimensional digital beamformer followed by an analog beamformer implemented using phase shifters. Since there is no dedicated RF chain per transmitter/receiver antenna, the conventional channel estimation techniques for fully-digital systems are impractical. Exploiting the fact that the channel entries are uncorrelated in its eigen-domain, we seek to estimate the channel entries in this domain. Due to the limited number of RF chains, channel estimation is typically performed in multiple time slots. Under a total energy budget, we aim to design the hybrid precoder and the receive combiner in each training time slot, in order to estimate the channel using the minimum mean squared error criterion. To this end, we choose the precoder and combiner in each time slot such that they are aligned to transmitter and receiver eigen-directions, respectively. Further, we derive a water-filling type expression for the optimal energy allocation at each time slot. This expression illustrates that, with a low training energy budget, only significant components of the channel need to be estimated. Simulation results indicate that the proposed channel estimation scheme can efficiently estimate correlated massive MIMO channels within a few training time slots.
Real-Time Violence Detection Using CNN-LSTM

Violence rates however have been brought down about 57% during the span of the past 4 decades yet it doesn't change the way that the demonstration of violence actually happens, unseen by the law. Violence can be mass controlled sometimes by higher authorities, however, to hold everything in line one must"Microgovern"over each movement occurring in every road of each square. To address the butterfly effects impact in our setting, I made a unique model and a theorized system to handle the issue utilizing deep learning. The model takes the input of the CCTV video feeds and after drawing inference, recognizes if a violent movement is going on. And hypothesized architecture aims towards probability-driven computation of video feeds and reduces overhead from naively computing for every CCTV video feeds.
Modeling Cooperation and Competition in Biological Communities

The far-reaching consequences of ecological interactions in the dynamics of biological communities remain an intriguing subject. For decades, competition has been a cornerstone in ecological processes, but mounting evidence shows that cooperation does also contribute to the structure of biological communities. Here, we propose a simple deterministic model for the study of the effects of facilitation and competition in the dynamics of such systems. The simultaneous inclusion of both effects produces rich dynamics and captures the context dependence observed in the formation of ecological communities. Our findings reproduce relevant aspects in plant succession and highlight the role of facilitation mechanisms in species coexistence and conservation efforts.
Lower-luminosity Obscured AGN Host Galaxies Are Not Predominantly in Major-merging Systems at Cosmic Noon

For over 60 yr, the scientific community has studied actively growing central supermassive black holes (active galactic nuclei, AGNs), but fundamental questions on their genesis remain unanswered. Numerical simulations and theoretical arguments show that black hole growth occurs during short-lived periods (∼107–108 yr) of powerful accretion. Major mergers are commonly invoked as the most likely dissipative process to trigger the rapid fueling of AGNs. If the AGN–merger paradigm is true, we expect galaxy mergers to coincide with black hole accretion during a heavily obscured AGN phase (N H > 1023 cm−2). Starting from one of the largest samples of obscured AGNs at 0.5 < z < 3.1, we select 40 nonstarbursting lower-luminosity obscured AGNs. We then construct a one-to-one matched redshift and near-IR magnitude-matched nonstarbursting inactive galaxy control sample. Combining deep color Hubble Space Telescope imaging and a novel method of human classification, we test the merger–AGN paradigm prediction that heavily obscured AGNs are strongly associated with galaxies undergoing a major merger. On the total sample of 80 galaxies, we estimate each individual classifier’s accuracy at identifying merging galaxies/postmerging systems and isolated galaxies. We calculate the probability of each galaxy being in either a major merger or an isolated system, given the accuracy of the human classifiers and the individual classifications of each galaxy. We do not find statistically significant evidence that obscured AGNs at cosmic noon are predominantly found in systems with evidence of significant merging/postmerging features.
AGNIFS survey of local AGN: GMOS-IFU data and outflows in 30 sources

