Miracle-dz/UniFAR-FTUs huggingface dataset:
Scene-LSTM: A Model for Human Trajectory Prediction

We develop a human movement trajectory prediction system that incorporates the scene information (Scene-LSTM) as well as human movement trajectories (Pedestrian movement LSTM) in the prediction process within static crowded scenes. We superimpose a two-level grid structure (scene is divided into grid cells each modeled by a scene-LSTM, which are further divided into smaller sub-grids for finer spatial granularity) and explore common human trajectories occurring in the grid cell (e.g., making a right or left turn onto sidewalks coming out of an alley; or standing still at bus/train stops). Two coupled LSTM networks, Pedestrian movement LSTMs (one per target) and the corresponding Scene-LSTMs (one per grid-cell) are trained simultaneously to predict the next movements. We show that such common path information greatly influences prediction of future movement. We further design a scene data filter that holds important non-linear movement information. The scene data filter allows us to select the relevant parts of the information from the grid cell's memory relative to a target's state. We evaluate and compare two versions of our method with the Linear and several existing LSTM-based methods on five crowded video sequences from the UCY [1] and ETH [2] datasets. The results show that our method reduces the location displacement errors compared to related methods and specifically about 80% reduction compared to social interaction methods.
Dependency-to-Dependency Neural Machine Translation

Recent research has proven that syntactic knowledge is effective to improve the performance of neural machine translation (NMT). Most previous work focuses on leveraging either source or target syntax in the recurrent neural network (RNN) based encoder–decoder model. In this paper, we simultaneously use both source and target dependency tree to improve the NMT model. First, we propose a simple but effective syntax-aware encoder to incorporate source dependency tree into NMT. The new encoder enriches each source state with dependence relations from the tree. Then, we propose a novel sequence-to-dependence framework. In this framework, the target translation and its corresponding dependence tree are jointly constructed and modeled. During decoding, the tree structure is used as context to facilitate word generations. Finally, we extend the sequence-to-dependence framework with the syntax-aware encoder to build a dependence-NMT model and apply the dependence-based framework to the Transformer. Experimental results on several translation tasks show that both source and target dependence structures can improve the translation quality and their effects can be accumulated.
A Clockless Synchronisation Framework for Cooperating Mobile Robots

Cooperating mobile robots are real-time systems that often require mutual synchronisation, either to carry out cooperative sensing and actuation, or to improve the quality of wireless communications. Concerning this last aspect, a common technique to improve the communication channel is to eliminate access collisions by allocating predefined disjoint time slots to robots, in a circular list, which is known as Time Division Multiple Access (TDMA). This technique typically requires a global clock to identify each slot. However, this method is not robust with respect to asynchronous transmissions generated by external or joining nodes. Consequently, this work proposes a global TDMA protocol that allows for real-time and guaranteed delivery of messages within deadlines, given its predictable schedule, and that: i) applies to dynamic mesh networks of cooperating mobile robots; ii) synchronises slots in a relative fashion using locally perceived delays of message exchanges that are globalised throughout the network, thus not relying on a global clock; and iii) tolerates external traffic and asynchronous joining robots using underneath standard ad-hoc wireless RF technologies that provide CSMA-type arbitration. We describe our protocol and prove that under common operating conditions all robots eventually reach synchronisation. We also propose a heuristic for the few cases that were not covered by the previous proof, which always led to consensus under extensive simulation testing. To the best of our knowledge, this is the first guaranteed clockless synchronisation approach for ad-hoc networks of mobile robots that works over commodity wireless protocols.
Early effects of previous experience on conscious perception

Abstract Constructive theories of brain function such as predictive coding posit that prior knowledge affects our experience of the world quickly and directly. However, it is yet unknown how swiftly prior knowledge impacts the neural processes giving rise to conscious experience. Here we used an experimental paradigm where prior knowledge augmented perception and measured the timing of this effect with magnetoencephalography (MEG). By correlating the perceptual benefits of prior knowledge with the MEG activity, we found that prior knowledge took effect in the time-window 80–95 ms after stimulus onset, thus reflecting an early influence on conscious perception. The sources of this effect were localized to occipital and posterior parietal regions. These results are in line with the predictive coding framework.
Adaptive Tracking Control of Switched Linear Systems Using Mode-Dependent Average Dwell Time