We analyse optical data cubes of the inner kiloparsec of 30 local (z ≤ 0.02) active galactic nucleus (AGN) hosts that our research group, AGNIFS, has collected over the past decade via observations with the integral field units of the Gemini Multi-Object Spectrographs. Spatial resolutions range between 50 and 300 pc and spectral coverage is from 4800 or 5600 to 7000 Å, at velocity resolutions of ≈50 $\rm ~km~s^{-1}$. We derive maps of the gas excitation and kinematics, and determine the AGN ionization axis – which has random orientation relative to the galaxy – and the kinematic major axes of the emitting gas. We find that rotation dominates the gas kinematics in most cases, but is disturbed by the presence of inflows and outflows. Outflows have been found in 21 nuclei, usually along the ionization axis. The gas velocity dispersion is traced by W80 (velocity width encompassing 80 per cent of the line flux), adopted as a tracer of outflows. In seven sources, W80 is enhanced perpendicularly to the ionization axis, indicating lateral expansion of the outflow. We have estimated mass-outflow rates $\dot{M}$ and powers $\dot{E}$, finding median values of $\log \, [\dot{M}/({\rm \, M_\odot \, yr^{-1}})]=-2. 1_{-1.0}^{+1.6}$ and $\log \, [\dot{E}/({\rm \, erg\, s^{-1}})]=38. 5_{-0.9}^{+1.8}$, respectively. Both quantities show a mild correlation with the AGN luminosity (LAGN). $\dot{E}$ is of the order of 0.01 LAGN for four sources, but much lower for the majority (nine) of the sources, with a median value of $\log \, [\dot{E}/L_{\rm AGN} ]=-5. 34_{-0.9}^{+3.2}$, indicating that typical outflows in the local Universe are unlikely to significantly impact their host galaxy evolution.
LIGO–Virgo correlations between mass ratio and effective inspiral spin: testing the active galactic nuclei channel

Observations by LIGO–Virgo of binary black hole mergers suggest a possible anticorrelation between black hole mass ratio (q = m2/m1) and the effective inspiral spin parameter χeff, the mass-weighted spin projection on to the binary orbital angular momentum. We show that such an anticorrelation can arise for binary black holes assembled in active galactic nuclei (AGNs) due to spherical and planar symmetry-breaking effects. We describe a phenomenological model in which (1) heavier black holes live in the AGN disc and tend to spin-up into alignment with the disc; (2) lighter black holes with random spin orientations live in the nuclear spheroid; (3) the AGN disc is dense enough to rapidly capture a fraction of the spheroid component, but small in radial extent to limit the number of bulk disc mergers; (4) migration within the disc is non-uniform, likely disrupted by feedback from migrators or disc turbulence; (5) dynamical encounters in the disc are common and preferentially disrupt binaries that are retrograde around their centre of mass, particularly at stalling orbits, or traps. Comparisons of predictions in (q, χeff) parameter space for the different channels may allow us to distinguish their fractional contributions to the observed merger rates.
The Magnetic Mechanism for Hotspot Reversals in Hot Jupiter Atmospheres

Magnetically driven hotspot variations (which are tied to atmospheric wind variations) in hot Jupiters are studied using nonlinear numerical simulations of a shallow-water magnetohydrodynamic (SWMHD) system and a linear analysis of equatorial SWMHD waves. In hydrodynamic models, mid-to-high-latitude geostrophic circulations are known to cause a net west-to-east equatorial thermal energy transfer, which drives hotspot offsets eastward. We find that a strong toroidal magnetic field can obstruct these energy transporting circulations. This results in winds aligning with the magnetic field and generates westward Lorentz force accelerations in hotspot regions, ultimately causing westward hotspot offsets. In the subsequent linear analysis we find that this reversal mechanism has an equatorial wave analogy in terms of the planetary-scale equatorial magneto-Rossby waves. We compare our findings to three-dimensional MHD simulations, both quantitatively and qualitatively, identifying the link between the mechanics of magnetically driven hotspot and wind reversals. We use the developed theory to identify physically motivated reversal criteria, which can be used to place constraints on the magnetic fields of ultra-hot Jupiters with observed westward hotspots.
Social Recognition: Experimental Evidence from Blood Donors

Does social recognition motivate repeat contributors? We conduct large-scale experiments among members of Italy's main blood donors association, testing social recognition both through social media and in peer groups. We experimentally disentangle visibility concerns, peer comparisons and simple ask effects, and we study how exposure to different norms of behavior affects giving. Overall, asking increases giving by 20.4 percent. However, a simple ask to donate is at least as effective as asks that emphasize social recognition. Using peer groups, we show that visibility concerns do not generate additional donations, and we find that donations respond weakly and non-monotonically to social norms of giving. In an original study and two subsequent replications, we find no evidence that donors give blood for the prospect of their donations being observed by others on social media. Overall, our findings caution against over-reliance on social recognition to promote good citizenship.
Planckian Dissipation in Metals

We review the appearance of the Planckian time τPl = ~/(kBT ) in both conventional and unconventional metals. We give a pedagogical discussion of the various different timescales (quasiparticle, transport, many-body) that characterize metals, emphasizing conditions under which these times are the same or different. Throughout, we have attempted to clear up aspects of the problem that had been confusing us, in the hope that this helps the reader as well. We discuss the possibility of a Planckian bound on dissipation from both a quasiparticle and a many-body perspective. Planckian quasiparticles can arise naturally from a combination of inelastic scattering and mass renormalization. Many-body dynamics, on the other hand, is constrained by the basic timeand lengthscales of local thermalization. 1 ar X iv :2 10 7. 07 80 2v 3 [ co nd -m at . s tr -e l] 1 8 Ju n 20 22
Effect of Intravenous Dexmedetomidine on Spinal Anesthesia

Background Subarachnoid block (SAB) with hyperbaric bupivacaine is routinely administered for abdominal and lower limb surgeries. Various agents have been used intrathecally as adjuvants to local anesthetic to increase efficacy and prolong the duration of SAB, among which opioids and α2 agonists are most commonly used. Intravenously administered dexmedetomidine has also been shown to prolong the duration of sensory and motor blockade obtained with subarachnoid block. Methods A total of 212 adult patients scheduled to undergo abdominal or lower limb surgeries were enrolled in this prospective, double-blind, randomized study. They were divided into two equal groups to receive either intravenous dexmedetomidine (group D) 1μg/kg loading followed by maintenance at 0. 5μg/kg or intravenous normal saline (group C) after 15 mins of subarachnoid block; no other intraoperative sedatives were used. The onset of sensory block and motor block, and the highest level of sensory block attained were assessed. The time taken for sensory regression to L1, Modified Bromage scale 0, and rescue analgesia requirement were assessed. Results Patients receiving intravenous dexmedetomidine showed no significant change in terms of onset and level of sensory block (P-0.774) and the onset of motor block (P-0.738). The time taken for sensory regression to L1 was significantly prolonged (P-0.000). Also, the time taken to achieve Modified Bromage scale of 0 and time taken for rescue analgesia was significantly prolonged (P-0.000). Conclusion Intravenous dexmedetomidine prolonged the duration of sensory and motor block, and also appears to provide sedation with easy arousability and analgesia postoperatively while maintaining hemodynamic stability with no significant side effects.
Neuroimaging in Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is one of the most common forms of dementia. It can present as neurocognitive decline, visual hallucinations, and concomitant symptoms of rapid eye movement (REM) sleep behavior disorder. Early diagnosis remains one of the cornerstones of managing this form of neurocognitive disorder but, often, making an early and accurate diagnosis can prove to be challenging. For this article, our goal was to review the utility of various neuroimaging modalities in making a swift and accurate diagnosis of DLB. We used PubMed to look for helpful, informative, and peer-reviewed articles. We discussed the role of a plethora of different imaging techniques, ranging from structural imaging like computed tomography (CT) and magnetic resonance imaging (MRI) to molecular imaging (single-photon emission computed tomography, positron emission to- tomography) as a diagnostic tool for DLB. We arrived at the conclusion that these novel neuroimaging modalities have already proven to be very helpful in ruling out differentials and making an early diagnosis of DLB. However, ongoing research is required to increase the diagnostic accuracy, leading to the early identification and treatment of DLB.
Bidding and Pricing in Budget and ROI Constrained Markets