This paper studies model reference adaptive control for switched linear systems with large parametric uncertainties. An aggregate leakage approach is proposed to develop a novel adaptive law, which overcomes the state-of-the-art assumption of knowing the upper and lower bounds of the parameter uncertainty. In addition, a switching law is developed based on mode-dependent average dwell time scheme, which exploits the information of the known reference model for every subsystem, i.e., average dwell time is realized in a subsystem sense. Based on the proposed time-constraint scheme, switching signals that are less conservative than those based on dwell time and average dwell time can be designed. Global uniform ultimate boundedness of the closed-loop adaptive switched system is guaranteed. Furthermore, the tracking error is shown to be upper bounded and also an ultimate bound is presented. Simulations using NASA GTM aircraft illustrate the proposed method.
Impaired Development and Expansion of Germinal Center Follicular Th Cells in Simian Immunodeficiency Virus–Infected Neonatal Macaques

Germinal center (GC) CD4+ follicular Th (Tfh) cells are critical for cognate B cell help in humoral immune responses to pathogenic infections. Although Tfh cells are expanded or depleted in HIV/SIV-infected adults, the effects of pediatric HIV/SIV infection on Tfh cells remain unclear. In this study, we examined changes in lymphoid follicle formation in lymph nodes focusing on GC Tfh cells, B cell development, and differentiation in SIV-infected neonatal rhesus macaques (Macaca mulatta) compared with age-matched cohorts. Our data showed that follicles and GCs of normal infants rapidly formed in the first few weeks of age, in parallel with increasing GC Tfh cells in various lymphoid tissues. In contrast, GC development and GC Tfh cells were markedly impaired in SIV-infected infants. There was a very low frequency of GC Tfh cells throughout SIV infection in neonates and subsequent infants, accompanied by high viremia, reduction of B cell proliferation/resting memory B cells, and displayed proinflammatory unresponsiveness. These findings indicate neonatal HIV/SIV infection compromises the development of GC Tfh cells, likely contributing to ineffective Ab responses, high viremia, and eventually rapid disease progression to AIDS.
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering

The most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we address the problem of learning vector representations for complex semantic parses that consist of multiple entities and relations. Previous work largely focused on selecting the correct semantic relations for a question and disregarded the structure of the semantic parse: the connections between entities and the directions of the relations. We propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. We show on two data sets that the graph networks outperform all baseline models that do not explicitly model the structure. The error analysis confirms that our approach can successfully process complex semantic parses.
High-Isolation Multimode Multifunction 24-/60-GHz CMOS Dual-Bandpass Filtering T/R Switch

Fully integrated 24-/60-GHz dual-band transmit/receive (T/R) switch capable of bandpass filtering and switching operations in single bands and concurrent dual band, coupled with simultaneous transmission and reception, is developed using a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> SiGe BiCMOS process. The developed bandpass filtering switch can also function as a diplexer with switching functions. The measured insertion losses of the T/R switch are 2.9 and 8.7 dB at 24 and 60 GHz in single-band modes and 3/8.8 dB at 24/60 GHz in concurrent dual-band mode, respectively. The measured isolations are 53 and 43 dB at 24 and 60 GHz in single-band modes and 50/57 dB at 24/60 GHz in concurrent dual-band mode, respectively. The measured inputs <inline-formula> <tex-math notation="LaTeX">$P_{1-\text {dB}}$ </tex-math></inline-formula> are 20.6 and 16.4 dBm at 24 and 60 GHz in single-band modes and 15.9/13 dBm at 24/60 GHz in concurrent dual-band mode, respectively. The measured inputs IP<sub>3</sub> are 23.2 and 22.5 dBm at 24 and 60 GHz in corresponding single-band modes, respectively. The total chip size is <inline-formula> <tex-math notation="LaTeX">$1480~\mu \text{m}\times 520~\mu \text{m}$ </tex-math></inline-formula> excluding all the RF and dc pads.
Contrast-Enhanced Ultrasound for Early Prediction of Response of Breast Cancer to Neoadjuvant Chemotherapy