In online advertising markets, setting budget and return on investment (ROI) constraints are two prevalent ways to help advertisers (i.e. buyers) utilize limited monetary resources efficiently. In this work, we provide a holistic view of ROI and budget constrained markets. We first tackle the buyer’s bidding problem subject to both budget and ROI constraints in repeated second-price auctions. We show that the optimal buyer hindsight policy admits a “threshold-based" structure that suggests the buyer win all auctions during which her valuation-to-expenditure ratio is greater than some threshold. We further propose a threshold-based bidding framework that aims to mimic the hindsight bidding policy by learning its threshold. We show that when facing stochastic competition, our algorithm guarantees the satisfaction of both budget and ROI constraints and achieves sublinear regret compared to the optimal hindsight policy. Next, we study the seller’s pricing problem against an ROI and budget constrained buyer. We establish that the seller’s revenue function admits a bell-shaped structure, and then further propose a pricing algorithm that utilizes an episodic binary-search procedure to identify a revenue-optimal selling price. During each binary search episode, our pricing algorithm explores a particular price, allowing the buyer’s learning algorithm to adapt and stabilize quickly. This, in turn, allows our seller algorithm to achieve sublinear regret against adaptive buyer algorithms that quickly react to price changes.
Next Generation Design and Prospects for Cannex

The Casimir And Non-Newtonian force EXperiment (Cannex) implements the unique geometry of macroscopic plane parallel plates that guarantees an optimum sensitivity with respect to interfacial forces and their gradients. Based on experience from the recently completed proof-of-principle phase, we have started a re-design of the setup aiming to reduce systematic effects and maximize the achievable sensitivity. Several propositions have been made to measure Casimir forces in and out of thermal equilibrium, hypothetical axion and axion-like dark matter interactions, and forces originating from chameleon or symmetron dark energy interactions. In the present article, we give details on the design for the next implementation stage of Cannex and discuss the experimental opportunities, as well as limitations expected for this new setup.
Internet-Augmented Dialogue Generation

The largest store of continually updating knowledge on our planet can be accessed via internet search. In this work we study giving access to this information to conversational agents. Large language models, even though they store an impressive amount of knowledge within their weights, are known to hallucinate facts when generating dialogue (Shuster et al., 2021); moreover, those facts are frozen in time at the point of model training. In contrast, we propose an approach that learns to generate an internet search query based on the context, and then conditions on the search results to finally generate a response, a method that can employ up-to-the-minute relevant information. We train and evaluate such models on a newly collected dataset of human-human conversations whereby one of the speakers is given access to internet search during knowledgedriven discussions in order to ground their responses. We find that search-query based access of the internet in conversation provides superior performance compared to existing approaches that either use no augmentation or FAISS-based retrieval (Lewis et al., 2020b).
Dissolution of Kaolinite Induced by Citric Acid in HAc-NH4Ac Buffer Solution

The experiment was conducted to investigate the dissolution characteristics of Kaolinite induced by citric acids with batch method in HAc-NH4Ac buffer solution.The results showed the concentrations of Al and Si from Kaolinite increased with concentrations of citric acids and acidity of reactive solution and reactive time.Ligand-promoted Kaolinite dissolution rates(RL) declined with the acidity enhancement when the concentration of citric acids was lower 1mM.The rates(RL) enhanced with the acidity enhancement when the concentration of citric acids was not less than 5mM.The contribution of Ligand-promoted Kaolinite dissolution was lower with the acidity enhancement,but was primary compared with that of proton-promoted Kaolinite.