Abstract Purpose To evaluate the time-intensity curve (TIC) parameters on contrast-enhanced ultrasound (CEUS) for early prediction of the response of breast cancer to neoadjuvant chemotherapy (NAC). Materials and Methods This prospective study included 41 patients with breast cancer. CEUS was performed before and after the first cycle of NAC. TIC parameters were analyzed for different regions of interest (ROIs). ROI 1 targeted the hotspot area of greatest enhancement, ROI 2 delineated the area of hyperenhancement, ROI 3 included the entire tumor on grayscale ultrasound, and ROI 4 encircled the normal parenchyma. The TIC perfusion values for ROI 1, 2, and 3 were divided by the ROI 4 value. Results 11 (26.8 %) of the 41 patients showed a good response (Miller-Payne score 4 or 5) and 30 (73.2 %) showed a minor response (Miller-Payne score 1, 2, or 3). There were significant differences in the wash-out area under the curve, the wash-in and wash-out areas under the curve on ROI 1/4 after the first cycle of NAC, pre-NAC mean transit time local (mTTl) on ROI 2/4, and pre-NAC mTTl on ROI 3/4 between good and minor responders (area under the receiver-operating characteristic curve > 0.70, p < 0.05). Conclusion Some TIC parameters obtained by CEUS may allow prediction of the response of breast cancer to NAC at a very early time point.
Anti-diuretic activity of a CAPA neuropeptide can compromise Drosophila chill tolerance

ABSTRACT For insects, chilling injuries that occur in the absence of freezing are often related to a systemic loss of ion and water balance that leads to extracellular hyperkalemia, cell depolarization and the triggering of apoptotic signalling cascades. The ability of insect ionoregulatory organs (e.g. the Malpighian tubules) to maintain ion balance in the cold has been linked to improved chill tolerance, and many neuroendocrine factors are known to influence ion transport rates of these organs. Injection of micromolar doses of CAPA (an insect neuropeptide) have been previously demonstrated to improve Drosophila cold tolerance, but the mechanisms through which it impacts chill tolerance are unclear, and low doses of CAPA have been previously demonstrated to cause anti-diuresis in insects, including dipterans. Here, we provide evidence that low (femtomolar) and high (micromolar) doses of CAPA impair and improve chill tolerance, respectively, via two different effects on Malpighian tubule ion and water transport. While low doses of CAPA are anti-diuretic, reduce tubule K+ clearance rates and reduce chill tolerance, high doses facilitate K+ clearance from the haemolymph and increase chill tolerance. By quantifying CAPA peptide levels in the central nervous system, we estimated the maximum achievable hormonal titres of CAPA and found further evidence that CAPA may function as an anti-diuretic hormone in Drosophila melanogaster. We provide the first evidence of a neuropeptide that can negatively affect cold tolerance in an insect and further evidence of CAPA functioning as an anti-diuretic peptide in this ubiquitous insect model. Summary: Many insects lose ion balance in the cold. CAPA neuropeptide can slow ion transport and reduce the cold tolerance of a fly.
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition

Deep convolutional neural networks (DCNNs) are currently popular in human activity recognition (HAR) applications. However, in the face of modern artificial intelligence sensor-based games, many research achievements cannot be practically applied on portable devices (i.e., smart phone, VR/AR). DCNNs are typically resource-intensive and too large to be deployed on portable devices, and thus, this limits the practical application of complex activity detection. In addition, since portable devices do not possess high-performance graphic processing units, there is hardly any improvement in Action Game (ACT) experience. Besides, in order to deal with multi-sensor collaboration, all previous HAR models typically treated the representations from different sensor signal sources equally. However, distinct types of activities should adopt different fusion strategies. In this paper, a novel scheme is proposed. This scheme is used to train 2-bit CNNs with weights and activations constrained to {−0.5, 0, 0.5}. It takes into account the correlation between different sensor signal sources and the activity types. This model, which we refer to as DFTerNet, aims at producing a more reliable inference and better trade-offs for practical applications. It is known that quantization of weights and activations can substantially reduce memory size and use more efficient bitwise operations to replace floating or matrix operations to achieve much faster calculation and lower power consumption. Our basic idea is to exploit quantization of weights and activations directly in pre-trained filter banks and adopt dynamic fusion strategies for different activity types. Experiments demonstrate that by using a dynamic fusion strategy, it is possible to exceed the baseline model performance by up to ~5% on activity recognition data sets, such as the OPPORTUNITY and PAMAP2 data sets. Using the quantization method proposed, we were able to achieve performances closer to that of the full-precision counterpart. These results were also verified using the UniMiB-SHAR data set. In addition, the proposed method can achieve $\sim 9\times $ acceleration on CPUs and $\sim 11\times $ memory saving.
The Legacy Effect in Type 2 Diabetes: Impact of Early Glycemic Control on Future Complications (The Diabetes & Aging Study)

OBJECTIVE To examine for a legacy effect of early glycemic control on diabetic complications and death. RESEARCH DESIGN AND METHODS This cohort study of managed care patients with newly diagnosed type 2 diabetes and 10 years of survival (1997–2013, average follow-up 13.0 years, N = 34,737) examined associations between HbA1c <6.5% (<48 mmol/mol), 6.5% to <7.0% (48 to <53 mmol/mol), 7.0% to <8.0% (53 to <64 mmol/mol), 8.0% to <9.0% (64 to <75 mmol/mol), or ≥9.0% (≥75 mmol/mol) for various periods of early exposure (0–1, 0–2, 0–3, 0–4, 0–5, 0–6, and 0–7 years) and incident future microvascular (end-stage renal disease, advanced eye disease, amputation) and macrovascular (stroke, heart disease/failure, vascular disease) events and death, adjusting for demographics, risk factors, comorbidities, and later HbA1c. RESULTS Compared with HbA1c <6.5% (<48 mmol/mol) for the 0-to-1-year early exposure period, HbA1c levels ≥6.5% (≥48 mmol/mol) were associated with increased microvascular and macrovascular events (e.g., HbA1c 6.5% to <7.0% [48 to <53 mmol/mol] microvascular: hazard ratio 1.204 [95% CI 1.063–1.365]), and HbA1c levels ≥7.0% (≥53 mmol/mol) were associated with increased mortality (e.g., HbA1c 7.0% to <8.0% [53 to <64 mmol/mol]: 1.290 [1.104–1.507]). Longer periods of exposure to HbA1c levels ≥8.0% (≥64 mmol/mol) were associated with increasing microvascular event and mortality risk. CONCLUSIONS Among patients with newly diagnosed diabetes and 10 years of survival, HbA1c levels ≥6.5% (≥48 mmol/mol) for the 1st year after diagnosis were associated with worse outcomes. Immediate, intensive treatment for newly diagnosed patients may be necessary to avoid irremediable long-term risk for diabetic complications and mortality.
Culture and neuroscience: How Japanese and European Canadians process social context in close and acquaintance relationships

ABSTRACT Recent cultural psychology findings suggest that social orientation affects neural social attention. Whereas independent cultures process people as separate from social context, interdependent cultures process people as dependent on social context. This research expands upon these findings, investigating what role culture plays in people’s neural processing of social context for two relationship contexts, close and acquaintance relationships. To investigate, we had European Canadian and Japanese participants rate the emotions of center faces in face lineups while collecting ERP data. Lineups were either congruent, with all faces showing similar emotions, or incongruent, with center face emotions differing from background faces. To investigate relationship types, we framed face lineups to be in close or acquaintance relationships. We found that for acquaintances, only Japanese processed incongruent social context as meaningful, as seen through N400 incongruity effects. Contrasting with these patterns, only European Canadians showed N400 incongruity effects for close relationships. These patterns were seen whether or not the two groups noticed the emotional conflict, as seen by N2 incongruity effects. Finally, we found that social orientation was differentially related to the neural incongruity effects for the two relationships. These findings further elucidate the nuances of how culture affects neural social attention.
A comparative study of intratracheal and aerosolization instillations of bleomycin inducing experimental lung fibrosis in rat

Abstract We aimed to investigate in the present work, using metabonomics approaches, the scalability of lung fibrosis-biomarkers, in bleomycin (BLM) model of pulmonary fibrosis in rats. Sixty male Wistar rats, weighing 250 ± 10 g, were randomly divided into three groups: a negative control group receiving normal saline treatment (G1), an intratracheal BLM instilled group (G2), and an aerosol BLM instilled group (G3). Rats were investigated at various times after BLM instillation. Metabolic changes observed in different biofluids have been integrated into the results of the histological examination (increase in inflammation, fibrosis score, and TGF-β immunostaining) which provide a novel pathway of biomarkers in pulmonary fibrosis. These two BLM-models showed an efficacy in the production of pulmonary fibrosis in rats, accompanied by an oxidative stress in lung tissue as assessed by the increase of lipid peroxidation and the depletion in the level of antioxidant enzymes such as superoxide dismutase and catalase. The aerosol model was more advantageous showing fibrotic foci occupying the majority of the lung in contrast to intratracheal instillation characterized by a non-homogeneous distribution of the fibroblastic foci.
Beyond Hot Spots: Biases in Antibody Somatic Hypermutation and Implications for Vaccine Design

The evolution of antibodies in an individual during an immune response by somatic hypermutation (SHM) is essential for the ability of the immune system to recognize and remove the diverse spectrum of antigens that may be encountered. These mutations are not produced at random; nucleotide motifs that result in increased or decreased rates of mutation were first reported in 1992. Newer models that estimate the propensity for mutation for every possible 5- or 7-nucleotide motif have emphasized the complexity of SHM targeting and suggested possible new hot spot motifs. Even with these fine-grained approaches, however, non-local context matters, and the mutations observed at a specific nucleotide motif varies between species and even by locus, gene segment, and position along the gene segment within a single species. An alternative method has been provided to further abstract away the molecular mechanisms underpinning SHM, prompted by evidence that certain stereotypical amino acid substitutions are favored at each position of a particular V gene. These “substitution profiles,” whether obtained from a single B cell lineage or an entire repertoire, offer a simplified approach to predict which substitutions will be well-tolerated and which will be disfavored, without the need to consider path-dependent effects from neighboring positions. However, this comes at the cost of merging the effects of two distinct biological processes, the generation of mutations, and the selection acting on those mutations. Since selection is contingent on the particular antigens an individual has been exposed to, this suggests that SHM may have evolved to prefer mutations that are most likely to be useful against pathogens that have co-evolved with us. Alternatively, the ability to select favorable mutations may be strongly limited by the biases of SHM targeting. In either scenario, the sequence space explored by SHM is significantly limited and this consequently has profound implications for the rational design of vaccine strategies.
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection

Machine learning-based solutions have been successfully employed for the automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen perturbations to the normal inputs. So far, the adversarial examples can only deceive detectors that rely on syntactic features (e.g., requested permissions, API calls, etc.), and the perturbations can only be implemented by simply modifying application’s manifest. While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective. In this paper, we introduce a new attacking method that generates adversarial examples of Android malware and evades being detected by the current models. To this end, we propose a method of applying optimal perturbations onto Android APK that can successfully deceive the machine learning detectors. We develop an automated tool to generate the adversarial examples without human intervention. In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning-based detectors that rely on semantic features such as control-flow-graph. The perturbations can also be implemented directly onto APK’s Dalvik bytecode rather than Android manifest to evade from recent detectors. We demonstrate our attack on two state-of-the-art Android malware detection schemes, MaMaDroid and Drebin. Our results show that the malware detection rates decreased from 96% to 0% in MaMaDroid, and from 97% to 0% in Drebin, with just a small number of codes to be inserted into the APK.